Online Material for the Data Analytics I course
This repository contains additional material for the Data Analytics I course. The material is created for educational purposes and serves students to get familiar with the R code and visualization of results.
Course content:
The course aims to explain the difference between causal and predictive modeling and introduces some of the widely used predictive modeling methods and their core principles. The lectures start with the basic concepts in causal and predictive modeling to underline different goals in each approach. The statistical theory is based on a linear regression model which is broadly used in the applied research. The theoretical results further deepen the understanding of what the differences between causal and predictive modeling are. During the rest of the course, several predictive methods are discussed. Additionally, strategies how to obtain the best predictive model including resampling methods such as cross‑validation are overviewed.
Course outline:
- Basic Econometrics Concepts
- Properties of Linear Estimators and Predictions
- Prediction vs. Causal Inference (Linear Regression)
- Supervised Machine Learning Methods