Applied Quantitative Methods to Analyse Business Data Vrije University - Summer & Winter graduate programs

We take an applied perspective on data analytics for businesses, emphasizing methods to evaluate different types of data that you encounter in your every-day work (e.g., customer purchase data, surveys, website data).

Course description

The course focuses on quantitative applications to real-world business data, teaching students econometric methods (principles of statistical testing, multiple linear regression, logistic regression, time series regression, panel data analysis) and implementation of these methods (skill development) in the statistic software R.

Students will get to know different types of company data (cross-sectional, longitudinal, panel data), learn the basic frequentist approach to statistical test theory, and be introduced to the main workhorses of causal analysis. They will gather theoretical knowledge about these methods, their assumptions, and remedies to violations of these assumptions. Furthermore, students will apply this knowledge practically to data provided from openly available case studies and proprietary records from collaborating companies using the free statistical software R. Finally, academic articles applying the introduced methods teach students comprehension and interpretation of econometric research. Thus, (1) econometric knowledge, (2) implementation skills, and (3) understanding of empirical academic procedures are the main objectives of this course.

Course Structure

The course follows a lecture-application sequence, with six major topics:

1. Introduction
2. Principles of statistical testing
3. Multiple linear regression (continuous outcome; cross-sectional data)
4. Logistic regression (binary outcome; cross-sectional data)
5. Time series regression (continuous outcome; longitudinal data)
6. Panel data analysis (continuous outcome; longitudinal data)

By the end of this course, students will be able to:

• understand different types of real-world data and the methods suitable to analyze them
• apply quantitative methods themselves using a popular, open-source statistical software
• interpret results of quantitative models and use them to make business decisions

Although methodological knowledge provides the basis of this course, the focus is on business practice. Academic articles serve as examples for such application, but the course is also suitable for students who seek a career in industry, positioning themselves in the intersection of management/marketing and analytics.

Examples include quantitative marketing fields such as business intelligence, data-driven customer relationship management, or related fields. PhD students aiming for an academic career can benefit from broadening and/or deepening their methodological knowledge and skillset, for instance in the direction of time-series or panel data analysis.