Practical Course Data Science (Summer 2018)

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Imbox content.png Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.


Workload/ECTS Credits: 180h, 6 ECTS
Module: M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
Lecturer: Prof. Xiaoming Fu
Teaching assistant: MSc. Jiaquan Zhang
Time: Thursday, 14-16
Place: Ifi 2.101
UniVZ [1]

Course Organization

In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

  • Introduction to the practical machine learning pipeline
  • Exploratory data analysis
  • The Python Data Science stack
  • How to deal with unbalanced data
  • Advanced algorithms for Data Science (an overview of competition winning algorithms)
  • Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester. Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (September 30).


  • You are highly recommended to have completed a course on Data Science (e.g., "Data Science and Big Data Analytics" taught by Dr. Steffen Herbold or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
  • Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries


When? What?
12.04.2018 Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
19.04.2018 Lecture 2: The Python Data Science Stack
03.05.2018 Task 1: Intermediate meeting
17.05.2018 Task 1: Presentation of Exemplary Solution // Task 2: Release
24.05.2018 Lecture 3: Advanced Algorithms for Data Science
31.05.2018 Lecture 4: Evaluation and Tuning of Models
07.06.2018 Task 2: Intermediate meeting
14.06.2018 Task 2: Presentations // Task 3: Release
21.06.2018 No lecture
28.06.2018 Task 3: Intermediate meeting I
05.07.2018 No lecture
12.07.2018 Task 3: Intermediate meeting II
08.08.2018-22.08.2018 Task 3: Presentations
30.09.2018 Final Report deadline