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Isobel Loutit (1909–2009)

Fri, 07/14/2017 - 5:04pm
by David Bellhouse, University of Western Ontario

Loutit

Last summer on a family holiday in Winnipeg, we had dinner with my father-in-law at his apartment building in the downtown area. Seated at our table in the dining room were four sisters. Since mealtime conversations at any dinner party I have ever attended rarely turn to the topic of statistics, I was surprised by the turn of events. For some reason that I cannot recall, one of the sisters, a diminutive and exuberant nonagenarian, stated that she was a statistician. After asking where she had worked and when, and where, when and with whom she had studied, I came to the conclusion that I was talking not only to the oldest surviving statistician in the province of Manitoba, but also to one of the first women, if not the first, to work professionally as a statistician in Canada. Her name is Isobel Loutit. I quickly made an appointment to interview her about her career. The nearly two hours I subsequently spent with her were both fascinating and informative.

Of Scottish ancestry (Loutit is an Orkney Scottish name), Isobel was born in Selkirk Manitoba in July of 1909. She studied mathematics with a minor in French at the University of Manitoba, graduating with a bachelor’s degree in 1929. This was before the time of one of Manitoba’s earliest statisticians, Cyril Goulden. Although Goulden had arrived in Manitoba in 1925 he did not teach until about two years after Isobel’s graduation. They never met; Goulden was working at the Dominion Rust Research Laboratory, which was located with the Agricultural College on the Fort Garry campus. At the time Isobel attended university, Arts and Science classes were held at a downtown campus, which is now the area occupied by Memorial Park opposite the Manitoba Legislature. Isobel’s major mathematics professors were Neil Bruce MacLean, Norman Wilson and Lloyd Warren. Originally an astronomer and applied mathematician by training, it was Warren who developed the actuarial science program at Manitoba. Wilson and Warren were authors of an undergraduate text that Isobel used in her first year at university. Warren also taught the statistics courses. When Isobel took her statistics course from Warren, the textbook for the course was by Gavett with Yule’s classic statistics book (probably the eighth edition) given, among three others, as an additional reference. The course syllabus contained topics in descriptive statistics, correlation, time series and curve fitting.

Isobel took some related courses. One course was on the theory of probability using the appropriate chapters (mainly permutations and combinations) from Hall and Knight’s classic mathematics book (probably the 25th printing of the fourth edition), and Coolidge as an additional reference, followed by applications to insurance using a standard life contingencies text. She also took courses in numerical analysis (called finite differences at the time) and least squares theory. Her classmates recognized Isobel as both a fun-loving and a very clever individual, traits that I also noticed when I talked to her.
In Isobel’s year, there were four women, including herself, who graduated in mathematics. Upon graduation there were only three avenues of employment open to them: teaching, nursing, and secretarial work. When I asked her if industrial jobs were open to women she replied, “Not really. We could try, but I don’t know anybody that got one.” Three of the women, including Isobel, went into teaching; the fourth took a secretarial course after graduation and worked as a secretary at Monarch Life in Winnipeg.

Given the restrictions on career choices, teaching was a natural route for Isobel to follow. Her father had been a school teacher and a principal, as well as a sales manager for Moir’s School Supplies. Even within the teaching profession there were restrictions; men were given the first preference for the subjects they wanted to teach. Trained primarily as a mathematician, Isobel taught French, her minor subject, instead. Occasionally, Isobel did get to teach some mathematics classes. Once when the regular mathematics teacher was off sick for a couple of weeks, Isobel took over his classes. When he came back to work many students continued to come to Isobel for help in mathematics. Isobel taught in the schools for about 10 years including a one-year stint in a country school and five years at Winnipegosis, finally ending up at a junior high in East Kildonan.

It was the World War II that changed the direction of Isobel Loutit’s career. In a very short period she went from being a school teacher to a quality control statistician at Northern Electric, now Northern Telecom, in Montreal. One day she saw a war casualty list on which the names of four of her former students appeared, and she decided to contribute directly to the war effort. At about the same time, to ease the labor shortage in the war effort, the government was advertising for women to take on jobs in industry that had been normally held by men. For the most part these were factory and clerical jobs. Isobel responded to an advertisement for women in the sciences, mathematics and physics in particular, to help engineers who were testing equipment and material for the war effort.

In January of 1942 Isobel joined the Inspection Board of the United Kingdom and Canada in Peterborough, Ontario. After two months she was posted as a government employee to Northern Electric in Montreal, which had a government contract to manufacture parts for the Vickers Anti-Aircraft Gun Predictor, an electrically run mechanical calculating device used to aim the artillery.

Isobel’s government job was to make sure that the calculating machines that were manufactured were actually carrying out the calculations correctly. As part of her training she was required to take the machines apart and reassemble them in working order. Because of Isobel’s mathematics and subsequent technical expertise, V.O. Marquez, then a Northern Electric manager and subsequently CEO, requested that the government release Isobel so that she could take a permanent position at Northern. It was not a straightforward transition. Government war workers normally were not allowed to change jobs. Further, the arrangements that were made for her were all verbal-nothing was in writing and she was required to be unemployed for one day before taking up her new job. She joined Northern Electric in January of 1943 and remained there until her retirement in 1972.

On arriving at Northern Electric there was an immediate problem. Isobel had come in on a government pay scale and there were two pay scales at Northern, one for men and another for women. Marquez could not give Isobel a raise since she was already at the highest salary level for women. Since her work was engineering-related, Marquez’s solution was to appoint her as an engineer, although she had no training or qualifications in that field. Northern Electric did not have a differential pay scale for engineers. She remained an “engineer” throughout her career at Northern Electric until she took on managerial responsibilities.

When the work with the Vickers Predictor ended, Isobel moved to the telephone division of the company. In 1947 she moved to the wire and cable division where she took charge of the statistical methods and quality control group in the division. She was in charge of data analysis and supervised a number of people, including the engineers, technicians and clerks who kept records related to product quality and who carried out regular quality control studies.

Her earliest computing environment was a Comptometer calculating machine. The statistical work was slow. The necessary calculations with the correct formulae took all day to get the required answer. Isobel kept abreast of new developments in equipment (for example, the move to computers with punch cards) and sampling inspection methodology. In 1966, although her job description and pay remained the same, she was formally given the title of Department Chief and a management job description. She was the first female in management at Northern Electric and, in Isobel’s words, it was a giant step for the company to take. Isobel was required to undergo a medical examination since the company was concerned about potential heart attacks among their managers. The medical examination turned out to be relatively useless since the only comparison group that her medical examiners had were male managers.

To remain current with new developments in her field Isobel took several professional development courses. In 1954 she took a two-week course on the design of experiments related to quality control. Held that summer at Queen’s University, the course was taught by Daniel DeLury of the University of Toronto. Later, she took a course from Western Electric in Allentown, Pennsylvania, and in 1961 she took a quality control management course run by General Electric at West Point in New York. The latter course was run in a case study and seminar format so that enrollment in this course was restricted to only 30 participants; Isobel was the only Canadian to attend and one of only two women in the course. The gender ratio in the course was probably an improvement over Isobel’s previous experiences. In 1955 she had attended a national quality control conference in New York. There were several hundred men in attendance and only a dozen women.

In the course of professional upgrading and conferences, Isobel met some of the giants of quality control, including in her early years in quality control W. Edwards Deming. She also met Walter Shewhart of Bell Labs and control chart fame. Her comment on Shewhart was, “He was a quiet guy-he lived in his charts.”

There was no incentive to publish scientific papers or articles. Consequently, Isobel never published any statistical or quality control work under her own name. She did, however, write a number of in-house technical reports on how to carry out statistical procedures so that employees could do their jobs better.

Isobel became very active in the American Society for Quality Control (ASQC) and the Montreal Section in particular, which was formed in 1950. Her most visible contributions to the ASQC were made in the 1960s. In 1961 Isobel was the program chair for the Quality Control All-Day Forum run by the Montreal Section. The forum had been held annually since 1957 as a one-day conference in quality control. Isobel invited her former boss V.O. Marquez, now promoted to vice president of Northern Electric, to give the address at lunch. At the forum Isobel launched a first for the Montreal Section and the ASQC. Her remarks at lunch as chair of the forum were given in French, the first official use of French by this professional society. There had been some squabbling in the section over the use of French at meetings and so Isobel took it upon herself, without any advance notice to others, to break the ice on the language barrier at the section.

The next year Isobel gave a talk on operator charting at one of the monthly meetings of the Montreal Section. She described the use of statistical quality control methods as it related to wire and cable production, and noted some of the difficulties that were encountered including homogeneity of lots, randomness of the samples and precision of measurements. Four years later in 1966 all the Canadian sections of the ASQC (Hamilton, Kitchener, London, Montreal, and Toronto) came together for the first Canadian Regional Conference of the ASQC. It was held in Toronto and Isobel was the program chair for the conference.

In 1969 she became chair of the Montreal Section of the ASQC, the first woman to hold this position. At the end of her term as Montreal Section chair she was invited to be the convener at a dinner for presidents of various societies held at McGill’s Faculty Club. She began her remarks with “Ladies and gentlemen …” When laughter immediately followed, she looked around and noticed she was the only woman at the dinner. Ironically, when in 2000 she was invited to attend the 50th anniversary celebrations of the Montreal Section of the ASQC and to provide some short remarks, her letter of invitation was addressed to M. (or Mr.) I. Loutit with a salutation of “Dear Sir.”

Isobel Loutit was a highly successful career statistician in an environment that was then almost exclusively a man’s world. It was a great pleasure to meet her and an enormous learning experience for me.

Reprinted with permission from the Statistical Society of Canada, Liaison, Volume 16.2 14-18 (May 2002).

Interview with Significance Editor Brian Tarran

Tue, 07/04/2017 - 7:00am
Significance magazine is written by statisticians for those with an interest in analysis and data. To find out how the magazine works and where story ideas originate, we asked the editor, Brian Tarran, the following questions.

Brian Tarran/Photo by Elyse Marks

What is the best part about being editor of Significance magazine?

The best part of any editor’s job is when the publication goes to print. There’s real satisfaction in putting together something for other people to read and enjoy. But as with any creative endeavor, that feeling only lasts about 24 hours. Then, you start to critique all the things you’ve done or wished you’d done differently, mulling them over and over until it suddenly dawns on you that you’ve got exactly eight weeks to get the next magazine together, so you’d better get a move on.

What’s unique about Significance, though, is the opportunity to work with a diverse array of expert contributors—and I really do think I learn a lot from each of them. I’m not a statistician. I came to the magazine as a journalist, and my job is to work with statisticians to help them craft interesting and compelling stories. Working through that process—whether with our contributors or our editorial board—is never less than fascinating.

I like to think I’ve helped our writers to better communicate their ideas, but I can say without hesitation that working with statisticians has made me a better editor and writer—and a more critical thinker, particularly where numbers are concerned.

June Highlights
The digital version of the June 2017 issue of Significance is available.

Read about how historians use a naval battle in World War I to explain how Bayesian thinking helps them reason with uncertainties of the past and how Lord Woolton, Britain’s minister of food during World War II, used statistics (and more) to prevent the nation from starving. Additionally, access an exclusive excerpt from the new book Errors, Blunders, and Lies: How to Tell the Difference by David S. Salsburg, author of The Lady Tasting Tea.

Also in this issue:

An economist argues Big Data may not be quite the game changer promised to those looking to predict stock market performance, especially with the ever-present danger of spurious correlations.

We learn why the “most popular” baby names might not be the most popular—which is something to keep in mind the next time baby-name rankings are published.

Data scientists apply machine learning to conference abstracts to speed up the event-planning process.

A biostatistician tracks the ups and downs of her own pregnancy weight gain.

Access the digital version of Significance through Members Only or download and read the magazine on the go with our iOS and Android apps.

If you are a print subscriber, your June issue will be arriving soon.

Where do story ideas originate?

Story ideas come from all manner of sources. Our editorial board is one key source. The members keep the magazine plugged in to what is happening in the statistical community and alert us to any interesting work that has been or is about to be published in journals or at conferences. We also keep an eye on what’s happening in the wider world so we can offer a statistical take, or a statistician’s perspective, on stories in the news. But many of the best ideas come directly from our contributors, and some of our most popular articles ever were submitted as part of our annual writing competition for early-career statisticians.

Do you have any special issues or articles planned that readers can look forward to?

We’ve tended to avoid special issues in recent years in favor of a broad selection of topics in each magazine. We also try not to plan too far ahead so we can keep the content topical and relevant to the concerns of the day. That said, we are planning features on official statistics, Big Data, deep learning, pollution, data visualization, and the history of statistics.

What is your vision for Significance, and how do you see the magazine evolving?

My vision remains true to the founding remit of Significance—that is, to create a magazine that introduces and explains statistical ideas and concepts to readers and to publish accessible, entertaining, and informative articles that showcase the contribution statistics makes to all walks of life.

The challenge facing us now, though, is to think about how we continue to deliver on that remit. The print magazine remains popular—so we’re not preparing for a digital-only future just yet—but we do need to take full advantage of digital media.

We relaunched our iOS and Android app last year and our website this year—both of which were redesigned to offer an improved mobile reading experience. But my long-term ambition is to start taking advantage of digital interactivity to bring our articles to life in a way that just isn’t possible on the printed page. So, we’re very much interested in working with statisticians who are excited about experimenting with different approaches to presenting content, both in print and online. As I said earlier, many of our best story ideas are to the credit of our contributors—and I expect that will be the case here, as well.

