A continuation of DATASCI 203, this course trains data science students to apply more advanced methods from regression analysis and time series models. Central topics include linear regression, causal inference, identification strategies, and a wide-range of time series models that are frequently used by industry professionals. Throughout the course, we emphasize choosing, applying, and implementing statistical techniques to capture key patterns and generate insight from data. Students who successfully complete this course will be able to distinguish between appropriate and inappropriate techniques given the problem under consideration, the data available, and the given timeframe.

Requisites

• MIDS students only. DATASCI 203 taken in Fall 2016 or later and completed with a grade of B+ or above. Strong familiarity with classical linear regression modeling; strong hands-on experience in R; working knowledge of calculus and linear algebra; familiarity with differential calculus, integral calculus and matrix notations.

Credit Restrictions

Students will receive no credit for DATASCI W271 after completing DATASCI 271. A deficient grade in DATASCI W271 may be removed by taking DATASCI 271.

Repeat Rules

Course is not repeatable for credit.