DATA 201 - Statistical Methods in Data Science

Statistical concepts and applications related to data science including advanced exploratory data analysis, nonparametric inference and simulation for larger datasets, logistic regression modeling, statistical programming, and advanced machine learning. Topics include fundamentals of supervised and unsupervised learning, resampling methods, model selection and regularization, time series or sequence data, tree-based methods, and unstructured data. PREREQUISITE(S): A grade of C or better in DATA 101 or consent of department. Three hours each week.

3 semester hours

Course Outcomes:
Upon completion of this course, a student will be able to:

Select appropriate existing analytical and presentational tools for specific analyses of large databases.
Develop new and appropriate analytical and presentational tools for specific analyses of large databases through programming.
Implement data science practices that allows for reproducible results.
Summarize findings based on complex analyses in a concise way for a general audience using multivariate graphics and statistical measures.
Evaluate and apply ethical principles and practices in the data lifecycle.

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