Course 2024-2025 a.y.

During interactive lectures, you'll gain insights into marketing analytics and acquire the skills to write your own computer code. In the initial part of the course, we'll focus on mastering the basics of regression analysis, decision trees, and the R language. As we progress into the latter part of the course, we'll delve into advanced methods for machine learning, prediction, and simulation.

In a hands-on group project, students will unleash the power of advanced techniques and causal inference models to unravel the intricacies of consumer decisions. Whether it's analyzing purchase patterns, decoding subscription behaviors, or predicting churn, students will choose the managerial question that fascinates them the most. Consequently, students will present the outcomes of a group project they've conceptualized and analyzed. For the group project, students will use R to simulate all the data they have to work with. (I.e., students do not need to carry out a survey or obtain company data for this course.)

Topics that can be expected during the course:

    Regression Analysis

    Regression and Decision Trees

    Model Assessment

    Machine Learning Methods: Random Forest, Lasso

    Matching

    Difference-in-differences

    Endogeneity

    Simulation of Consumer Choice

    Optional further topics: e.g., online reviews, product search, recommendation systems

• Apply causal inference and machine learning models to understand consumer decisions.
• Use R for statistical analysis.
• Create realistic artificial datasets.
• Simulate consumer decisions using utility maximization and discrete choice concepts.
• Predict outcomes of managerial decisions through simulations.

• Recognize the impact of data analysis on business decisions.
• Select and apply appropriate data analysis methods for business scenarios.
• Determine suitable analysis types for various business needs.
• Present data analysis findings to both experts and non-experts.
• Identify critical data sources for decision-making.
• Recommend profitable marketing campaigns based on data analysis.

Guest speakers

Senior data scientist(s) with 10 years of industry experience will share their insights on working in a data science team, the importance of managerial acumen, and how to lead data teams efficiently.

Exercises

We will cover a variety of coding exercises in R during the classes.

Group assignments

Students will collaborate in groups on a project that involves creating a computer simulation of a business scenario and testing the outcomes of various managerial decisions. An experienced data scientist will supervise the project, providing feedback on methodology and managerial insights. This approach offers an excellent opportunity to learn about industry requirements, the relevance of data analytics, and the use of effective data inputs.

Interactive class activities

For the group project, dedicated sessions will help student teams learn how to simulate data, form interesting managerial questions, and analyze the business scenario using predictions and data-driven simulations.

• Written individual exam (traditional/online)

• Collaborative Works / Assignment (report, exercise, presentation, project work etc.)

• Peer evaluation

Written Exam:

The final written exam will be based on the topics covered during class. It will evaluate students’ understanding and ability to apply the methods, models, and tools we have discussed. The exam does not include any elements related to computer coding, nor does it contain questions pertaining to coding. (Max 20 points)

Group Assignment:

Students will engage in a group project where their collaborative efforts will be assessed. The quality of group work, including the application of analytical methods, data interpretation, and presentation skills, will contribute to the overall evaluation. (Max 11 points)

Final Presentations Bonus Points:

A bonus point will be given to each member of the student group that delivers the best final presentation, as voted by their peers.

Lecture slides

Selected chapters from: Békés, Gábor, and Gábor Kézdi. Data Analysis for Business, Economics, and Policy. Cambridge University Press, 2021. (the digital version of the book is available for free in the library)

RStudio (free version) installed on laptop.