Do you have any tips you can offer someone interested in writing for Significance?

The starting point is always the synopsis. We’re looking for a good, strong outline of the story you want to tell and some explanation of why it’s important and why readers will want to read it. The story could be on any subject you like, provided you can make it accessible, relevant, and engaging to our audience.

When it comes to writing the article, contributors tend to have the most trouble with structure. People sometimes mistake Significance for a journal, and so submissions may be structured like a journal paper. But as a magazine, we’re looking for a different style of presentation and story-telling. My job is to help with those aspects, but there’s no real secret to writing in this way. If you enjoy reading magazines—including Significance—you’ll know already what makes for a good article.

What Does Larry Moulton Like to Do When He Is Not Being a Statistician?

Sat, 07/01/2017 - 7:00am
This column focuses on what statisticians do when they are not being statisticians. If you would like to share your pastime with readers, please email Megan Murphy, Amstat News managing editor.

Moulton after jumping from an airplane in Dubai in October of 2015. Photo by C Laszio

 

Larry Moulton

Who are you, and what is your statistics position?

My name is Larry Moulton. I am a professor in the department of international health (jointly appointed in the department of biostatistics) in the Johns Hopkins Bloomberg School of Public Health. I mainly design and analyze large, usually randomized, field trials around the world.

Tell us about what you like to do for fun when you are not being a statistician.

I have been a skydiver since I was 19, although there was a near-25-year break in jumping due to the demands of family and career. This is the kind of activity that requires a minimum amount of participation to remain ‘current.’ Currency will keep you from being an undue hazard to yourself or others, as well as help maintain your flying skills so you can frolic with your buddies in freefall.

My typical skydiving day is comprised of 3–5 jumps from 13,500 feet. Each jump starts by choreographing and practicing our planned aerial maneuvers on the ground—it looks a bit like synchronized swimming, as we join in one pattern, let go, and rejoin in another. Then, there is a 15-minute plane ride to the exit altitude, about a minute of freefall, a few minutes gliding about under canopy, and (usually) a soft, stand-up landing.

What drew you to this hobby, and what keeps you interested?

I suppose it was reading a lot of superhero comic books as a child—as an adult, I can dress up in a colorful costume and fly. Although we are all falling relative to each other, skydivers can fly down, up, sideways …

Although, now—especially after my long layoff—I am no longer on the cutting edge. It is still an awful lot of fun! One day of skydiving clears the mind as much as a week of traipsing about a forest. And, as with other sports, there are age-related goals to be attained. Last year, I was part of a 20-way formation that, while not large in itself, was big enough to gain us the Pennsylvania state record for number of skydivers over the age of 60 linked up in freefall (all members of SOS=Skydivers Over Sixty). As I get back into full gear, I am looking forward to national records (for old folks, anyway).

Now for the numbers. When I was a doctoral student, Kung-Yee Liang made the astute observation that my skydiving career could be modeled by a geometric distribution: number of successes until a failure! In the U.S., there is about one fatality for every 165,000 jumps. Among people making a tandem jump (attached to someone who has a twice-as-large parachute), the risk is about 1/500,000. My personal risk is perhaps 1/250,000—worse than a tandem jump, as I am with others in the air and opening the parachute somewhat lower, but not as risky as it is for skydiving students or experienced jumpers who use higher-performance parachutes in ways that give little margin for error. Still, even with the meager 60 or so jumps I make each year (some people will make 1,000 in a year), that is about a 1/4,000 chance skydiving this year will keep me from skydiving next year.

JSM 2018 Invited Session Proposals Sought

Sat, 07/01/2017 - 7:00am

Christian Léger

Christian Léger, JSM 2018 Program Chair

    As many of you are preparing to head to Baltimore for the 2017 Joint Statistical Meetings (JSM), it is already time to plan for JSM 2018, which will take place in Vancouver, British Columbia, Canada, from July 28 to August 2.

    If you attended JSM 2010, you know that—given the exceptional scenery—it will be a challenge to keep participants inside the Vancouver Convention Centre, so JSM Program Committee members are counting on you to help them prepare the very best program.

    Lisa LaVange, 2018 ASA president, has selected “#LeadWithStatistics” as the theme for JSM 2018. It promotes the idea that using statistics in the right way will improve any leaders’ chances of success! Its hashtag format is a nod to the social media age we live in and to the new generation of statisticians and data scientists who will become the future leaders of our field.

    At this time, the program committee is soliciting proposals for invited sessions to showcase some of the most innovative, impactful, and cutting-edge work in applied and theoretical statistics. The sessions can be oral presentations or panel discussions. Invited paper sessions consist of 2–6 speakers and discussants reporting new discoveries or advances in a common topic; invited panels include 3–6 panelists providing commentary, discussion, and engaging debate on a particular topic of contemporary interest.

    The ideal session involves fresh, important work that many JSM attendees will find interesting. Many of the most stimulating sessions present diverse viewpoints and strategies on a common topic or problem, with speakers coming from different institutions or practices.

    Many of you have probably never tried to organize an invited session, but why not try it this year? To organize a session, you should first set a theme of broad interest and identify and contact potential participants. Once these are arranged, you should write a proposal consisting of the title, a brief abstract/rationale, a list of participants, and tentative titles for the talks (titles can be changed later).

    In planning a meeting attracting more than 6,000 statisticians, the program committee has to abide by a number of rules. When planning an invited session, please note that JSM has strict guidelines for participation. Talk to potential speakers to ensure they are not committing to multiple invited proposals.

    There will be 181 invited sessions at JSM 2018. Most are allocated to the partner societies and ASA sections, which will select among the proposals submitted to each society or section. Note that most have a small number of invited sessions (1–4). After they make their selections, each ASA section will select up to two proposals to enter into a competition for the remaining spots on the invited program. It is therefore important to make your session proposal competitive with interesting topics and strong speakers, but also by providing a good description of the topic that committee members from other sections or organizations will appreciate in case the proposal goes into the competition.

    Session proposals must be submitted via the JSM online system, indicating type of session and proposed sponsor (partner society, ASA section, etc.). The online system will open July 18, and the deadline is September 6.

    As indicated earlier, you will understand that for a meeting of this size, it is essential to follow strict deadlines and procedures. The invited session proposal form allows the organizer to select up to three sponsors in ranked order. This is to ensure a worthy proposal is considered by other sponsors if it is not selected by its designated primary sponsor.

    Before submitting your proposal, you are encouraged to contact members of the program committee representing your chosen sponsors to discuss your proposal and see if they are willing to sponsor it given that most of the invited sessions will be selected by them. If you are a member of an ASA section or another sponsoring society, going through the corresponding representative is often a good way to proceed. But remember that only sessions submitted via the online system will be considered, so it is not enough to send your proposal by email to a member of the program committee. Decisions about the invited program will be made by the end of September. It is helpful to contact program committee members well ahead of the September 6 deadline.

    An invited poster session consisting of up to 30 electronic posters will take place during the Opening Mixer. Presenters in this session have access to a monitor, rather than a traditional poster board, that provides a unique opportunity to interact one-on-one with other researchers. Ideas for invited posters should be sent to Paul McNicholas of McMaster University, who is associate chair for invited and contributed posters, at paulmc@mcmaster.ca or jsm2018posterchair@gmail.com.

    I would also appreciate receiving good suggestions for two other important components of the JSM program: memorial sessions and introductory overview lectures (IOLs). A limited number of memorial sessions are planned at each JSM. Proposals can be submitted through the online invited session system (choose memorial session as sponsor). By doing so by the September 6 deadline, you can select other potential sponsors in case they would want to choose that session. In any case, I invite you to contact me if you are planning to submit a memorial session. Unless the session is selected by an organization or ASA section in September, decisions about memorial sessions will be made in the fall.

    IOLs are high-quality introductions to timely and important statistical topics of broad interest to JSM attendees and usually attract large audiences. I invite you to contact me with suggestions for topics or speakers for these sessions. Note that IOL speakers can also present an invited or contributed paper, panel, or poster. You can reach me at leger@dms.umontreal.ca.

    Thanks to the quality of the proposals sent by people like you, JSM offers a diverse, high-quality program that advances the knowledge of each participant. If you want to #LeadWithStatistics, Vancouver will be the place to be July 28 to August 2, 2018.

    On behalf of all program committee members, I thank you in advance for helping us make JSM 2018 an event as fantastic as its location!

    Karen Kafadar Elected ASA President

    Sat, 07/01/2017 - 7:00am
    Katherine Monti to Be Vice President Jill Talley, Public Relations Manager

     

    Karen Kafadar

    Karen Kafadar, chair and commonwealth professor in the department of statistics at the University of Virginia (UVA), has been elected the ASA’s 114th. She will serve a one-year term as president-elect beginning January 1, 2018; her term as president becomes effective January 1, 2019.

    The ASA membership also elected Katherine Monti, formerly chief statistical scientist at Rho, as ASA vice president. Monti’s term also begins January 1, 2019.

    “Complex problems—such as detecting emerging epidemics, ensuring food safety, protecting communications and other infrastructure networks, and establishing overall reliable standards—cannot be solved by single individuals. Statisticians are critical components of teams that address problems in academia, industry, and government, yet, all too often, their involvement arises by serendipity,” said Kafadar. “I’m eager to serve as ASA president in 2019 and look forward to engaging ASA members and the broader statistical community to expand efforts that will forecast areas of change, inspire the next generation of statistical thinkers, and generate diverse opportunities for professionals to grow collectively and in their specialty.”

    In her current role, Kafadar’s research interests focus on robust methods; exploratory data analysis; characterization of uncertainty in the physical, chemical, biological, and engineering sciences; and methodology for the analysis of screening trials, which includes awards from the U.S. Centers for Disease Control and Prevention, the ASA, and the American Society for Quality.

    Prior to her faculty appointment at UVA, Kafadar was a mathematical statistician at the National Institute of Standards and Technology; member of the technical staff at Hewlett Packard’s RF/Microwave R&D Department; fellow in the Division of Cancer Prevention at the National Cancer Institute; professor and Chancellor’s Scholar at the University of Colorado-Denver, and Rudy Professor of Statistics at Indiana University.

    Kafadar’s professional accomplishments and activities are vast. She currently serves as the biology and genetics editor for The Annals for Applied Statistics and previously was editor of the Journal of the American Statistical Association’s Review Section and Technometrics.

    Additionally, she chairs the ASA’s Committee on Statistics in Forensic Science, serves on the Forensic Science Standards Board, and is active on National Academy of Sciences committees. She is a fellow of the ASA, American Association for the Advancement of Science, and International Statistical Institute; has authored more than 100 journal articles and book chapters; and has advised numerous graduate students.

    She earned her PhD in statistics at Princeton University and both her master’s degree in statistics and bachelor’s degree in mathematics from Stanford University.

    Katherine Monti

    Katherine Monti was highly regarded throughout her career, which spanned academia, industry, medical devices, and pharmaceuticals. For 18 years, she worked at Rho, Inc., a contract research organization providing clinical research throughout the drug development process. Prior to that, she served as associate director at Astra Pharmaceuticals, senior statistician at Ciba Corning Diagnostics, senior statistician at Ralston Purina, and assistant professor in the department of mathematical sciences at the University of Missouri-St. Louis.

    Some of Monti’s major statistical activities have been in the field of clinical trials, applications to veterinary medicine and food science, and design of experiments. Additionally, she has worked on two legal cases, one regarding salary discrimination and another regarding false advertising. In mentoring colleagues, she taught by case-study example, emphasizing both statistics and situation management, which left a lasting impression on coworkers and students who came to appreciate the unique stories she shared.

    Monti is an ASA fellow and served as the Council of Chapters Representative to the ASA Board and as a board representative on the Council of Chapters Governing Board. She has held several committee and section appointments, including on the Committee on Fellows, Advisory Committee on Continuing Education, Biopharmaceutical Section, and Committee on Law and Justice Statistics. Her involvement with the ASA Boston Chapter spanned several officer positions, and she was also a member of the North Carolina Chapter.

