MBM437 Introduction to Data Science for Marketing

• Topics

  In this course, we will follow closely the structure of our textbook (i.e., Llaudet and Imai 2023) to present students with important topics in the field of data science for marketing. The list of core topics is below:

  1. Introduction to R

  Since we will use the R programming language in all the data science assignments in this course, there will be 1-2 sessions where students will be introduced to R, RStudio, and R Markdown and their basics. Note that no prior knowledge of R is required.

  2. Causality and experimentation

  While causality is one of the most crucial topics in science in general and data science in particular, it is also a difficult topic to study for many people. In this session, we will discuss important conditions to make a (valid) causal claim and what to do when those conditions are not met.

  3. Measurement and survey research

  Another fundamental topic in data analysis is measurement. Here we will talk about different measurement issues that can occur with a given marketing data set such as reliability and validity. We will also discuss the importance of sampling and the representativeness of our sample.

  4. Prediction

  It is common for marketing researchers or data analysts to predict future customer behaviors, the firm’s market share, or the future conditions of the market. In this session, we will discuss possible modeling approaches to do prediction (e.g., multiple linear regression) and how to interpret the results.

  5. Probability

  There is a lot of uncertainty when we work with data and depending on our purposes, we often need to calculate certain probabilities conditional upon a given list of assumptions. With this topic, students will be introduced to the relevant concepts about probability and probability distribution, in addition to the major differences between the two popular (and dominant) perspectives: frequentist and Bayesian statistics.

  6. Uncertainty

  Students will learn about how to quantify uncertainty in data analysis (e.g., how to calculate and interpret confidence intervals, how to conduct hypothesis testing, etc.).

• Learning outcome

  Knowledge

  After completing the course, students should know:

  • the basics of data science for marketing and relevant concepts
  • the different characteristics of different types of data
  • the connections between practical business problems and required data
  • a number of data science methods to analyze data and how to apply it in marketing tasks

  Skills

  After completing the course, students should be able to:

  • use R for data cleaning, manipulation, preparation, and visualization
  • work with raw data of different types for further analyses
  • perform a number of data science methods to analyze data
  • understand the benefits and limitations of a selected number of data science methods for marketing tasks

  General Competence

  After completing the course, students should be able to:

  • develop a solid understanding of the study topics as well as the applications of data science in marketing
  • reflect on the benefits and limitations of the discussed data science methods when working with (marketing) data
  • be ready for more advanced courses in marketing and customer analytics

• Teaching

  This course will use a combination of regular lectures, lab sessions, and data analysis exercises. Regular lectures will introduce students to fundamental methods of quantitative data analysis in social science and the corresponding assumptions of these methods. Each regular lecture will typically be followed immediately by a lab session that illustrates how to use R to implement these methods using real-world data and how to interpret the results accordingly. Students will also need to work on 6 small data analysis exercises throughout the course (graded as Pass/Fail) and it is compulsory to pass at least 4 of these assignments. Solutions to these assignments will be provided and discussed.

• Recommended prerequisites

  Knowledge of statistics would be an advantage.

• Required prerequisites

  None

• Compulsory Activity

  There will be 6 exercises (work requirements) on 6 important topics in the course where students will work (individually or in groups, with a maximum of 4 students per group) with corresponding data science assignments. You are required to pass at least 4 of them to get course approval.

• Assessment

  Term paper that can be done individually or in groups (maximum of 4 students per group), which is 100% of the final grade in the course.

• Grading Scale

  Grading scale: A-F

• Computer tools

  R, RStudio, R Markdown

• Literature

  Llaudet, E. and Imai, K. (2023). Data Analysis for Social Science: A Friendly and Practical Introduction. Princeton University Press.

  And a list of scientific articles.