After successful completion of this module:

• You are familiar with different types of analysis such as descriptive, explorative or predictive analyses and can name the most important differences. You are able to perform descriptive and explorative data analyses and visualise the results with the help of typical graphical representations.
• You are familiar with various classifications of algorithmic approaches to machine learning, such as the distinction between supervised and unsupervised learning.
• You are able to recognise which kind of algorithms can be used to solve a problem for a specific application.
• You are familiar with the basic methods, techniques and algorithms in the field of statistics and machine learning, such as linear and logistic regression, decision trees, distance-based methods such as k-Nearest Neighbour, Support Vector Machines or Neural Networks.
• You are familiar with the different steps involved in implementing a data science project and have dealt with process models for implementing such projects.
• You are able to implement data analysis and modeling independently, e.g. in the programming language Python, and are able to implement steps for data preprocessing, feature engineering as well as training and testing algorithms prototypically.