    The ASA membership also elected the following:

      Katherine Halvorsen, professor of mathematics and statistics, Smith College, as the Council of Sections Representative to the ASA Board

      Don Jang, vice president and director, Center for Excellence in Survey Research, NORC at the University of Chicago, as the Council of Chapters Representative to the ASA Board

      Scott Evans, senior research scientist, Center for Biostatistics in AIDS Research, Department of Biostatistics, Harvard University, as the publications representative to the ASA Board

      Natalie Rotelli, consultant computational statistician, Eli Lilly and Company, as chair-elect of the Council of Sections Governing Board

      Isaac Nuamah, director, clinical biostatistics, Janssen R&D, as chair-elect of the Council of Chapters Governing Board

    Following is a list of the entire slate of election results, including officers for each of the ASA’s 27 sections:

    Board of Directors

    President-elect
    Karen Kafadar

    Vice President
    Katherine Monti

    COSGB Representative to the Board
    Katherine Halvorsen

    COCGB Representative to the Board
    Don Jang

    Publications Representative to the Board
    Scott Evans

    Council of Chapters Governing Board
    Chair-elect, 2018
    Isaac Nuamah

    Vice Chair, Region 1, District 1
    Lynn Sleeper

    Vice Chair, Region 1, District 2
    David Fardo

    Council of Sections Governing Board
    Chair-elect
    Natalie Rotelli

    Vice Chair
    Phil Scinto

    Section on Bayesian Statistical Science

    Chair-elect
    Susan Paddock

    Program Chair-elect
    Robert Gramacy

    Publications Officer
    Xinyi Xu

    Biometrics Section

    Chair-elect
    Sheng Luo

    Council of Sections Representative
    Dipankar Bandyopadhyay

    Biopharmaceutical Section

    Chair-elect
    Richard Zink

    Program Chair-elect
    Margaret Gamalo-Siebers

    Secretary
    Janelle Charles

    Council of Sections Representative
    Brian Millen

    Business and Economic Statistics Section

    Chair-elect
    Peter Zadrozny

    Program Chair-elect
    Marina Gindelsky

    Section on Statistical Computing

    Chair-elect
    Wendy Martinez

    Program Chair-elect
    Usha Govindarajulu

    Secretary/Treasurer
    Jared Murray

    Council of Sections Representative
    David van Dyk

    Section on Statistical Consulting

    Chair-elect
    LeAnna Stork

    Secretary/Treasurer
    Mekibib Altaye

    Council of Sections Representative
    Hrishikesh Chakraborty

    Executive Committee at Large
    Jason Machan

    Section on Statistical Education

    Chair-elect
    Mine Çetinkaya-Rundel

    Council of Sections Representative
    Matthew Hayat

    Executive Committee at Large
    Sharon Lane-Getaz
    Cassandra Pattanayak

    Section on Statistics and the Environment

    Chair-elect
    Christopher Wikle

    Program Chair-elect
    Alexandra Schmidt

    Treasurer
    Maria Terres

    Publications Officer
    K. Sham Bhat

    Council of Sections Representative
    Wendy Meiring

    Section on Statistics in Epidemiology

    Chair-elect
    Kathleen Jablonski

    Program Chair-elect
    Veronica Berrocal

    Publications Officer
    Colin Fogarty

    Council of Sections Representative
    Nandita Mitra

    Section on Government Statistics

    Chair-elect
    Elizabeth Mannshardt

    Program Chair-elect
    Jeffrey Gonzalez

    Section on Statistical Graphics

    Chair-elect
    Dianne Cook

    Program Chair-elect
    Ed Mulrow

    Publications Officer
    Joyce Robbins

    Health Policy Statistics Section

    Chair-elect
    Ruth Etzioni

    Section on Statistics in Marketing

    Chair-elect
    Victoria Gamerman

    Program Chair-elect
    Tim Trudell

    Treasurer
    Hiya Banerjee

    Section on Physical and Engineering Sciences

    Chair-elect
    Bryan Smucker

    Program Chair-elect
    Brad Evans

    Secretary/Treasurer
    Jennifer Kensler

    Section on Quality and Productivity

    Chair-elect
    Brian Weaver

    Program Chair-elect
    Shan Ba

    Section on Risk Analysis

    Chair-elect
    Susan Simmons

    Program Chair-elect
    Aric LaBarr

    Secretary/Treasurer
    Christopher Sroka

    Publications Officer
    Maria Barouti

    Council of Sections Representative
    Edsel Pen

    Social Statistics Section

    Chair-elect
    Trudi Renwick

    Program Chair-elect
    Stephanie Ewert

    Secretary/Treasurer
    Stephanie Eckman

    Section on Statistics in Sports

    Chair-elect
    Luke Bornn

    Program Chair-elect
    Andrew Swift

    Council of Sections Representative
    Stephanie Kovalchik

    Survey Research Methods Section

    Chair-elect
    Kennon Copeland

    Program Chair-elect
    Asaph Young Chun

    Secretary
    Safaa Amer

    Council of Sections Representative
    Jamie Ridenhour

    Section on Teaching Statistics in the Health Sciences

    Chair-elect
    Amy Nowacki

    Section on Nonparametric Statistics

    Chair-elect
    Dimitris Politis

    Program Chair-elect
    Bing Li

    Treasurer
    Limin Peng

    Publications Officer
    Po-Ling Loh

    Section on Statistics in Defense and National Security

    Chair-elect
    Jane Pinelis

    Program Chair-elect
    Erin Hodgess

    Section for Statistical Programmers and Analysts

    Chair-elect
    Jonathan Lisic

    Program Chair-elect
    William Coar

    Secretary
    Marianne Miller

    Treasurer
    Amy Gillespie

    Publications Officer
    Tasneem Zaihra

    Section on Statistical Learning and Data Science

    Chair-elect
    Tian Zheng

    Program Chair-elect
    Ali Shojaie

    Section on Statistics in Imaging

    Chair-elect
    Hernando Ombao

    Program Chair-elect
    Tingting Zhang

    Council of Sections Representative
    Amanda Mejia

    Section on Mental Health Statistics

    Chair-elect
    Booil Jo

    Program Chair-elect
    Ramzi Nahhas

    Section on Medical Devices and Diagnostics

    Chair-elect
    Zhen Zhang

    Program Chair-elect
    Martin Ho

    Section on Statistics in Genomics and Genetics

    Chair-elect
    Dan Nettleton

    Program Chair-elect
    Hongkai Ji

    Council of Sections Representative
    Pei Weng

    Chapter Volunteers Speak to AP Stats Students

    Sat, 07/01/2017 - 7:00am
    Chris Barker

      The San Francisco Bay Area Chapter has organized an initiative encouraging chapter volunteers to give a lecture on careers in statistics to AP Statistics students at local high schools for the past eight consecutive years.

      This year was unprecedented because of the number of volunteers (seven) and number of high schools (two) where lectures were given.

      The volunteer speakers for 2017 included the following:

      • Sundar Dorai-Raj
      • Mike Crager
      • Debbie McCullough
      • Seth Michaelson
      • Mei Cheng
      • Nacer Abrouk
      • Chris Barker

      Pictures of each speaker and AP Statistics teacher are available on Google photos.

      Opportunities Await Applied Statisticians at JSM

      Sat, 07/01/2017 - 7:00am
      This column is written for statisticians with master’s degrees and highlights areas of employment that will benefit statisticians at the master’s level. Comments and suggestions should be sent to Megan Murphy, Amstat News managing editor, at megan@amstat.org.

      Contributing Editor
      Sameera Wijayawardana holds a PhD in biostatistics from Emory University. For six years, he has worked as a statistician at Eli Lilly and Company, supporting the development of targeted cancer therapeutics and companion diagnostics. He has also been active with the ASA and other international statistical professional associations.

       

      As a recently appointed member of the ASA Committee on Applied Statisticians (CAS), I’m delighted to have this opportunity to talk to you about this year’s Joint Statistical Meetings. JSM, as the largest gathering of statisticians held in North America, offers a unique opportunity for statisticians in academia, industry, and government to exchange ideas and explore opportunities for collaboration. This year, JSM will take place in Baltimore, Maryland, from July 29 to August 3. The theme is “Statistics: It’s Essential,” meant to emphasize the fundamental importance of statistics to all aspects of scientific and societal endeavors, and even to seemingly mundane daily life. It is certainly a timely theme when we consider how much the ‘news’ we are exposed to seems to rely on misrepresentations of quantitative evidence that would not stand up to even a rudimentary application of statistical reasoning.

      This year’s JSM program, as is normally the case, consists of many technical sessions on a variety of topics. When you add in all the roundtable discussions, business meetings, professional development courses and workshops, award ceremonies, and other social events on the program, sifting through it all can seem daunting. The best place to start this process is with the JSM online program, where you can use the Advanced Search option to search by day, event type, and event sponsor.

      For example, being a member of the biopharmaceutical section and of CAS, I tend to look for sessions sponsored by those two groups first. In my experience, a good way to whittle the program to a manageable size is to figure out the top three events you want to attend each day. Once you have budgeted time for these top events, you can start filling in the gaps in your schedule with a mix of technical sessions and other meetings and activities. I also use the My Program option to add events I want to attend to a customized list that I can download as a .csv file.

      When you’re searching through the program, note that a ‘*’ preceding a session name means the session is designated as an “applied” session. A ‘!’ preceding a session name means the session reflects this year’s meeting theme.

      In addition to technical sessions, there are a number of professional development offerings worth considering. These are mostly additional-fee events, but they offer tremendous value if you are interested in staying up to date in a technical area or want to learn something new to add to your repertoire.

      The introductory overview lectures are good to catch if you want to learn about a new area, albeit at a basic level. I am particularly interested in going to the “Computer Age Statistical Inference” lecture that will be given by professors Brad Efron and Trevor Hastie on Monday.

      The various awards and recognition ceremonies sprinkled throughout the meeting offer an excellent opportunity to mingle with distinguished members of our profession and to hear their thoughts about current opportunities and challenges. Of note is ASA President Barry Nussbaum’s talk, “Statistics: Essential Now More Than Ever (Or, Why Uber Should Be in the Driver’s Seat for Cars, Not for Data Analysis)” during the ASA President’s Address and Founders & Fellows Recognition Tuesday night.

      The A.M. and P.M. roundtables are a great way to share ideas with people working on similar areas or issues of interest. And the JSM Opening Mixer on Sunday night and Dance Party (a fun highlight of JSM) on Tuesday night are excellent venues for unwinding and mingling with fellow attendees.

      JSM also provides numerous ways to contribute to our profession by volunteering for sections, chapters, and ASA initiatives. You can learn more about these by attending the relevant section/chapter meetings in the evenings. For example, if you want to learn more about CAS, you are welcome to come to the friends of CAS social mixer Tuesday from 3–4 p.m. in the Hilton – Poe B.

      With the wealth of technical sessions, networking opportunities, and social events offered this year, I think we are going to have a great JSM in Baltimore. I’m eager to get there in July, and I look forward to seeing all of you there as well!

      Opportunities for Applied Statisticians at JSM

      CC=Baltimore Convention Center
      H=Hilton Baltimore Hotel (401 West Pratt Street)

      Note: View the online program for up-to-date times and locations.

      Data Challenge 2017 Contestants Set to Present at JSM

      Sat, 07/01/2017 - 7:00am

      Data Challenge 2017 contestants will highlight their analyses in a Joint Statistical Meetings speed poster session Monday, July 31, from 8:30 a.m. to 10:20 a.m. Each contestant will give a five-minute talk and then stand by their poster in the exhibit hall to answer questions and discuss their work with JSM attendees.

      The Statistical Computing, Government Statistics, and Statistical Graphics sections sponsored Data Challenge 2017. The contest was open to anyone, including college students and professionals from the private or public sector. It challenged participants to analyze the Consumer Expenditure Survey from the Bureau of Labor Statistics using statistical and visualization tools and methods.

      Winners of the challenge will be announced at the general membership meetings/mixers of the sponsoring sections. There will be two awards categories—professional (one level) and student (three levels).

      Data Challenge 2016

      Five contestants who participated in Data Challenge 2016 submitted papers for a special issue of Computational Statistics, which will be guest edited by Roya Amjadi of the Federal Highway Administration and Wendy Martinez of the Bureau of Labor Statistics.

      Additionally, Amjadi—who continues to work with the 2016 contestants—facilitated a sponsorship from the Evaluation of Low Cost Safety Improvements Pooled Fund Study for Data Challenge 2016 winner Jonathan Auerbach to present his paper, “A Hierarchical Model to Evaluate Policies for Reducing Vehicle Speed in Major American Cities” to 40 state member representatives in June. The paper offers a new statistical methodology for highway safety evaluations and a fresh perspective on pedestrian safety improvement evaluation.

      San Antonio Chapter Recognizes Excellence at Science and Engineering Fair

      Sat, 07/01/2017 - 7:00am

      The San Antonio Chapter sent judges to the 2017 Alamo Regional Science and Engineering Fair, held at St. Mary’s University on February 24, to help select those senior division projects that showed excellence in the use of statistical methods.

      The San Antonio Chapter presented the following awards at the ceremony:
       

      First Place and $125
      Sandra Moon, Lady Bird Johnson High School
      “A Genetic Polymorphism in the PCSK9 Gene Associated with a Rapid HIV Disease Progression Among European- and Hispanic-Americans”

       

      Second Place and $75
      Adithya Mummidi, Keystone Upper High School
      “Gene Expression Profiles of Subcutaneous Adipose Tissue, Skeletal Muscle Tissue, and White Blood Cells in Pre-Diabetic and Normoglycemic Mexican-American Individuals”

       

      Third Place and $50
      Isuru Somawardana, Keystone Upper High School
      “Utilizing Cardiac and Pulmonary Function to Power a Pacemaker”

       

      Judges, from left: (front row) Vincent Spadafore, Howard Monroe, and Jared Schettler (back row): John Schoolfield, Michael Mader, Danny Sharon, Steve Zinkgraf, and Jesus Cuellar-Fuentes

      Statisticians Highlight Scientific Research on Capitol Hill

      Sat, 07/01/2017 - 7:00am
      Amy Nussbaum, ASA Science Policy Fellow

        Mark and Stacey Culp on Capitol Hill
        (Photo by Amy Nussbaum)

          On May 16, the ASA sponsored two exhibitors as part of the 23rd annual Coalition for National Science Funding (CNSF) Congressional Exhibition, intended to celebrate National Science Foundation grant recipients and their most recent research.

