Course Units

How-to Data Analysis

This course uses a blended-learning format. The introductions, explanations, and examples can also support independent study.

01 First Things First

This unit introduces R, R Markdown, RStudio, version control with Git, and Classroom 50 for GitHub assignments. You will use these tools to solve and submit course assignments.

02 First Things Second

Look closer at data types and object types before focusing on the most important features of programming languages, namely operators and control structures.

03 Look at Your Data

Become familiar with reading and writing data, computing summary statistics, and exploring data visually as the basics of data analysis.

04 Clean Your Data

Check, clean and document a dataset so that its observations, values and limitations are clear before analysis.

05 Describe Your Linear Data

Compute simple statistical linear regression models that relate a dependent to an independent variable.

06 Predict Your Linear Data

Compute simple linear models to predict dependent data and assess the performance with independent test samples.

07 Select Your Variables

Evaluate the importance of your independent variables and select an optimal subset for your prediction model.

08 Tune Your Models

Evaluate model tuning strategies and find optimal settings for your prediction model.

09 Predict Time Series

Look into some specific characteristics of time series data and predict future observations based on past dynamics.

10 Analyze Time Series

Analyze your time series data and decompose it into seasonal characteristics and long-term trends.

11 MOHA

Follow the link to start the Marburg Open Hackathon (MOHA).

12 Graphics

Visualize your data, get some hints for publication-quality graphics, and learn about some packages specifically made for visualizations.