Data Science for Marketing Analytics

2026/2027

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

Department of Management

Course type:

Compulsory course

When:

2 year, 1, 2 module

Instructor

Ляпин Илья Викторович

Course Syllabus

Abstract

The course trains students to apply data-science methods to marketing and customer analytics on real and synthetic datasets, with a focus on decisions that maximize incremental profit under modern measurement constraints (privacy regulation, deprecation of third-party identifiers, attribution loss). The course is built around three pillars: • (1) Experimentation and causal inference as the foundation of measurement; • (2) Predictive customer analytics — customer lifetime value, churn, response, time-to-event; • (3) Personalization and decision systems — uplift targeting, recommendation, contextual bandits. The working stack is Python-first (pandas, scikit-learn, statsmodels, econml, causalml, pymc-marketing, lifetimes, lifelines, scikit-survival), with R used selectively where it remains best-in-class (notably Meta Robyn for MMM). All assignments are submitted as reproducible Git repositories. Prerequisites: an introductory course in statistics or econometrics and basic programming experience.