Data Analytics, BS

Please note, the program-level Learning Objectives here are specific to the Data Analytics and Computer Science classes within the program; and thus, do not include courses in mathematics.

• Graduates will possess a comprehensive knowledge base of statistical concepts: They will be able to evaluate and apply fundamental statistical concepts in the context of a broad range of data problems, including Bayes Theorem, common statistical tests and biases, inference and causal inference and hypothesis testing.
• Graduates will be able to apply and evaluate machine learning algorithms in a business problem context, with an emphasis on selecting predictive modeling only when appropriate. Further, they will be able to perform feature engineering and data preprocessing in order to improve the accuracy and efficacy of predictive models.
• Graduates will be able to perform in-depth exploratory analysis to form hypotheses. They will be able to design experiments to answer causal questions and evaluate the results of the experiments.
• Graduates will be able to communicate effectively in written, oral, and visual forms and use visualization techniques to communicate insights.
• Graduates have considered their vocational goals and are reflective of how these may be embodied in their professional and/or personal lives.

Total Credit Hours: 120

• General Education Core: 38 hours
• Data Analytics Core: 18 hours
• Programming Requirements: 12 hours
• Mathematics Requirements: 10 hours
• Choose two: 6-8 hours
• Elective Requirements: 36 hours (Watch upper division hours; 30 upper division hours required.)