Information on individual educational components (ECTS-Course descriptions) per semester

Introduction to Programming (Python)

Basics of statistics

• Introduction to data-driven company
• Data analytics lifecycle models: e.g. CRISP, ML Canvas
• Supervised learning: e.g. regression (Multivariate Linear Regression), classification (logistic regression, Decision Trees), k-Nearest Neighbour algorithm (kNN)
• Validation methods: e.g. Cross Validation
• Unsupervised learning: e.g. clustering (k-Means), Anomaly detection (Isolation forests)
• Overview of AI methods and tools (NN, CNN, RNN, chatGPT) 

Companies collect data resulting from the execution of business processes, for example data from production or sales. This data contains information that drives business decisions. Statistical methods enable data to be processed, analyzed, and presented understandably to people, such that relevant information can be obtained. For example, production process data can contain information about potential problems (deviations of processing times from the "norm").

The students understand the value of data within an organization and are familiar with the basic requirements for a data-driven organization. The students can name methods and tools for project management of data analysis projects and apply them appropriately. The students can identify and explain possible challenges when it comes to empirical data analysis, and they can suggest potential corrective action if necessary (i.e. in case of data quality issues). The students are able to select appropriate methods for a simple multivariate data analysis and apply them accordingly. The students can provide a rough overview of AI methods and tools and state when each should be used.

Based on the requirements of a task and the properties of the methods they are able to select and implement suitable methods and can solve simple tasks with the help of the programming language Python and interpret the results.

Interactive course with lecture and exercises

Final Exam

None

Backhaus, Klaus u.a. (2015): Multivariate Analysemethoden: Eine anwendungsorientierte Einführung. 14., überarb. u. aktualisierte Aufl. 2016 edition. Berlin Heidelberg: Springer Gabler.

Fahrmeir, Ludwig u.a. (2016): Statistik: Der Weg zur Datenanalyse. 8., überarb. u. erg. Aufl. 2016 edition. Berlin Heidelberg: Springer Spektrum.

Grus, Joel (2015): Data Science from Scratch. 1 edition. Beijing: O’Reilly and Associates.

Guido, Sarah (2016): Introduction to Machine Learning with Python: A Guide for Data Scientists. 1. Aufl. Sebastopol, CA: O’Reilly UK Ltd.

Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2017): The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. 2nd ed. 2009, Corr. 9th printing 2017 edition. New York, NY: Springer.

Python Software Foundation (o. J.): Python. Online im Internet: URL: https://www.python.org/ (Zugriff am: 21.05.2018).

Scikit-learn (o. J.): Machine learning in Python — Scikit-learn Documentation. Online im Internet: URL: http://scikit-learn.org/stable/ (Zugriff am: 06.09.2018).

Services, EMC Education (2015): Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. 1 edition. Indianapolis, Ind: Wiley.

Classroom-based course.