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Professur Numerische Mathematik
Professur Numerische Mathematik
Professur Numerische Mathematik 

Introduction to Data Science (4V, 2Ü) Prof. Ernst, WS 2019/20

Content

Tentative list of course topics:
  • Introduction: What is Data Science
  • Learning Theory
  • Regression
  • Classification
  • Clustering and Tree-Based Methods
  • Unsupervised Learning

Notices

Room Change On Thursday 5 December 2019 instead of the class lecture there will be an extra lab tutorial from 13:45 to 15:15 in the computer room (738, Rh39/41).
Cancellations There will be no DS lectures during the week of November 11-15. On Thursday, November 14, Jan Blechschmidt will hold an additional Q & A session on Python programming and other lab-related issues in the computer room (738, Rh39/41).
First Class Monday, October 14, 2019.
First Exercise Lab Monday, October 14, 2019.

Listing of this course in the electronic Vorlesungsverzeichnis (course directory):

Nummer Name Zeit Raum Details
220000-C60
[Vorlesung]
Beginn der LV 15.15 Uhr !
Mittwoch (Wöchentlich)
15:30-17:00
C22.202
(alt: 2/B202)
220000-C60A
[Vorlesung]
Dienstag (Wöchentlich)
13:45-15:15
C25.015
(alt: 2/W015)
220000-C61
[Übung]
Donnerstag (Wöchentlich)
13:45-15:15
C46.738
(alt: 2/39/738)

Lecture

Literature

  • James, Witten, Hastie & Tibshirani. An Introduction to Statistical Learning – with Applications in R. Springer 2013. Available online by follow this link.
  • Here's a continually updated annotated reading list for the course (06.02.2020).

Slides

Exercises

The exercise labs take place in the computer pool of the Mathematics Computing Service. To carry out the programming tasks, everybody needs a valid login which can be picked up at Margit Matt's office (Rh 39/room 704). All programming tasks can be carried out on our Jupyter Hub or on your own laptop. The Jupyter Hub can be accessed inside the university network or via VPN.

Problem sheets

You can see the feedback of your homework by following this link.

Installation

If you want to install your own Jupyter environment, we suggest the following procedure: Get the latest version of miniconda from this page and follow the steps in the installation dialogue. Create a new environment via
conda create --name ds2019
Activate the ds2019 environment via
conda activate ds2019
or (depending on your system)
source activate ds2019
and install the required packages (this list is continually updated)
conda install -c conda-forge jupyter numpy scipy pandas matplotlib
Please refer to Conda (Installation) and Conda (Managing environments) for further information.