Advanced Practical Course Data Science (Summer 2019): Difference between revisions

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|credits=180h, 6 ECTS
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Xiaoming Fu]
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://134.76.18.81/?q=people/dr-yali-yuan Dr. Yali Yuan]
|ta=[http://www.net.informatik.uni-goettingen.de/people/jiaquan_zhang MSc. Jiaquan Zhang]
|ta=[http://www.net.informatik.uni-goettingen.de/people/jiaquan_zhang MSc. Jiaquan Zhang]
|time=Thursday, 16-18  
|time=Thursday, 16-18  

Revision as of 16:29, 16 April 2019

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
Time: Thursday, 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?
18.04.2019(15:00 to 16:00 for first lecture) Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
25.04.2019 Lecture 2: The Python Data Science Stack
02.05.2019 NO LECTURE (Holiday)
09.05.2019 Task 1: Intermediate meeting
16.05.2019 Task 1: Presentation of Exemplary Solution // Task 2: Release
23.05.2019 Lecture 3: Advanced Algorithms for Data Science
30.05.2019 NO LECTURE (PUBLIC HOLIDAY)
06.06.2019 Lecture 4: Evaluation and Tuning of Models
13.06.2019 Task 2: Intermediate meeting
20.06.2019 No lecture
27.06.2019 Task 2: Presentations // Task 3: Release
04.07.2019 Task 3: Intermediate meeting I
11.07.2019 No lecture
18.07.2019 Task 3: Intermediate meeting II
08.08.2019-22.08.2019 Task 3: Presentations
30.09.2019 Final Report deadline
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