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
Fulfills General Education Requirement(s): IF-Quantitative Data Literacy (IFQD)
DescriptionIntroduction to methods used to analyze and present data with an emphasis on interpretation and informed decision making. Exploratory graphical methods will be developed using statistical software for data analysis.
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DSST 289 Introduction to Data Science
Units: 1
Fulfills General Education Requirement(s): Linguistics elective (LING)
DescriptionTopics will include techniques for collecting, organizing, analyzing, modeling, and presenting data. Applications to a variety of fields will be emphasized. Includes an extensive introduction to statistical programming.
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DSST 310 Causal Inference
Units: 1
DescriptionIntroduction to a theoretical framework and statistical methods for assessing cause and effect. Will cover experimental design as well as well as common non-experimental approaches to causal inference, including difference in differences, regression discontinuity, and instrumental variables analysis.
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DSST 311 Data Collection and Design
Units: 1
DescriptionIntroduction to the techniques for generating and modeling complex, multivariate datasets from both experimental and non-experimental sources. The course will focus on the relationships between data collection, statistical methods, and the underlying research questions.
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DSST 312 Predictive Models
Units: 1
DescriptionAn introduction to the core concepts of predictive modeling. Includes classical techniques for prediction as well as a survey of more recent advances.
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DSST 329 Probability
Units: 1
DescriptionTheory, interpretation, and application of models for randomness and uncertainty. Topics include probability spaces, random variables, expectation, limits, and simulation.
PrerequisitesMATH 212 or MATH 235
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DSST 330 Mathematical Statistics
Units: 1
DescriptionFormal introduction to methods of statistical estimation and model fitting. Topics include parameter estimation, confidence regions, significance testing, and Bayesian inference. Applications will be drawn from a variety of domains.
PrerequisitesDSST 329 or MATH 329
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DSST 331 Regression Theory and Applications
Units: 1
DescriptionStudy of the geometric, computation, and probabilistic properties of linear and generalized regression models. Extensions may include non-linear models, regularization techniques, and neural networks. Applications applying models to data from various domains using the R programming language will illustrate the connection between theory and practice.
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DSST 389 Advanced Data Science
Units: 1
Fulfills General Education Requirement(s): Linguistics elective (LING), MTEC math elective (MTEL)
DescriptionAdvanced approaches to the analysis of data. Focuses on putting together larger, complete projects that combine quantitative and qualitative analyses.
PrerequisitesDSST 289 or MATH 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 in Data Science & Statistics
Units: 0.25-1
DescriptionSelected topics in data science and statistics.PrerequisitesDSST 289, additional prerequisites may be required depending on the topic.
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DSST 406 Summer Undergraduate Research
Units: 0