          Mark and Stacey Culp, professors in the statistics department at West Virginia University, traveled to Washington, DC, to present work from Mark’s NSF CAREER Grant on Machine Learning Solutions to Big Data Problems in Biometrics and Drug Discovery. Likening Big Data problems to “searching for a needle in a haystack while someone’s dumping more hay on top of you,” Mark explained his research to congressional staff, NSF personnel, and members of other scientific organizations during the official reception in the Rayburn House Office Building and described how the methodologies can be applied in multiple arenas.

          Mark Culp discusses his research with NSF Director France Córdova.

          Mark Culp discusses his research with Rep. Jerry McNerney (D-CA), a PhD mathematician.

          Mark and Stacey Culp with ASA Science Policy Fellow Amy Nussbaum (Photo by Steve Pierson)

          Earlier in the day, the Culps met with staff in the offices of Senators Joe Manchin and Shelley Moore Capito—as well as representatives David McKinley, Alex Mooney, and Evan Jenkins—to urge robust NSF funding for FY18 and discuss support for science in West Virginia. The West Virginia delegation to Washington is especially influential on NSF funding, with both senators and Rep. Jenkins serving on the appropriations subcommittee that determines the NSF’s budget.

          “We’re really grateful to Mark and Stacey for spending the day in Washington,” said Steve Pierson, ASA director of science policy. “Members of Congress and their staff continually hear from their constituents on a variety of issues and it’s imperative to the health of agencies like NSF and NIH that such constituents include supporters of federal research funding.”

          CAREER grants also require teaching commitments, so Mark was able to discuss both his research and equipping students with the latest tools of the trade so they can find jobs upon graduating and connect with industry. Many staffers were delighted to see such a return on investment coming from the NSF research dollars.

          Stacey also discussed her research with statistics and health care applications and how such work can improve the quality of life for West Virginians—another top priority for their legislators.

          Mark stated, “It was an honor to represent the ASA at the CNSF exhibition and to share some Big Data applications of my NSF-funded research on Capitol Hill. I was able to discuss my research with a steady stream of engaged attendees and other presenters.” Stacey added, “I appreciated the warm reception by the staffers and was impressed by their understanding of the significance of the NSF and the need to continue to support it.”

          The Coalition for National Science Funding is an alliance of more than 100 organizations “united by a concern for the future vitality of the national science, mathematics, and engineering enterprise.” In addition to the annual exhibition, CNSF also organizes sign-on letters, hosts monthly stakeholder meetings, and targets key congressional appropriators in support of increasing national investment in the National Science Foundation’s research and educational programs. Other member organizations include both scientific associations and universities.

          This is the seventh time the ASA has participated in the event. Previous representatives include Richard Smith of the Statistical and Applied Mathematical Sciences Institute, Genevera Allen of Rice University and the Neurological Research Institute at the Baylor College of Medicine, and Peter Craigmile of The Ohio State University.

          Stats from the Road

          Sat, 07/01/2017 - 7:00am
          Amanda Malloy, ASA Director of Development

            Amanda Malloy

            “Before we go upstairs, would you like a vegan energy shot with cayenne pepper?” In more than 12 years of fundraising, I had never been asked this question. I declined the energy shot, but gratefully accepted the artistic cappuccino prepared by an expert barista. This particular visit was filled with firsts as we discussed possible ways the company I was visiting could support ASA programs.

            I was drawn to fundraising work because, like the visit I just described, it is filled with new experiences and incredible people. I started as the director of development in August of 2014, and in that time, I’ve met one on one with more than 50 ASA members and many more in groups at chapter meetings, luncheons, receptions, and other events. I’ve been invited into homes, places of work, and to favorite coffee shops and restaurants. I cherish each and every visit and truly love getting to know so many members. Through these relationships, I’ve learned so much more about the broad impact the statistics profession has on everyday life. It certainly validates for me why the ASA’s work, focused on the following four key areas, is so important.

            EDUCATION

            Opening Minds Through Statistics | Giving every student from kindergarten through graduate school the opportunity to learn and become excited about careers in statistics

            PROFESSIONAL DEVELOPMENT AND MENTORING

            Growing at Every Career Level | Helping statisticians at every career level continue to grow, learn, and be successful

            SCIENCE POLICY AND ADVOCACY

            Raising the Profile of the Profession | Ensuring the proper use of statistics and that statisticians are involved in important policy decisions

            STATISTICAL LITERACY AND OUTREACH

            Changing the Stereotype | Making access to good statistics more accessible to the public and raising awareness for the many careers in statistics and data science

            It’s been gratifying to not only see the impact of these contributions at work, but also to tell members about what the ASA does that they may not know about. It is a great time to be the ASA’s director of development because of the hard work and volunteering of ASA members. Your willingness to help and contribute allows the association to do great things.

            A big thank-you to all who have let me come visit and learn about what is important to you in your personal and professional lives. It has been a pleasure to get to know you!

            If you are interested in chatting about giving opportunities, please reach out to me at amanda@amstat.org.

            Count the Ways … to JSM

            Thu, 06/01/2017 - 7:00am
            Christopher Moriarity and Kevin Ward Drummey

              This year’s Joint Statistical Meetings (JSM) will take place at the Baltimore Convention Center, which is convenient to public transportation. The Maryland Transit Administration (MTA) Light Rail line has a Convention Center stop just west of the convention center. Additionally, the Maryland Area Regional Commuter (MARC) Baltimore/Camden Station is a short distance away; this is the northern terminus of the MARC Camden Line from Washington, DC.

              The Baltimore-Washington International (BWI) Airport is the closest airport to this year’s JSM. The MTA Light Rail has a station at BWI Airport on the lower level, Concourse E vicinity. The one-way fare from BWI Airport to the Baltimore Convention Center is $1.70, making this the least expensive travel option from BWI Airport to JSM. The MTA Light Rail runs at 20–30 minute intervals from early morning to late evening, with a somewhat reduced schedule on Sunday (~11 a.m. to 8:30 p.m. northbound from BWI Airport). The airport station fare machines are located inside the airport terminal adjacent to the station.

              In the Know

              More information about public transportation in the northern Virginia; Washington, DC; and Baltimore areas can be found at the following websites:

              Another public transit option from BWI Airport is train travel from the BWI Amtrak Station. One must take a free shuttle from the airport to reach the Amtrak station. The MARC Penn Line and Amtrak run from the BWI Airport station to Baltimore’s Penn Station. Penn Station is not within convenient walking distance of the convention center; however, an MTA Light Rail spur line runs at ~30 minute intervals from Penn Station southbound down to the convention center area.

              The next-closest airport to JSM is Reagan National Airport. One can travel from the airport to Union Station via the DC subway system (Metro), and then catch either an Amtrak or MARC train to Baltimore.

              The Metro requires use of a SmarTrip card. Each passenger must have their own SmarTrip card, which costs $2. Some of the fare machines at the Metro stations have an option for buying a card and adding fare to it in one transaction.

              International travelers may need to travel to JSM via Dulles Airport. The trip from this airport to JSM is much longer, with limited public transit options. Metro doesn’t yet go all the way to Dulles Airport, but there are two bus options to get from Dulles Airport to the Metro. Metrobus 5A runs from Dulles Airport into downtown DC, terminating at the L’Enfant Plaza station. Fairfax Connector bus routes 981 and 983 run from Dulles Airport to the Wiehle-Reston East subway station. One can then travel to Union Station via the Metro.

              Baltimore has a single subway line and a bus network, both run by MTA. The bus network is undergoing a transformation in June to “BaltimoreLink.” A $4 day pass allows unlimited travel for one day on the MTA Light Rail, subway, and buses.

              Getting Around Baltimore for Free: The Charm City Circulator

              A free public transit option for visiting some areas in Baltimore is the Charm City Circulator. There are four routes, three of which pass near the convention center. The Orange Route, an east-west route, passes directly in front of the convention center on Pratt Street and runs east to transfer points for the Green Route, the one route that does not pass near the convention center. The Purple Route is a north-south route, going to the Federal Hill area of Baltimore to the south, and it has a stop at Penn Station to the north. The Banner Route runs to the southeast to historic Fort McHenry, site of the War of 1812 battle that inspired the creation of the U.S. national anthem by Francis Scott Key. Purple Route and Banner Route stops are a short distance east of the convention center. Page 36 of the 2017 JSM Conference Registration Guide has a map that shows where the nearby Purple Route and Banner Route stops are.

              Touring Baltimore Harbor: The Baltimore Water Taxi

              A picturesque way to visit areas of Baltimore Harbor is traveling on the Baltimore Water Taxi. The nearest stop to the convention center is Harborplace, several blocks southeast. A single-trip one-way fare is $8. An all-day pass is $14. All fares are paid by credit card after boarding the taxi. The all-day pass includes a coupon book for discounts at locations near taxi stops and is required if one wants to use the water taxi to travel to/from Fort McHenry.

              Be sure to allow adequate time for water transport and transfers between taxi routes, particularly if you plan to go Fort McHenry. The last taxi from the Fells Point taxi stop to Fort McHenry leaves at 3:30 p.m. The last taxi from Harborplace that can get you to Fells Point in time for the 3:30 departure leaves Harborplace at 3:00 p.m.

              ‘Florence’: A Statistics Song

              Thu, 06/01/2017 - 7:00am
              Lyric ©2017 Lawrence Mark Lesser

                To the tune of Julie Gold’s Grammy-winning song “From a Distance,” a No. 2 hit for Bette Midler during the first Gulf War.

                This new lyric honors the approaching bicentennial of the birth of Florence Nightingale, adapting her quote: “To understand God’s thoughts, we must study statistics, for these are the measure of His purpose.”

                With statistics,
                many soldiers were saved
                in the Crimean War.
                With statistics,
                Florence Nightingale
                found what made the death rate soar.
                With statistics, Florence graphed the data
                in innovative ways:
                A rose diagram, circular histogram,
                a polar area display.

                With statistics,
                sanitation was found
                to have caused those extra deaths.
                With statistics,
                Florence led reform
                to implement what was best.
                With statistics, she founded modern nursing
                with brilliance and compassion:
                She gave herself to the cause of health,
                she took bold action.

                God is teaching us, God is teaching us,
                God is teaching us through statistics.

                With statistics,
                England and India
                were healthier places to live.
                Oh, statistics
                shone like the lamp
                Florence brought from bed to bed.
                With statistics, she set an example
                of vision and of strength:
                More than pie charts, her mind and heart
                would light and lead the way.

                What Does Mary Sammel Do When She Is Not Being a Statistician?

                Thu, 06/01/2017 - 7:00am
                This column focuses on what statisticians do when they are not being statisticians. If you would like to share your pastime with readers, please email Megan Murphy, Amstat News managing editor.

                Sammel

                Who are you, and what is your statistics position?

                My name is Mary Dupuis Sammel, and I am a professor of biostatistics at the University of Pennsylvania’s Perelman School of Medicine in Philadelphia.

                Tell us about what you like to do for fun when you are not being a statistician.

                I am a volunteer puppy raiser for The Seeing Eye, an organization that trains guide dogs for the visually impaired. This is an activity for the whole family—my husband, daughter, and son. We foster a puppy from 8–10 weeks of age until he/she is 15 months old. We do basic house training and expose the puppies to as many situations in our world as we can. We are raising our fourth puppy, a female German Sheppard named Blossom. Blossom is almost 14 months old, so she will be leaving us soon to go off to her next adventure.

                What drew you to this hobby, and what keeps you interested?

                As my children grew older, I missed having a little one to take care of, so now I have a puppy baby. I love getting a new dog and enjoy teaching them to walk on a leash. And let’s face it, who can resist a cute puppy?

                Mary Dupuis Sammel raises puppies–like Everett, above–for The Seeing Eye, an organization that trains guide dogs for the visually impaired.

                We belong to a local puppy raiser’s club, which meets once a month. We share training tips and take the puppies on outings to places like the movies, bowling alleys, baseball games, and even wine tasting. I’ve made some new friends. We puppy sit for one another and get the dogs together to play. Having a dog helps me break away from my computer, get outside, and move. Also, as an introvert, having a puppy in public takes the attention off me and gives me something to talk about.

                What really inspires me is that, after the dog has returned to The Seeing Eye and completed its training, the raiser is invited to see the puppy do a demonstration called a Town Walk. We don’t get to interact with the dog, but we see how they guide through the city and across busy streets. It is amazing! After the demo, we get to chat with our puppy’s trainer, who has grown to love our dog, too.

                Once the dogs are matched with a person and they train together, we receive a postcard telling us a little about our dog’s new owner. It is fulfilling, yet bittersweet. I am so proud of our two dogs, currently working as guides. Our third puppy, Fawn, is a breeder. We hope to raise one of Fawn’s puppies in the future and, when she retires, become her owners.

                Master’s Programs in Data Science and Analytics (Continued …)

                Thu, 06/01/2017 - 7:00am
                Steve Pierson, ASA Director of Science Policy More universities are starting master’s programs in data science and analytics, of which statistics is foundational, due to the wide interest from students and employers. Amstat News reached out to those in the statistical community who are involved in such programs. Given their interdisciplinary nature, we identified programs involving faculty with expertise in different disciplines to jointly reply to our questions. In our April issue, we profiled four universities; here are several more.
                  NC State University

                  Michael Rappa is a professor of computer science and the founding director of the Institute for Advanced Analytics at North Carolina State University. As head of the institute, he is the originator and principal architect of the nation’s first Master of Science in Analytics, established in 2007.


