Advanced Practical Course Data Science (Winter 2019/2020)

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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.


Details

Workload/ECTS Credits: 180h, 6 ECTS
Module: M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
Lecturer: Prof. Xiaoming Fu; Dr. Yali Yuan
Teaching assistant: MSc. Jiaquan Zhang http://www.net.informatik.uni-goettingen.de/?q=people/shuai-xu Shuai Xu
Time: Friday, 16-18
Place: Ifi 2.101
UniVZ link


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).

Prerequisites

  • 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

Schedule

When? What?
25.10.2019 Lecture 1: Introduction & The Data Science Pipeline
01.11.2019 Lecture 2: The Python Data Science Stack - Task 1: Release
08.11.2019 No lecture
15.11.2019 Task 1: Intermediate meeting
22.11.2019 Lecture 3: Advanced Algorithms for Data Science // Task 2: release
29.11.2019 No lecture // Task 1 report submission
06.12.2019 No lecture // Task 3: release
13.12.2019 No lecture // Task 2 report submission
20.12.2019 Lecture 4: Evaluation and Tuning of Models
27.12.2019 Task 3: Intermediate meeting I
03.01.2020 No lecture
10.01.2020 Task 3: Intermediate meeting II
17.01.2020 No lecture
24.01.2020 No lecture
31.01.2020 No lecture // Final slides submission (Only slides)
07.02.2020 Task 3: Presentations (Final Presentation)
31.03.2020 Final Report deadline (Including report and code)
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