DATA 201 - Introduction to Data II

5 CR

This course builds upon the foundation established in DATA 101, providing a deeper exploration of statistical inference and advanced modeling techniques. Students will explore advanced topics such as model comparison, multivariate analysis, model validation, supervised and unsupervised learning algorithms, and concepts related to big data. The course emphasizes hands-on experience, allowing students to further develop their skills in using modern software tools and programming languages to analyze complex datasets, build predictive models, and make data-driven decisions.

Prerequisite(s): Completion of DATA 101 with a C or better or permission of the instructor

Course Outcomes

Explain advanced inferential statistical methods and their practical applications in applied data science.
Compare and evaluate a variety of statistical models, including multivariate models and models with interactions.
Implement model validation and selection techniques to optimize model performance.
Apply supervised and unsupervised machine learning algorithms for data analysis, classification, and prediction.
Understand and apply foundational concepts in big data and distributed computing in the context of applied data science.
Solve complex applied data science problems using advanced statistical and computational tools and techniques.

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