                  The Master of Science in Analytics (MSA) is a novel curriculum aimed squarely at producing graduates with the multi-faceted skills needed to draw insights from complex data sets and communicate those insights effectively. It is the product of a three-year collaboration by an interdisciplinary group, including mathematicians, computer scientists, statisticians, economists, geographers, operations researchers, and faculty with expertise in various fields of business and management.

                  Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.

                  The MSA is a single, fully-integrated course of study—not a menu of core and elective courses—taught exclusively to students in the program. It is highly interactive. Students work in teams and receive personalized coaching to improve their productivity. It is an intensive 10-month learning experience designed to immerse students in the acquisition of practical knowledge and application of methods and techniques. The curriculum is carefully calibrated and continuously updated to meet the evolving challenges facing data scientists. The institute houses classrooms, team rooms, study spaces, and other amenities under one roof, as well as the faculty and staff, who are available to interact with students throughout the day.

                  Master of Science in Analytics

                  Year in which the first students graduated/are expected to graduate: 2008
                  Number of students currently enrolled: 120
                  Partnering departments: Institute for Advanced Analytics
                  Program format: In-person, 30 credit hours, full-time, practicum team project

                  MSA students hone their skills working on challenging problems with actual data shared from sponsoring organizations. The practicum spans eight months and culminates with an executive-level report and presentation to the sponsor. Students work with leading industry-standard programming tools. Since the program’s inception, MSA students have engaged in 134 projects with more than 100 sponsors spanning virtually every industry segment and including some of the world’s leading organizations and best-known brands.

                  With a decade of experience and hundreds of graduates, the curriculum has a proven track record of producing superior student outcomes.

                  What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?

                  The importance of being able to store and quickly manipulate large amounts of electronic data has a history that spans several decades. However, by the 1990s, the rapid emergence of the web presented us with huge amounts of real-time streaming data. The supply of university graduates with the skills needed to manage, analyze, and draw insights from the new data reality wasn’t keeping up with the demand. We saw an opportunity to offer a new graduate degree focused on producing data-savvy students who could meet the evolving needs of employers.

                  There has been an overwhelming response to the MSA program. Already, in just a decade, it has become one of NC State’s largest graduate degree programs in terms of degrees conferred annually. It’s also the university’s most selective degree program, with more than 1,000 applicants each year and an acceptance rate in the low teens. It’s gratifying to see the quality of students attracted to the program and their passion for data.

                  The program gets high marks from students. They enjoy the team-driven learning experience and hand-on approach to working with data in their practicum projects. They can see the close connection between what they are learning and what they will be doing on the job as an analytics professional. It greatly enhances their employability.

                  What types of jobs are you preparing your graduates for?

                  In the institute’s employment report, you will see each job title for the positions our students enter upon graduation. The positions can be bucketed into three large categories: analysts, consultants, and data scientists. The first two categories come with a variety of adjectives (e.g., risk analyst or integration consultant). The data scientist position is a relatively recent development. It has come on strong in the last five years.

                  The institute’s annual employment report also provides information about the distribution of employment by industry sector (financial services, software/internet, and consulting are the big three). Typically, MSA graduates land in any one of a dozen industry sectors. There are also government placements, including the armed services, and perhaps one or two graduates who will head into a position within a university.

                  What advice do you have for students considering a data science/analytics degree?

                  The ultimate litmus test is your passion for working with data. People will tell you about the great job opportunities and the flashy headlines. Sorry to say it, but there’s really nothing sexy about working with data. It’s hard work. It’s tedious. There are times it will make your head hurt. For every great insight, there are a hundred frustrating dead ends. But if you’re really good and love the work, the potent insight now and then makes it all worth it. This could be said for just about any college major, if you take it seriously and set your sights on performing at the highest level.

                  Describe the employer demand for your graduates/students.

                  The institute has a decade of experience and has placed 651 students in the profession—with an unparalleled track record of 90–100% placement by graduation year after year since our inception. We collect comprehensive data on every placement, and indeed every job offer, and keep track of our graduates as they progress in their careers. The current median starting salary is $100,000 for graduates with prior work experience and $90,000 for graduates without prior work experience.

                  You don’t hear employers lamenting anymore about the shortage of talent. What they fret about is the scarcity of high-quality, well-prepared graduates. The institute is as successful as it is because we have the highest standards for admission and a unique learning format proven to produce high-quality results. Our students are the kind of graduates employers look to recruit.

                  Do you have any advice for institutions considering the establishment of such a degree?

                  Unless you have unlimited resources, work together. Put creative energy into the kinds of organizational innovations that will facilitate collaboration across unit boundaries. Universities have their own histories and cultures that define how the academic disciplines fit within departments and colleges and give the institution a set of possibilities and constraints uniquely its own. No single approach to establishing a degree will fit every university. Rely on the members of your faculty who are by their nature boundary-spanners. Enable them to do what they do best.

                  The Institute for Advanced Analytics is, by design, a university-wide collaboration. The institute brings together faculty in fields such as mathematics, statistics, computer science, operations research, and business disciplines to work together to develop, refine, and deliver the Master of Science in Analytics. The result is, by every measure, a resounding success for us.



                  Penn State University

                  Colin J. Neill is an associate professor of software and systems engineering and director of engineering programs. He is the author of more than 80 articles about the development and evolution of complex software and systems and the management and governance thereof. As director of engineering programs, Neill oversees the division’s portfolio of graduate degree programs, including the MPS in data analytics delivered both in residence and online.

                  John I. McCool is a distinguished professor of systems engineering. He has taught courses in statistics, experiment design, reliability, statistical process control, applied data mining, probability models, and optimization. His research includes statistical inference for the Weibull distribution and industrial statistics.


                  How do you view the relationship between statistics and data science?

                  Statistics provides the foundational concepts of random sampling, the central limit theorem, common probability distributions, hypothesis testing, and predictive modeling. These concepts undergird the intelligent application of computer intensive data mining tools of the data scientist such as neural networks, decision trees, cluster analysis, and association modeling.

                  MPS in Data Analytics

                  Year in which the first students graduated/are expected to graduate: 2017
                  Number of students currently enrolled: 200
                  Partnering departments: School of Graduate Professional Studies (Lead); Smeal College of Business; Applied Statistics; Harold & Inge Marcus Department of Industrial and Manufacturing Engineering

                  That said, one can certainly ponder the role of statistics in the age of Big Data. Statistics tells us how to infer from samples of the population, so what does that mean when we potentially don’t need to sample the population, given the computation and data storage we have at our disposal?

                  Please describe the basic elements of your data science curriculum and how it was developed.

                  It was developed in collaboration with faculty from engineering, business, statistics, information technology, and software engineering/computer science. Separately, we all recognized the value of an interdisciplinary program that covered the techniques, technologies, theory, and application of data science and analytics and sought to create a program that simultaneously spanned that broad expanse, yet dealt with each aspect in depth.

                  The core of the program covers the central statistics of analytics, as well as the computational statistics and machine learning used in predictive and prescriptive analytics. The program we created has options that allow students to focus on a specific area within data analytics—technologies used in development of such systems, data storage and processing at scale, prescriptive analytics techniques, business analytics focused on applying analytics for strategic advantage, marketing analytics (coming soon), and hopefully other areas as more academic partners come on board.

                  What was your primary motivation(s) for developing a master’s data science program? What’s been the reaction from students so far?

                  As we said above, we all recognized the value of a program that focused on addressing the data deluge seen in almost every area of the private and public sectors. The student reaction has been phenomenal. Applications have been very strong, and that allows us to maintain high admissions standards so the entire student body is accomplished and driven. This allows for a rich classroom environment—whether virtual in our online program or literal in our face-to-face program. Our students seem particularly energized by the opportunity to engage in faculty research in our Big Data lab, a research group that also functions both physically and virtually.

                  What types of jobs are you preparing your graduates for?

                  Since we have multiple options within the program, we are preparing students for a broad array of professional roles, but I personally find the job titles out there aren’t well defined, so I hesitate in using them too categorically. I certainly believe our program can prepare graduates for roles as data scientists, data architects, and data analysts, depending on the option pursued and the electives selected.

                  What advice do you have for students considering a data science degree?

                  My main advice would be do it! Every job outlook report indicates it is everything from the sexiest job of the 21st century to the most highly sought after for the next decade and beyond. One can certainly get related skills in a computer science or statistics degree, but data science combines them, adds to them, and puts them into context, and that is valuable in the marketplace.

                  Describe the employer demand for your graduates/students.

                  The demand for data science graduates is incredible. In just more than a year of offering the degree, we have had direct requests for graduates and interns from employers in transportation, logistics, health care, automotive, entertainment, and finance.

                  Do you have any advice for institutions considering the establishment of such a degree?

                  Well, we aren’t seeking competition, but if I were to offer advice, I would say find academic partners in the various aspects of data science—statistics, machine learning, data processing and storage, information retrieval—as well as the various domains that are employing analytics so you have domain knowledge, too. Of course, with so many partners, the forming of consensus gets harder, but as the saying goes, the hardest steel is forged in the hottest fire.



                  University of Vermont

                  James P. Bagrow is an assistant professor of mathematics and statistics at the University of Vermont and a member of the Vermont Complex Systems Center. He has degrees in liberal arts (AS) and physics (BS, MS, and PhD).


                  Jeffrey S. Buzas is professor and chair of mathematics and statistics and director of the statistics program at the University of Vermont. He has degrees in mathematics (BS) and statistics (MS and PhD).


                  Peter Sheridan Dodds is a professor in mathematics and statistics at the University of Vermont, where he is also the director of the Vermont Complex Systems Center and co-director of the Computational Story Lab.


                  Margaret J. Eppstein is professor and chair of computer science at the University of Vermont and founding director of the Vermont Complex Systems Center. She has a BS in zoology, MS in computer science, and PhD in environmental engineering.


                  Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.

                  Our program provides students with a transdisciplinary education that prepares them for business environments or a PhD in an analytic field. Our program is more scientific than professional and is unique in its combination of complex systems and data science (CSDS). Throughout the MS in CSDS program, students are challenged to create defensible arguments for their findings, with warnings against the many potential pitfalls associated with exploring large-scale data sets, coupled with the use of computational process-based models that lend insight into emergent properties of complex systems. Admissions requirements include courses in calculus, programming, data structures, linear algebra, and probability and statistics. We offer opportunities for students to make up missing prerequisites.

                  Master in Complex Systems and Data Science

                  Year in which the first students graduated/are expected to graduate: 2016
                  Number of students currently enrolled: Five
                  Partnering departments: Department of Mathematics and Statistics, Department of Computer Science
                  Program format: In-person instruction, 30 credits (coursework only, project, and thesis options). Support for finding internships, no graduate teaching assistantships, research assistantships possible for students working with externally funded advisers.

                  What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?

                  Almost all scientific fields have moved from data scarce to data rich, and sophisticated analyses have been made possible by the advent of distributed computing and storage, with accompanying advances in algorithms and theory. As Big Data has become a common thread across disparate disciplines, so too have methods for contending with the many difficulties presented by large-scale data analysis. The program was created to address the opportunities created by these conditions. Faculty were already working on modeling and analysis of complex systems using transdisciplinary approaches, and we already had a five-course Certificate of Graduate Study in Complex Systems, so it was natural to build an MS degree upon this foundation.

                  Quote from a student course evaluation: “This class changed the way I see the world.”

                  How do you view the relationship between statistics and data science/analytics?

                  Data science is at the intersection of statistics and computer science. Data munging, machine learning, visualization, text processing, heterogeneous data types, and web scraping are examples of tasks not typically addressed in traditional statistics programs. Inferential logic/methodologies and design of experiments are not typically taught in computer science programs. A large number of schools have business-themed data science programs. The core data science part of our MS in CSDS at UVM provides more general purpose training, though certainly a career in the business world would be a possible outcome for students.

                  What types of jobs are you preparing your graduates for?

                  We have developed close relationships with several companies, and they are helping to support our programs. The program is new and we don’t yet have data to address demand for our graduates, but it is worth noting that data scientists are increasingly in demand across the spectrum of occupations in government, finance, corporations, and journalism. The job title of data scientist is now commonplace. Popularized by Nate Silver and Moneyball, training in data science is being sought after across the United States. Perhaps the clearest evidence is the growth of data science degrees globally. Also, these degrees, which are largely master’s level, are easily being filled by applicants within the United States.

                  What advice do you have for students considering a data science/analytics degree?

                  We aim to serve students coming from a wide variety of backgrounds and therefore deliberately keep the prerequisites to a minimum. Students must have a bachelor’s degree in a relevant field and prior coursework in computer programming, data structures, calculus, linear algebra, probability, and statistics.

                  Our program is ideal for students interested in the intersection of statistics, computer science, and mathematics with applications in any of a wide variety of domains. The degree provides more exposure to computing and statistics than the traditional statistics or computer science degrees (respectively), offers unique transdisciplinary courses in complex systems and data science, does not require strictly disciplinary courses (e.g., we do not require computer science–specific courses like operating systems), and provides a great deal of flexibility in customizing coursework to student interests.

                  Do you have any advice for institutions considering the establishment of such a degree?

