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Technische Universit├Ąt BerlinIntroduction to Data Science with Python

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Introduction to Data Science with Python

* Course fully booked! *

Summer University Term 4: August 19th - August 30th, 2019


Course price: 950 Euros

22 hours of class per week, 3 ECTS credit points, max. 18 participants


This practice-oriented course is ideal for beginners who are looking to be quickly introduced to data science. At its end, you will be equipped with the toolset needed to analyze, understand and gain new insights from data.

You will learn how to use Python coupled with Jupyter to manipulate and visualize data. You will peek into the theoretical foundation of machine learning and understand its main algorithms. Furthermore, you will apply your knowledge on real world problems by using machine learning to classify and predict data.

Learning Goals and Syllabus


At the end of this course you will be able to:

  • Code in Python
  • Manipulate and visualize data using numpy, pandas, matplotlib and sci-kit learn
  • Run exploratory analysis on data and gain new insights
  • Understand the theoretical foundation of machine learning
  • Apply machine learning to predict and classify data
  • Understand and apply Linear regression, K-Means Clustering, PCA, Decision Trees and Neural Networks


Please find the syllabus for this class HERE


Course components

The course will include the following topics:

  • Crash Course in Python
  • Manipulating and visualizing data in Jupyter
  • Introduction to Machine Learning
  • Regression
  • Classification
  • Neural Networks
  • Data representation and Model evaluation


The general prerequisites of the TU Berlin Summer University are the following: at least one year of university experience + English level B2 or equivalent.

Basic programming knowledge is also required for this course. Students should be able to write and run small programs in the language of their choice. Students should also have basic knowledge in linear algebra and statistics/probability theory and know what loops, conditionals, methods/functions, libraries, vectors, matrices, gradient and probability distributions are.


Mahmoud Mabrouk graduated in 2012 from the TU Berlin with a Master in Computer Science. He specializes in applying machine learning to solve biological problems as well as developing new algorithms for protein folding.

Since 2013 he has been working in the Robotics and Biology Laboratory at the department of Computer Engineering and Microelectronics at the TU Berlin. He has significant experience in both research and teaching, and is involved in teaching at both the undergraduate and graduate level at the TU Berlin.

Please direct questions about the course to the TU Berlin Summer University Team at: . We will answer your questions and direct specific queries regarding course content to the course lecturers where necessary.


Registration for Term 4 is now closed. Check out our program for Winter 2020!


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