Applied Data Analytics

Introduction

Please note that this is a preliminary course description. The final version will be published in June 2026.

This course gives an applied introduction to the most important techniques in business-related data analytics. Students are given hands-on experience using programming languages to work with data, using descriptive statistics to motivate models, and using models to turn data into actionable knowledge. Mathematical and statistical theory and applied data analysis will be interwoven into projects that the students will solve.
 

Course content

• An introduction to sums and summation notation, and other foundational issues.
• An introduction to research design, and some case studies. An introduction to cross-sectional studies, time series and longitudinal studies. 
• Introductory descriptive statistics, data visualization and data re-organization. Data exploration and visualization.
• A review of statistical inference and large sample inference using simulation-experiments.
• An introduction to statistical models. Simple regression models. Explanation versus prediction. Machine learning versus traditional multiple linear regression modeling.
• Multiple linear regression: Dummy variables, interaction terms, data transformations and interpretation.