                  The statistics, computer science, and mathematics programs at UVM have a collegial relationship, which has helped significantly in the formation of our BS in data science and our MS in complex systems and data science degrees. We work closely on course scheduling so the courses in the different disciplines do not conflict. Cross-listing of courses also provides for increased options for students. Collaboration is also made easier in that the participating disciplines all reside in the College of Engineering and Mathematical Sciences.



                  University of Wisconsin-Madison

                  Mark Craven is a professor in the department of biostatistics and medical informatics at the University of Wisconsin-Madison. His research involves developing machine-learning methods to infer network models of interactions among genes, proteins, environmental factors, and phenotypes of interest.


                  How do you view the relationship between statistics and data science/analytics?

                  Data science is the combined use of tools and concepts from statistics/biostatistics and computer science/biomedical informatics for gathering, integrating, analyzing, interpreting, and visualizing data for scientific inquiry and decision-making. In addition to those two core disciplines, data science incorporates case studies, methods, theory, and principles from other fields, including systems engineering, human-centered design, and information sciences. Biomedical data science is focused on the quantitative and computational aspects of generating and using data to further biomedical research, broadly construed.

                  Master of Biomedical Data Science

                  Year in which the first students graduated/are expected to graduate: 2017
                  Number of students currently enrolled: Six
                  Partnering departments: None

                  Please describe the basic elements of your data science/analytics curriculum and how it was developed.

                  Our program in biomedical data science includes areas such as machine learning and data mining, optimization, database methods, image analysis, formal study design methods for biomedical research, and formal statistical principles for quantifying uncertainty and making inferences.

                  Each student must take a core sequence comprising one course in each of biostatistics, bioinformatics, medical image analysis, and clinical informatics. They also each develop an area of concentration with two additional courses. Examples might include, among others, clinical biostatistics, more advanced bioinformatics or computational biology, or clinical informatics. Students also take a research ethics course and may engage in a capstone research project.

                  What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?

                  Recent growth in the size and complexity of data arising in biology, biomedical research, and public health policy—including applications in high-throughput biology, medical image analysis, clinical and health services research, and genetics and genomics—requires continued research and training in the separate disciplines of statistics and computer science, and, as importantly, their synthesis.

                  Nationwide, the biomedical research community is struggling to manage, share, analyze, and fully exploit expanding quantities of data in the biomedical sciences. The need for a workforce capable of innovating, implementing, and using methods from biomedical data science is widely recognized. This demand has been driven by the following factors:

                  • The proliferation of high-throughput biological experimental methodologies (e.g., next-generation sequencing, microarrays, SNP arrays) has transformed biology into a data-intensive science.
                  • Increasingly, biomedical studies and clinical decision-making are integrating and making inferences with varied types of data (genotypes, molecular profiles, images, electronic health records, and population-based data), which heightens the need for sophisticated computational methods.
                  • Incentives, such as those specified by the Health Information Technology for Economic and Clinical Health (HITECH) Act, are accelerating the adoption and broadening functionality of electronic health records and health care billing records, including application in comparative effectiveness research.

                  The NIH has clearly identified biomedical data science as an area of priority for increased training for clinical and translational research to proceed at a pace that takes advantage of the tremendous output of scientific and clinical data. The Data and Informatics Working Group of the NIH director’s advisory committee made a specific recommendation to “build capacity by training the workforce in the relevant quantitative sciences such as bioinformatics, biomathematics, biostatistics, and clinical informatics.” Following this report, the NIH formally recognized the need to expand the quantitative sciences workforce and methodology through its Big Data to Knowledge (BD2K) initiative (https://commonfund.nih.gov/bd2k/index), which has called for innovative new research and training programs focused on the management and analysis of biomedical data. Thus, there is a pressing need and a keen interest among translational researchers for such training.

                  What types of jobs are you preparing your graduates for?

                  This is a new program, and we are eagerly anticipating our first graduates. The jobs for which they are preparing are quite varied. Some of our students joined the program with medical or other advanced clinical degrees. They are gaining methodological skills and experience that will complement their clinical training and facilitate their work as medical researchers. Other students—typically coming in with a bachelor’s degree—will be looking for positions in industry in an array of fields including biotechnology, direct-to-consumer genetics, electronic health records development, and medical instruments.

                  What advice do you have for students considering a data science/analytics degree?

                  The best advice for students interested in biomedical data science is to develop a basic foundation in mathematics (optimally, at least two semesters of calculus, plus linear algebra) and computer sciences (two semesters), and to develop an interest and some coursework in biology or biomedical investigation.

                  Upon graduation, students should continue to emphasize all three contributing scientific areas in their professional development, including (bio)statistics and computer sciences. Our students need to be prepared to quickly deploy skills in computer science and data analytics. In addition, a basic foundation in an area of biology or biomedical science is exceptionally valuable. For this type of degree, students need to be a “triple threat,” instead of simply focusing their efforts in one area.

                  Describe the employer demand for your graduates/students.

                  Employment opportunities for data scientists are growing rapidly and include numerous and growing opportunities in the health care industry. In a January 2016 article in the Denver Post, Shawn Wang, vice president of data science for Anthem Insurance’s health care analytics department, was quoted as saying, “Data science has been mature for the last couple years in retail, e-commerce, and fintech (financial technology). They’re really strong. We have to leverage those. Our preference is to find people within the health care space, but we know there is a limited supply. It’s not easy.”

                  This is true in Wisconsin, as well. UW computer sciences professor Jignesh Patel stated in the Milwaukee Journal Sentinel, “Wisconsin has potential in the big data arena, particularly in the arenas of health care IT where Madison has deep expertise …”

                  Data-driven job search websites and resources bear out this trend. For example:

                  The Jobs Rated Report 2016 list of the top 200 jobs at Careercast.com lists data scientist at #1 and statistician at #2. Glassdoor’s list of the 25 best jobs in America also places data scientist at #1.

                  The Indeed.com job trend chart for data scientist indicates that data scientist jobs as a fraction of all listings increased approximately eight-fold between August, 2012, and August 2016.

                  Do you have any advice for institutions considering the establishment of such a degree?

                  There are many units on campus with interests and initiatives in data science. We have been successful by focusing on the biological and biomedical application area. I would advise any institution considering this area to build on existing partnerships between statistics, biostatistics, computer sciences, and biomedical informatics. No one unit can or should “own” this area, so proceeding in a broad and inclusive way makes the most sense.



                  South Dakota State and Dakota State Universities

                  Thomas Brandenburger has more than 20 years of leadership, academic, and consulting experience in both the private and public sectors. He is a retired U.S. Naval Officer and a former information technology consultant at Perot Systems. Currently, he is an associate professor of statistics at SDSU teaching predictive analytics courses.

                  Jun Liu is an assistant professor of information systems at Dakota State University. He has been serving as the coordinator of the Master of Science in Analytics program since 2014. Liu earned a PhD in MIS from the University of Arizona. His research is in enterprise data management, business intelligence, large-scale networks, and Big Data analytics.

                  Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.

                  Development Principles. Both universities leveraged their industry ties and sought expert opinions on both content and delivery of material. SDSU has a formal industry advisory board with regional business executives. DSU solicited input regarding our program from global leaders in data science and analytics such as IBM, SAS, and Cloudera. Both universities use industry analytics and data tools. SDSU uses SAS- and R-based platforms for coursework. DSU also uses the SAS Academic initiative free access. Additionally, IBM has collaborated with DSU and allowed no-charge access to software (such as BigInsights and Cognos) and participation in the IBM Academic Skills Cloud pilot program.

                  A common concern was expressed in reviewing other programs nationally. These programs seemed to either be too theoretical or too managerial for what we were hoping to achieve. It was thought that both programs had the following common set of three goals for the program:

                  • Relevancy to the practitioner
                  • A continuum of skills
                  • High standards of student output

                  Leveraging Strengths. The joint program takes advantage of faculty expertise at both universities. DSU’s faculty in information systems has expertise and experience on the IT side of data science and teaches courses such as system development, databases and data warehousing, machine learning and predictive modeling, and Big Data. DSU’s professors in health informatics and information assurance provide expertise in highly applicable areas including health data analytics, forensic statistics, and fraud detection. SDSU’s faculty focuses on the statistics/mathematics side of data science and has deep expertise across the full spectrum of analytics and applied statistics and mathematics practices pertinent to this program.

                  Master of Science in Data Science (MSDS)/Master of Science in Analytics (MSA)

                  Websites: South Dakota State and Dakota State universities
                  Year in which the first students graduated/are expected to graduate: 2015
                  Number of students currently enrolled: 40 and 70, respectively, at SDSU and DSU
                  Partnering departments: South Dakota State University Mathematics and Statistics (Brookings, SD) and Dakota State University College of Business and Information Systems (Madison, SD). The two programs share six common core courses that are jointly offered.
                  Program format: Thirty credit hours. Six core courses (18 credits) are offered jointly with Dakota State University with three courses from SDSU in predictive analytics and modeling and three courses from DSU in Big Data and information systems technologies.

                  Prerequisite Knowledge Requirements. To level set entrance into the programs, students in both programs are expected to have taken courses or have work experience in programming principles, database design and programming (including familiarity with SQL), and statistical principles before they enter the program. If a student does not meet these requirements, he/she is required to take additional prerequisite courses to cover any gaps.

                  What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?

                  The primary goal of both programs was to fill demand that was being expressed by industry partners and to fulfill the strategic missions of the universities to South Dakota. SDSU offers the MSDS, an MS in mathematics, MS in statistics, and PhD in computational science and statistics. In addition to the MS in analytics, DSU offers an MS in information systems, MS in health informatics, MS in information assurance, MS in applied computer science, DSc in IS, and DSc in cybersecurity.

                  How do you view the relationship between statistics and data science/analytics?

                  Data science is a much broader field, encompassing everything related to data, from data cleansing, data manipulation, data storage (including databases and data warehousing), and data analysis (including machine learning, statistics, text mining, social network analysis, etc.) to Big Data analytics. Traditionally, statistics focuses on inference, including testing hypotheses and deriving estimates, while analytics focuses on using machine learning to extract insights from data and to make predictions. Nowadays, we are often dealing with Big Data in different formats that require the use of a very different technology stack than used previously with traditional statistical analysis. These two fields are rapidly merging. We require our students to have a solid background in statistics and also keep up with emerging technologies such as Big Data analytics and large-scale machine learning.

                  What types of jobs are you preparing your graduates for?

                  South Dakota has a strong banking industry, especially in consumer lending, with many major credit card and student loan companies. Many SDSU graduates have taken jobs in this field in situations that require the prediction of customer behavior, whether that be credit risk, marketing, portfolio management, or forecasting and optimization of customer contact. These all require the analysis and modeling of large transaction-based data sets. Others have gone on to work for the health care industry. The application of analytics in health care has grown dramatically in a short period. Still others have gone into areas as diverse as large consulting firms, manufacturing, large agriculture firms, consumer retail companies, and private weather forecasting. Many of DSU’s graduates are working as data scientists/analysts in the health care domain in South Dakota and other midwestern states. DSU also has graduates working as data scientists or software engineers with analytics focus in financial institutions. The jobs the graduates take are representative of the continuum of skills both universities are teaching across the full domain lifecycle of data.

                  What advice do you have for students considering a data science/analytics degree?

                  Data science/analytics students should be familiar with the whole process, from data collection, data cleansing, exploratory analysis, and data transformation to data storage and data analysis. Data science/analytics students should be good programmers who can use languages such as R and Python to do data processing and data mining. It is recommended that any aspiring data scientist learn statistics with a heavy focus on statistical programming using real-world examples. A focus should be put on establishing both breadth and depth of skill. Not a jack of all trades approach, but rather a Swiss Army knife approach. Be good at several things.

                  Describe the employer demand for your graduates/students.

                  Demand for graduates is high. The feedback from employers who have hired our students is positive because of the practical and hands-on nature of our program.

                  Do you have any advice for institutions considering the establishment of such a degree?

                  It’s harder than it looks. Traditional university hierarchies do not reward for non-research-based activities. Often, this is an obstacle at research-based universities and so sometimes smaller or private universities are more likely to be able to establish these programs. Ensure the leadership of your university is fully on board and establish the lines of funding at the outset. Find a couple key external stakeholder companies that have a vested interest in making it happen.

                  Additionally, it is difficult to recruit professors with strong analytics and data science backgrounds because they are in high demand.



                  Harvard University

                  Rafael Irizarry is the director of the health data science master’s program. He has worked on the analysis and signal processing of microarray, next-generation sequencing, and genomic data. Recently, he began developing diagnostic tools and discovering biomarkers. He also develops open-source software, and is one of the leaders and founders of the Bioconductor Project.

                  Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.

                  The new master’s degree program in health data science provides students with the rigorous quantitative training and essential computing skills needed to manage and analyze health science data to address important questions in public health, medicine, and basic biology. The program trains students to extract knowledge from data and to communicate this knowledge across disciplines.

                  SM in Health Data Science

                  Year in which the first students graduated/are expected to graduate: 2019
                  Number of students currently enrolled: Expected matriculation for our first class in fall of 2017 is 16 students
                  Partnering departments: Biostatistics Department
                  Program format: 60-credit SM, including hands-on, semester-long, project-based research course (7.5 credits); traditional/full-time program format

                  The first year consists of case-based training in statistical inference, machine learning, and programming, as well as training in public health and biomedical sciences. Through this case-based approach, students simultaneously learn computing skills necessary to manage and analyze data and start gaining experience in answering scientific questions with data. Although these skills are generally applicable, we focus on applications related to public health and the biomedical sciences.

