Course Units
Go through a brute force introduction into R, R Markdown, the RStudio IDE, version management with Git and GitHub’s classroom functionality to get ready for solving the upcoming assignment problems and submitting your solutions.
02 First Things SecondLook closer at data sets and data types before focusing on the most important features of programming languages, namely run-time control and loop structures.
03 Look at Your DataBecome familiar with reading and writing data, computing summary statistics and visual data exploration as the basics of data analysis.
04 Clean Your DataCheck the integrity of datasets and clean them up to ensure that the data basis for your analysis is consistent.
05 Describe Your Linear DataCompute simple statistical linear regression models that relate a dependent to an independent variable.
06 Predict Your Linear DataCompute simple linear models to predict dependent data and assess the performance by independent test samples.
07 Select Your VariablesEvaluate the importance of your independent variables and select an optimal subset for your prediction model.
08 Tune Your ModelsEvaluate model tuning strategies and find optimal settings for your prediction model.
09 Predict Time SeriesLook into some specific characteristics of time series data and predict future observations based on past dynamics.
10 Analyse Time SeriesAnalyse your time series data and decompose it into seasonal characteristics and long-term trends.
11 MOHAFollow the link to start the Marburg Open Hackathon (MOHA)
12 Publication quality graphicsVisualize your data, get some hints for publication quality graphics, and learn about some packages specifically made for visualizations.