Introductory Data Science for Marketing

Introduction

The course provides students with a rather detailed overview of methods for statistical modeling and inference. The course focuses on fundamental data science topics such as descriptive analysis, basic probability theory, parameter estimation, procedures for making inference, and regression analysis. The practical relevance of these topics are demonstrated using examples from the social sciences. Throughout the course, students will make use of statistical software for data handling and model estimation.

Course content

The course includes topics such as:

• Univariate and bivariate descriptive statistics and plots.
• Causation, randomization, sampling, bias and variability.
• Probability models, random variables, characteristics of random variables, and linear combinations of random variables.
• The distribution of the sample mean and the central limit theorem.
• Parameter estimation.
• Inference under exact normality and inference under more realistic conditions.
• Linear regression analysis (incl. One-way ANOVA and ANCOVA).