                  These skills are further developed during an intensive semester-long course during the third semester that focuses on project-based work. This culminating research experience allows students to integrate the knowledge and skill they have attained to answer real-world questions. Program faculty define the projects assigned in this course.

                  A total of 60 credits of coursework is required for the MS in health data science.

                  This includes a 30-credit ordinally graded core curriculum consisting of the following courses:

                  • BST 222 Basics of Statistical Inference (Fall, 5 credits)
                  • BST 260 Introduction to Data Science (Fall, 5 credits)
                  • BST 261 Data Science II (Spring, 2.5 credits)
                  • BST 263 Applied Machine Learning (Spring, 5 credits)
                  • BST 262 Computing for Big Data (Fall, 2.5 credits)
                  • HDS 325 Health Data Science Practice (Fall, 7.5 credits)
                  • EPI 201 Introduction to Epidemiology Methods I (Fall, 2.5 credits)

                  Students are also required to take five credits of coursework in computer science. In addition to the computer science courses, a minimum of 22.5 additional credits come from a list of elective courses offered by the departments of biostatistics, biomedical informatics, computer science, statistics, and epidemiology.

                  All candidates for admission to master’s programs must have the following:

                  • An undergraduate degree in mathematical sciences or allied fields
                  • Practical knowledge of computer scripting and programming, as well as experience with a statistical computing package such as R or Python
                  • Calculus through multivariable integration
                  • Excellent written and spoken English

                  What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?

                  The main gap we aim to address relates to bringing the subject matter question to the forefront and treating the statistical techniques and computing as tools that help answer the question. Through answering these questions, students learn to connect subject matter to statistical frameworks. We also cover computing and programming in much more depth, teaching R, Python, and techniques for handling data sets that do not fit in memory. Our program also has a stronger focus on machine learning techniques and computing than the traditional statistics master’s.

                  The program is designed to be an essential bridge between developing a solid understanding of statistical issues and building the computing and programming skills to implement best practices in applied health science research.

                  How do you view the relationship between statistics and data science/analytics?

                  Statistical inference and methodology are integral to a data scientist’s toolbox. However, the demand for data science education is surging and traditional courses offered by statistics and biostatistics departments are not meeting all the needs of those seeking this training. Some programs have been adapting by having computing play a more prominent role. While we agree that increasing the training of computing skills is necessary, our main motivation for creating this program was the necessity to bring applications to the forefront.

                  Although traditional statistical programs are housed in departments with faculty performing research that falls exactly into what students interested in data science want, educational programs don’t always teach what we do. Our program looks to change this, and we will prepare students to create, connect, and compute with data to answer real-world questions from the public health and biomedical fields.

                  What types of jobs are you preparing your graduates for?

                  The SM in health data science is designed to be a terminal professional degree, giving students essential skills that are in demand in a growing data-driven industry. The program also provides a strong foundation for students interested in continuing in a PhD program in biostatistics or other quantitative or computational science with an emphasis on data science.

                  What advice do you have for students considering a data science/analytics degree?

                  For those seeking hands-on training that builds skills to apply to real-world problems, a data science/analytics degree offers the rigorous quantitative training and essential computing skills needed to manage and analyze data to do this. The data science/analytics degree is different from a computer science or statistics degree in that it focuses on solving real-world problems with data, rather than on learning theory and methods and using data only as an example.

                  Describe the employer demand for your graduates/students.

                  We anticipate strong demand for graduates from this master’s program in health data science. First data will be available after the first cohort graduates in 2019.

                  Do you have any advice for institutions considering the establishment of such a degree?

                  Our main recommendation is that those who develop data science courses should not only have rigorous statistical and computing training, but also experience analyzing data with the main objective of solving real-world problems.

                  Highlights of the April ASA Board of Directors Meeting

                  Thu, 06/01/2017 - 7:00am

                  ASA President Barry Nussbaum receives a birthday cake during the April Board of Directors meeting.

                  ASA President Barry Nussbaum convened the first ASA Board meeting of 2017 at the ASA offices in Alexandria, Virginia. The highlights of the meeting follow.

                  Discussion Items
                  • ASA Executive Director Ron Wasserstein and Nancy Kidd, executive director of the American Sociological Society, led the board in a discussion about the ASA’s role as an advocate for the profession during a time of political strife. The board considered various scenarios and asked whether the ASA would get involved as an advocate in those situations. The board also considered the development of a rubric that would help ASA decision-makers determine whether an issue warranted ASA involvement.
                  • An enlightening discussion with Andreas Georgiou, former president of the Hellenic Statistical Authority (that is, Greece’s top official statistician), took place. Georgiou has faced multiple criminal and civil charges in Greece for presenting the national statistics, particularly those indicating the relative size of the national debt, in accordance with EU law. The situation has reverberations for official statistics not only in Greece, but around the world.
                  Action Items

                  The following editorial appointments were made:

                    • Steve Rigdon, St. Louis University, Journal of Quantitative Analysis in Sports, 2018–2020
                    • Dan Jeske, University of California, Riverside, The American Statistician, 2018–2020
                    • Extended the terms of co-editors of the Journal of Educational and Behavioral Statistics, Dan McCaffrey and Li Cai, through 2018
                  • Journal prices were reviewed, and an increase of 5% on institutional North-American and international print and online prices for 2018 was approved. A 2% increase on ASA member rates for print, the first increase in these rates in three years, was approved. Online access for ASA members is free.
                  • A feasibility study was authorized to consider how the ASA might launch a campaign to increase public awareness of the importance, reliability, and trustworthiness of government statistics.
                  • Sites for JSM 2024 were considered. A final decision will be made by the ASA Executive Committee in the next few months.
                  Reported Items
                  • Associate Executive Director and Director of Operations Steve Porzio updated the board on ASA finances for 2016. The year ended well in the black for several reasons, including a strong attendance at JSM. Also, Porzio and ASA Treasurer Amarjot Kaur presented the results of the ASA’s annual audit. The board thanked Porzio and staff for another clear audit.
                  • The board received progress reports on the three strategic initiatives launched by ASA President Barry Nussbaum. All are well under way. In addition, ASA President-elect Lisa LaVange outlined initiatives for 2018 based on the ASA’s Strategic Plan and the efforts of her predecessors.
                  • The Council of Chapters Governing Board (COCGB) and Council of Sections Governing Board (COSGB) reported on their recent activities. The COCGB highlighted its work to increase involvement by chapter members and improve cross-chapter communications. The COSGB has been active in providing additional support for interest groups and budget planning and guidance for sections.
                  • Trevor Butterworth, director of Sense About Science (SAS) USA, updated the board on STATS, the ASA’s partnership with SAS USA to improve statistical literacy. STATS has been active in providing workshops to train journalists in statistics.
                  • ASA Director of Education Rebecca Nichols and K–12 Statistical Ambassador Chris Franklin briefed the board on a wide variety of educational leadership activities engaged in by ASA members.
                  • The annual report of the Professional Issues and Visibility Council was presented by Vice President Kathy Ensor. Likewise, Vice President David Williamson presented the annual report of the Membership Council. These council reports help the board stay connected with ASA committees and vice versa.
                  • Amanda Malloy, director of development, provided a brief update on the ASA’s fundraising activities.
                  • Steve Pierson, director of science policy, updated the board on our advocacy work.

                  The full board meets again July 28–29 in Baltimore, immediately prior to JSM 2017.

                  2017 Board of Directors

                  Barry Nussbaum, President

                  Lisa LaVange, President-Elect

                  Jessica Utts, Past-President

                  Rob Santos, 3rd-Year Vice President

                  Kathy Ensor, 2nd-Year Vice President

                  David Williamson, 1st-Year Vice President

                  Wendy Lou, 3rd-Year Council of Chapters Representative

                  Paula Roberson, 2nd-Year Council of Chapters Representative

                  Julia Sharp, 1st-Year Council of Chapters Representative

                  Anna Nevius, 3rd-Year Council of Sections Representative

                  Eileen King, 2nd-Year Council of Sections Representative

                  Jim Lepkowski, 1st-Year Council of Sections Representative

                  Cynthia Bocci, International Representative

                  David van Dyk, Publications Representative

                  Amarjot Kaur, Treasurer

                  Ron Wasserstein, Executive Director and Board Secretary

                  ASA Shares Excitement of Statistics at NCTM Meeting

                  Thu, 06/01/2017 - 7:00am

                  April was an important month for the ASA’s outreach effort to mathematics, statistics, and science teachers.

                  ASA Director of Education Rebecca Nichols traveled to San Antonio, Texas, for the National Council of Teachers of Mathematics (NCTM) annual conference to represent the ASA at its exhibition booth. Members of the ASA/NCTM Joint Committee presented at NCTM and also assisted with the booth.

                  NCTM is the largest mathematics education organization. Their 2017 annual meeting was attended by 7,000 K–12 math and statistics teachers, teacher educators, and university faculty. The ASA has been exhibiting at the NCTM conference for about 20 years.

                  On Display

                  The ASA also had an exhibit booth—for the first time—at the annual conference of the National Science Teachers Association (NSTA).

                  ASA K–12 Statistical Ambassador Christine Franklin highly recommended the ASA make the NSTA conference a regular destination because 10,000 people attend. “The science community recognizes the importance of statistical reasoning in the science field and K–12 science curriculum,” she said. “They also welcome assistance with teacher professional development and collaboration with the statistics community. Many attendees expressed to me their appreciation of the ASA having a presence at the conference.”

                  Later this summer, the ASA will exhibit at the annual meeting of the American School Counselor Association in Denver. Paul Buckley, an ASA member who teaches at Gonzaga High School in Washington, DC, will represent the association.

                  Those staffing the booth enjoyed talking with K–12 math teachers, AP Statistics teachers, teacher educators, and other university faculty who stopped by. The booth included K–12 and undergraduate statistics education resources, information about careers in statistics, and contributions statisticians make to society.

                  A highlight at the booth was Statistics Teacher (ST), a new online journal published by the ASA/NCTM Joint Committee on Curriculum in Statistics and Probability for Grades K–12. ST supports the teaching and learning of statistics through education articles, lesson plans, announcements, professional development opportunities, technology, assessment, and classroom resources.

                  Bridging the Gap Between Common Core State Standards and Teaching Statistics, designed to help educators bring statistics into elementary- and middle-school classrooms, and Making Sense of Statistical Studies, which provides investigations for upper middle-school or high-school students to gain experience in designing and analyzing statistical studies, were available for purchase at the booth.

                  For free were copies of Guidelines for Assessment and Instruction in Statistics Education (GAISE) Pre-K–12 Report and the Statistical Education of Teachers (SET) report. Booth staffers also shared information about Census at School, a free international classroom project that engages students in grades 4–12 in statistical problem solving using their own real data, as well as the Meeting Within a Meeting (MWM) statistics workshop for math and science teachers and Beyond AP Statistics (BAPS) workshop, which will be held in conjunction with the 2017 Joint Statistical Meetings in Baltimore.

                  Displayed at the booth were winning posters from the ASA Poster Competition for K–12 students and information about the ASA Project Competition (written report) for students in grades 7–12.

                  Booth staffers gave out free one-year trial K–12 teacher memberships and displayed copies of Significance and CHANCE magazines. The ASA baby, toddler, and youth shirts for sale were also a reason some teachers stopped by the booth, which provided an opportunity for those at the booth to chat about statistics education resources.

                  To aid NCTM attendees interested in statistics, Nichols and ASA K–12 Statistical Ambassador Christine Franklin highlighted statistics-related talks.

                  Read more about the ASA’s K–12 education initiatives.

                  ASA Challenges Stat-Savvy Students Through Public Education Campaign

                  Thu, 06/01/2017 - 7:00am
                  Jill Talley, ASA Public Relations Manager

                    Just as the fields in which statistics is applied are dynamic, so too are the channels and resources we use to inform and engage students, teachers, and parents about the value of statistics education. The ASA’s public education campaign, ThisIsStatistics, continues to implement unique strategies and expand its creative educational portfolio to attract growing audiences both inside and outside the academic environment.

                    Growing its presence beyond math teachers and school counselors, ThisIsStatistics appeared at the National Science Teachers Association annual conference in April. The ASA’s K–12 Statistical Ambassador Christine Franklin <> promoted the campaign and shared statistics education materials with thousands of high-school science teachers across the United States. She also connected with producers of National Public Radio’s popular series, “Science Friday,” helping to spark interest and understanding of statistics as a scientific discipline to a potential global audience.

                    Keeping students, teachers, and parents updated on diverse statistics activities and developments, the ThisIsStatistics blog recently featured entries about dynamic research applications and career guidance during Mathematics and Statistics Awareness Month, professional opportunities to make a difference in climate change during National Environmental Education Week, a report from the Business Higher Education Foundation and PwC chronicling the demand for students and professionals with data science skills, and an inspirational profile of high-school statistics student Jenny Chen.

                    Like parents and other influencers, students consume news on a daily basis and are just as easily to be swayed by misrepresented or manipulated data that may appear legitimate and trustworthy. In an effort to combat such confusion, ThisIsStatistics is publishing a “quick tips” guide to help the public discern statistical facts from fiction so consumers can feel confident in the news they consume and data being reported.

