Courses
The minor in Data Science & Statistics prepares students to address the challenges of collecting, understanding, and presenting structured and unstructured data from a variety of different domains and contexts.
The program takes an interdisciplinary approach built around five specific skills needed to achieve these goals:
- Developing proficiency in data-oriented programing
- Understanding probability theory and statistical inference
- Understanding which methods are appropriate for which kinds of data analysis
- Ability to identify and address the ethical and privacy concerns regarding data analysis, and
- Gaining experience applying techniques and presenting results within the context of an application domain.
Courses
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DSST 189 Introduction to Statistical Modeling
Units: 1
DescriptionTopics will include exploratory data analysis, correlation, linear and multiple regression, design of experiments, basic probability, the normal distribution, sampling distributions, estimation, hypothesis testing and randomization approach to inference. Exploratory graphical methods, model building and model checking techniques will be emphasized with extensive use of statistical software for data analysis.
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DSST 289 Introduction to Data Science
Units: 1
DescriptionMultiple linear regression, logistic regression, ANOVA and other modeling based topics. Exploratory graphical methods, model selection and model checking techniques will be emphasized with extensive use a statistical programming language (R) for data analysis.
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DSST 329 Probability
Units: 1
DescriptionIntroduction to the theory, methods, and applications of randomness and random processes. Probability concepts, independence, random variables, expectation, discrete and continuous probability distributions, moment-generating functions, simulation, joint and conditional probability distributions, sampling theory, laws of large numbers, limit theorems.
PrerequisitesMATH 212 or MATH 235
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DSST 330 Mathematical Statistics
Units: 1
DescriptionIntroduction to basic principles and procedures for statistical estimation and model fitting. Parameter estimation, likelihood methods, unbiasedness, sufficiency, confidence regions, Bayesian inference, significance testing, likelihood ratio tests, linear models, methods for categorical data, resampling methods.
PrerequisitesMATH 329 or DSST 329
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DSST 389 Advanced Data Science
Units: 1
DescriptionComputational statistics and statistical algorithms for building predictive models from large data sets. Topics include model complexity, hyper-parameter tuning, over- and under-fitting, and the evaluation of predictive performance. Models covered include linear regression, penalized regression, additive models, gradient-boosted trees, and neural networks. Applications are drawn from many areas, with a particular focus on processing unstructured text and image corpora.
PrerequisitesMATH 289 or DSST 289
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DSST 390 Directed Independent Study
Units: 0.25-1
DescriptionTopics independently pursued under supervision of faculty member. -
DSST 395 Special Topics Data Science and Statistics
Units: 0.25-1
DescriptionSelected topics in data science and statistics.PrerequisitesDSST 289; additional prerequisites may be required depending on the topic.