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Regular version of the site

Data Science for Marketing Analytics

June 28 – July 9, 2021

40 Contact hours

This course unlocks a huge range of quantitative techniques to expand your experience in marketing and advertising.

Course Description

This course introduces hands-on approaches to solving marketing problems using quantitative data. Multiple specific problems/cases will be showcased from various fields of marketing analytics: marketing research, customer analytics, social network analysis, advertising, etc. The course will give good insight into quantitative methods (statistical analysis of substantial volumes of sales/survey/customer data).

The course is taught by Evgeny A. Antipov.

The course features a unique set of computer exercises using R, Excel and other packages that will give our students skills that are immediately applicable in the industry

Why Choose This Course?

  • Hands-on: students will use Excel and R during most classes
  • Templates for solving real-world problems will be provided
  • Lecturers are open for future research collaborations leading to peer-reviewed journal publications
  • Students will be granted free access to DataCamp.com platform for learning data science for the period of the course and several months after the course


  • Advanced Excel functions useful for marketing analysis
  • Optimization modeling in marketing
  • Exploratory analysis of marketing data
  • Data processing of customer data
  • Customer segmentation
  • Introduction to predictive modeling and its applications: modeling customer lifetime value, churn, etc.
  • Sales modeling
  • Marketing Mix Modeling
  • Introduction to text mining for marketing

Skills and Competence

Upon completion of this course, students will be able to: 

  • Use Excel and R for solving some of the key marketing problems
  • Extract maximum insight from various data sources available to a company
  • Apply basic statistical and machine learning method to increase profit
  • Interpret the results of data analysis, followed by reporting and communicating the results

Teaching Methods

The course will contain tutorial sessions where students will conduct analysis along with the instructor followed by a session where they will solve similar problems on their own to check their understanding.


  • Mandatory: basic knowledge of Microsoft Excel
  • Some training in general management is recommended, but participants with other backgrounds are also welcomed
  • Introductory knowledge of R programming is preferable but not mandatory. We recommend at least studying A (very) short introduction to R

Final Assessment 

Final problem set solved in class (time limit: 80 minutes). The test will include multiple-choice and open-ended questions that will test problem-solving skills. Some problems may require using MS Excel or R.

Final Grade Background

To complete the course students, must attend no less than 70% of lectures and seminars and solve problem sets given to them in class regularly. The final grade will be computed according to the following scheme: 0.5*Final Test + 0.5*Classwork. 

Recommended Reading List

Hodeghatta, U. R., & Nayak, U. (2016). Business Analytics Using R-A Practical Approach. Apress.

Chapman, C., & Feit, E. M. (2015). R for marketing research and analytics. New York, NY: Springer.

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