Applied Quantitative Methods

Applied Quantitative Methods to Analyse Business Data

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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:

A. Motivation and Statistical Background (Day 1)

(1) Introduction

(2) Principles of statistical testing

B. Refresher: Analysis of Surveys and (Field) Experiments (Days 1-4)

(3) Multiple linear regression (continuous outcome; cross-sectional data)

(4) Logistic regression (binary outcome; cross-sectional data)

C. Main Focus: Analysis of Observational Data (Days 5-10)

(5) Time series regression (continuous outcome; longitudinal data)

(6) Panel data analysis (continuous outcome; longitudinal data)

Continue reading below for more information.

Summer courses

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Fees, discounts and scholarships

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Application and admissions

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Course level

Master / PhD

Credits

3 ECTS

Contact hours

Language

English

Tuition fee

€735 - €1310

Forms of tuition and assessment

This course will be taught through lectures and business case studies. Students will apply new knowledge practically to data provided from openly available case studies and proprietary records from collaborating companies using the free statistical software R.
Assessment will be carried out in in-class assignments.
Learning objectives

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
About the course coordinator

Nico is Associate Professor of Technology & Innovation Marketing at Vrije Universiteit Amsterdam. He studied business administration at the University of Muenster, Germany, and the University of Rome “La Sapienza”, Italy. He obtained his doctoral degree at the Marketing Center Muenster, Institute of Business-to-Business Marketing (Prof. Klaus Backhaus) in 2014 with work on network effects in hardware-software platform markets. During this time, Nico also engaged in several consulting projects for mid-sized B2B and B2C companies.
After his PhD, Nico obtained practical experience at the e-commerce start-up Mister Spex in Berlin and at Vodafone Germany in Duesseldorf. In September 2016, he revisited academia as a postdoctoral researcher at the University of Cologne, Department of Retailing and Customer Management (Prof. Werner Reinartz). Nico joined the marketing department of Vrije Universiteit Amsterdam in 2020.
In his research, Nico focuses mainly on topics related to technology and innovation marketing. His research is mostly quantitative, applying marketing models on observational data or data generated through (field) experiments. His work on digital platforms includes the dynamics of hardware-software and software-software systems, such as entertainment systems or automobiles, but also matchmaking and brand platforms like branded apps. Related to the latter, Nico has also been studying innovations in retailing (e.g., disruption of the retailing value chain, mobile commerce) and brand communications (e.g., impact of privacy regulations, media channel orchestration) both conceptually and empirically. His work has been published in major academic outlets such as Journal of Marketing, Journal of the Academy of Marketing Science, International Journal of Research in Marketing, and Journal of Interactive Marketing, as well as in practitioner and transfer magazines like Harvard Business Review, Harvard Business Manager, Marketing Review St. Gallen, and Marketing Intelligence Review.
Preliminary syllabus

Here you can download the preliminary syllabus for the summer course.
^{*Note that it is preliminary and that it still might be subject to change.}