Courses aimed at Graduate Students in Statistics

Data Science Practicum

Course Number: 
628
Credits: 
1-3
Frequency: 
Spring semesters
Course Description: 
This course is aimed at providing graduate students with an understanding of and experience with turning statistics concepts into practice through data science practicums inspired by realistic projects. Students will combine theory and methods expertise with communications skills to translate from a vaguely stated project description and complex data set into a concisely summarized analysis, including both written and graphical interpretation that can be used by decision makers in an organization.
Course Prerequisites: 
Graduate student in Statistics

Professional Skills in Data Science

Course Number: 
627
Credits: 
1-3
Frequency: 
fall semesters
Course Description: 
This topics course is aimed at providing statistics graduate students with an understanding of and experience with important aspects of professional development in statistics, including skills with internet tools, sophisticated use of statistical languages (such as R) and other emerging topics.

Statistical Methods II

Course Number: 
602
Credits: 
4
Frequency: 
Spring semesters
Course Description: 
Together with STAT 601, this course is to provide graduate students in statistics and related quantitative fields with a thorough grounding in modern statistical methods. The specific learning outcomes for the course are to understand data collection in context (how/why data were collected, key questions under study); explore data by effective graphical and numerical summaries; understand probability concepts and models as tools for studying random phenomena and for statistical inference; analyze data using appropriate, modern statistical models, methods, and software; understand the statistical concepts underlying methods; develop the ability to interpret results and critically evaluate the methods used; communicate data analysis and key findings in context.
Course Prerequisites: 
Stats 601

Statistical Methods I

Course Number: 
601
Credits: 
4
Frequency: 
fall semester
Course Description: 
Together with STAT 602, this course is to provide graduate students in statistics and related quantitative fields with a thorough grounding in modern statistical methods. The specific learning outcomes for the course are to understand data collection in context (how/why data were collected, key questions under study); explore data by effective graphical and numerical summaries; understand probability concepts and models as tools for studying random phenomena and for statistical inference; analyze data using appropriate, modern statistical models, methods, and software; understand the statistical concepts underlying methods; develop the ability to interpret results and critically evaluate the methods used; communicate data analysis and key findings in context. This course will assume students have had at least one semester of calculus and one semester of linear algebra.
Course Prerequisites: 
Requisites: Graduate standing
Course Audience: 
Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirement

Advanced Financial Statistics

Course Number: 
801
Credits: 
3
Frequency: 
Spring semesters
Course Description: 
Statistical theory and methodology for modern financial data. Topics include financial stochastic models based on time series and stochastic calculus, modern statistical inference, and statistical learning for financial data as well as their applications to financial problems.
Course Prerequisites: 
Stat 701
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