                    While millions were drawn to TV screens during March Madness, hundreds of high-school and undergraduate students took to computers, brackets, and statistical software, competing in Statsketball 2017, a competition where they used statistics to predict outcomes of the 2017 NCAA Men’s College Basketball Tournament. Challenged to select teams most likely to win in an upset in the Round of 64 and/or compile a group of teams with the most potential for round-by-round victories, participants showed their statistical prowess could be applied to one of the country’s most popular sporting events.

                    Judges Laurel Chiappetta, founder of Data Diva statistical consulting; Stephen Loftus, analyst in baseball research and development for the Tampa Bay Rays; and Steven Rigdon, professor of biostatistics at Saint Louis University and chair of the ASA’s Statistics in Sports Section, rated contestants not only on calculations, but also their ability to effectively communicate statistical strategy.

                    Naveen Gooneratne, a senior at Lower Merion High School in Ardmore, Pennsylvania, and James Andrews, Jordan Levy, and Connor Heuerman, seniors at College Park High School in Pleasant Hills, California, won at the high-school level. Michael McLaughlin, a junior at Temple University, and Jason Thompson and Graham Pash, sophomores at North Carolina State University, took the top spots at the undergraduate level.

                    While sports statistics may be a captivating specialization among today’s youth, the ASA recognizes it’s not the only popular field in which students can apply statistics. Plans for the coming school year may include a forensic science hackathon in addition to the popular videos and quizzes.

                    Aside from contests, news feeds, and conferences, engaging students and other stakeholders these days stretches into the wide world of social media. Through its digital platforms, ThisIsStatistics continues to expand its reach and draw even wider interest. To date, the website has counted more than 221,000 visits, 41% of which come from a mobile device or tablet, while Facebook and Twitter posts and advertisements yielded 10,000 followers. More than 650 students have participated in ThisIsStatistics contests and webinars and, whether it’s learning about unique career opportunities or hearing directly from employers, YouTube viewers have watched the equivalent of almost five months of ThisIsStatistics videos.

                    Visit ThisIsStatistics for dynamic educational tools and resources to help foster statistical literacy and understanding in the next generation.

                    Do You Need a Website?

                    Thu, 06/01/2017 - 7:00am
                    David R. Bristol, Statistical Consulting Services Inc. This column is written for anyone engaged in or interested in statistical consulting. It includes articles ranging from what starting a consulting business would entail to what could be taught in a consulting course. If you have ideas for articles, contact the ASA’s Section on Statistical Consulting publication’s officer, Mary Kwasny.

                    David Bristol has been a statistical consultant for more than 10 years. Prior to consulting, he was a statistician in several therapeutic areas of the pharmaceutical industry. Most recently, he was director of biostatistics and statistical programming at Purdue Pharma.

                    After consulting for more than 10 years as an owner-only S-corp., I decided to get a website, but I am not sure why. If you want, take a look. The website looks nice, even if the name is a bit long. I am happy with it, and I hope it is productive for something. There are currently at least 1 billion websites, but hopefully adding mine will make a difference.

                    A few years ago, a client for whom I was subcontracting requested I use a business email address, instead of the Gmail address I typically used. They thought it would appear more professional to their clients. I contacted a large company that specializes in such things and got the business email address, as requested. However, I only used it for the one client. The provider would often send advertisements for website building and support to me, which I typically ignored. Then I had a potential client ask for my URL. That request led me to think a website may be expected by some potential clients and I might look more professional if I had one. I’m not sure looking more professional should be important to me at this stage of my career, but I got the website, and also another professional email address.

                    Recently, I asked members of the Consulting Section if they had websites and whether they would recommend having one to others. My intent was primarily to determine whether it would be recommended for new consultants, as I already had mine.

                    Instead of presenting my thoughts about having a website here, based on my limited experience, I think the responses to my post would be more informative. Many of the respondents have several years of experience, typically with a website. Some responses are given here and are hopefully summarized in a way to be most useful, especially to those just starting or considering a website. Some comments are exactly as stated, quoted without specifying the person who wrote it. My apologies if any are taken out of context. Several recommendations, sometimes conflicting, were provided and are given here.

                    Recommendations

                    Often, there was an initial expectation that a website would generate business by attracting new clients. However, most of those respondents noted this did not occur and word-of-mouth is more successful: “In my case at least, it turned out that doing a good job for my existing clients pretty much assured me of repeat business and new clients via word-of-mouth.”

                    This was part of the motivation for my original question because I have done little business development beyond passing out business cards at professional meetings. However, a website can be considered “an important marketing tool for your business.” Or, as another put it, “It brings me in lots of business.” A website “is not the be-all-end-all to marketing and getting clients, rather it needs to be a tool.” However, I think the most important aspect is that “… [I]t is there when clients need to refer me to their colleagues.”

                    One respondent replied that “it’s the easiest way to let clients see what you do.” I think this is a great reason to have a website. If one checks my website, it is obvious I perform strategic work associated with clinical trials. A potential client would probably not contact me for any consulting project unless it is related to clinical trials. This may eliminate any other interesting projects, but someone else would probably better serve the client. A website “gives details about what I do and why, and gives the prospective client a bit of info about me and how I work.” A “good web presence can help clients see if you’re the type of statistician they want to work with.”

                    Many consultants have a website because they offer somewhat special services and use their website to describe them. Some, in fact, use their website to present their rates. Others noted it can be used to post newsletters, recordings of webinars, or recent publications. However, the complexity is a matter of personal taste, need, and the target audience. “Think of it as an electronic version of a business card. List your contact information, a bit about your qualifications, and maybe post your consulting rate. You could post other things like customer testimonials.”

                    My preference is a minimal website without all the information a potential client might want. The contact information should always be provided; if there is an interest, I can be quickly and easily contacted. Another goal is that “you can list past clients.” I have never provided such information or testimonials to a potential client and would not do so without prior approval. I have been asked for a list of clients a few times, but think it treads on the violation of confidentiality agreement. I know providing this information is a bit controversial.

                    Many websites have a link to the consultant’s CV. “I have a website, and it contains a mission statement, my CV, and a brief bio.” I do not include such a link, but I include a brief summary of my work experience and a description of my expertise. “The specific content will depend on who you expect to be selling to, negotiating with, and/or working with.”

                    Several respondents provided information regarding the provider. “Don’t get a website littered with other people’s ads.” As far as specific providers, there were several recommendations. One can use “1and1.com, a very professional company with good tech support.” Also, “Weebly is free and ad-free, and I think Google is, too.” “As far as designing the site, www.atomicbluedesign.com does a really good job, and for a small website, they are very reasonable.” One respondent got a “package through GoDaddy that provides me with my domain name, hosting, and email accounts.”

                    That is what I did. Apparently, I could have saved some money, but I was pleased by the professionalism and support. Speaking of saving some money, one respondent had a website, but it didn’t meet his needs and expectations, so he cancelled it when it was due for renewal.

                    As a final note, I was “ghosted” by the potential client who wanted to know my URL. For those who aren’t familiar with the term, ghosting is the disappearance of someone with whom you have a relationship without any further communication. Although it is usually used for dating relationships, most of us are used to the same behavior by potential clients.

                    I recently saw a TV commercial stating I don’t need a website … I need an app. I am not going to get an app.

                    Interview with Keith Mitchell, Prime Minister of Grenada

                    Thu, 06/01/2017 - 7:00am
                    David Marker

                      David Marker, Mary Marker, and Keith Mitchell in the prime minister’s office in Grenada

                      Keith Mitchell is the only head of state with a PhD in statistics. After earning his PhD in the 1980s, he worked as a statistician. We worked together at Applied Management Sciences, providing statistical support to the U.S. Energy Information Administration. I left to work at Westat, and he returned home to Grenada after the U.S. invasion in 1984. We met again at the prime minister’s office in Grenada in late February. Below are answers to a few questions I asked him about the intersection of Grenada and statistics in his life.


                      What led you to earn a PhD in statistics?

                      The basics started when I was in primary school. While I competed well with my peers in most subjects, I was always at the top of the class in arithmetic. This continued into secondary school, where I developed an even greater love for mathematics and all its applications in life.

                      While I was studying for my Bachelor of Science degree (at the University of the West Indies) in chemistry and mathematics, I developed a much better sense of mathematics and its role in solving life’s problems, so I decided I wanted to specialize at the post-graduate level in applied mathematics, specifically mathematical statistics.

                      Why did you select American University in DC?

                      After completing my Master of Science degree in mathematics at Howard University and having decided I wanted to do research in statistics, I applied to various universities with the sole aim of securing a postgraduate scholarship or teaching assistantship. After receiving several offers, the program at American University offered me the best hope of achieving my dream while supporting my family in America and Grenada.

                      How did you get involved in politics in Grenada?

                      The foundation for getting involved in politics might have been established since I was a child. I was always fond of people, and having come from a very poor background, I was always concerned about unfairness in our society and how little input the ordinary people had in governance of their country. I wanted to make a difference by speaking out against injustices. As a young man, I had a great platform to do so in the classroom, being a teacher at the secondary-school, high-school, and university levels, where I felt I could positively influence the lives and thinking of many young people.

                      My active role in sports—cricket in particular—was also a major avenue for influencing social issues in my society. In the early 1970s, a number of my influential friends pleaded with me to take part in the general elections of 1972.

                      It was therefore a natural consequence and no surprise when, in 1984, I give up my professional responsibilities in Washington, DC, to answer the call for patriots to help rebuild Grenada after the revolution.

                      You are the longest-serving prime minister in Grenada’s history (1995–2008, 2013–present). Has your statistical background been helpful in this role?

                      In making decisions that affect the lives of others, it is important that those decisions are made with consideration of correct historical data, proper analyses, and the best conclusions possible.

                      My statistical background has helped influence the establishment of a proper and professional statistics department, which can collect, store data on government activities, and analyze them appropriately. Before the establishment of this department, low priority was given to science and data collection when making far-reaching decisions that affect the lives of others.

                      You also serve as minister of finance, in which role you oversee your national statistical office. What are the special challenges that confront a statistical office for a small island nation?

                      There are many challenges faced by the National Statistical Office (NSO) for a small island nation relative to the production and dissemination of statistics required by decision-makers. These include the following:

                      • Limited human and financial resources, reflected in a lack of capacity that affects the quantity and quality of data produced and disseminated.
                      • Institutional weaknesses that lead to the inability to keep up with internationally recommended data requirements and standards. For example, a weak IT infrastructure to handle the increasing demands for data, to exploit Big Data while at the same time ensuring data security.
                      • Inadequacies in the legal framework, which affect the entire operation of the NSO, including its positioning as a central institution in the National Statistical System (NSS) to exhibit effective guidance of the NSS, technical independence in the performance of its work, and lack of coordination in data sharing for the compilation of statistics across all producing agencies of the NSS.
                      • The lack of management and leadership training to effectively address the demands and challenges facing the NSO and NSS in general, which require leadership that can exhibit flexibility and innovation in driving the change process required to improve the production of statistics.
                      • Low response rates, especially from establishments, which undermines the quality of critical statistics like GDP and balance of payments. Often, key suppliers do not have the confidence that their data will be treated as confidential, despite the provision in all statistics legislation prohibiting the disclosure of individual-level data.
                      • Inadequate user orientation, which affects the relevance of the statistics and the integrity of the systems of statistics produced.
                      Who are the most interesting political leaders you have met as prime minister? Any interesting stories about meeting them you wish to share?

                      While I have met many interesting political leaders in my more than 30 years in politics, and even before, I must say my top two political personalities are the now deceased Fidel Castro and Nelson Mandela, in that order.

                      I was impressed with Fidel from the first moment I met him because it was clear to me he was not the person he was painted to be. I spent many hours with him, and those moments I will always cherish. In my opinion, he was a man who cared deeply about people much more than the propaganda made it out to be. I got into politics because of my love for people and, in him, I saw the same passion for people.

                      He was a very interesting man to sit with because he had such wide-ranging interests and knowledge. We spoke at length about politics, his doubts about religion (he never said to me he was not a believer, just that he had his doubts). He was also engaging on my particular passion: sports and mathematics. And he was also very candid in discussing his upbringing and passions and choices in politics, especially his belief in socialism.

                      In Nelson Mandela, I admired the simplicity of the man and his human touch and spirit. We first met at the Heads of Government of the Commonwealth meeting in 1997 in Scotland. On meeting him, I could not help but be impressed and inspired, knowing his history and the sacrifices he made personally toward the liberation of his people.

                      Do you have suggestions for statisticians interested in going into politics?

                      My suggestions here are not only for statisticians, but scientists in general. Politics must be based on logic, so if you want to be involved in governance, your decisions must be based on serious logic, not only on feelings or whims. That’s where statistics come in. Good statistics must be the basis for good governance. You get into politics to improve people’s lives. The only way to do that is to gather information to make informed decisions.

                      The statistics generated must be understood in the context of how people’s lives must change. Then you need to adjust policies to ensure you make better decisions. In addressing a lawyer friend in parliament once, a long time ago, I said to him playfully, “My brother, the problem with you guys is that people pay you to create confusion. Those like me, statisticians, they pay us to find solutions.” In politics, it should be the same.

                      Grenada is a beautiful island. Please tell ASA members why they should visit.

                      I invite everyone to our beautiful country, Pure Grenada, Isle of Spice. Our country is not only aesthetically beautiful—but I assure you will find the friendliest people you have ever met in Grenada.

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