Syllabus

Title
1533 Data Mining and Decision Support Systems
Instructors
PD Nils Löhndorf, Ph.D.
Contact details
nils.loehndorf@wu.ac.at
Type
PI
Weekly hours
3
Language of instruction
Englisch
Registration
09/01/17 to 10/01/17
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Data Mining and Decision Support Systems
Dates
Day Date Time Room
Wednesday 10/11/17 09:00 AM - 01:45 PM D2.0.025 Workstation-Raum
Wednesday 10/18/17 09:00 AM - 01:45 PM D2.0.025 Workstation-Raum
Wednesday 10/25/17 09:00 AM - 01:45 PM D2.0.025 Workstation-Raum
Thursday 11/02/17 02:00 PM - 06:00 PM D2.0.025 Workstation-Raum
Wednesday 11/08/17 09:00 AM - 01:45 PM D2.0.025 Workstation-Raum
Wednesday 11/15/17 09:00 AM - 01:45 PM D2.0.025 Workstation-Raum
Wednesday 11/22/17 09:00 AM - 01:45 PM D2.0.025 Workstation-Raum
Wednesday 11/29/17 09:00 AM - 01:45 PM D2.0.030
Wednesday 12/06/17 11:30 AM - 01:30 PM D2.0.030

On this page:

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Contents
The course provides an introduction into data mining and decision support and provides an overview of basic concepts and methods in classification, regression, and unsupervised learning. Additionally, students will get some hands-on experience with widely used Python data mining libraries, such as Pandas, Scikit-Learn, and Statmodels.
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Learning outcomes

After completing the course, students will know how to handle a number of basic data mining methods and how to apply these methods to real world data sets in Python. 

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Teaching/learning method(s)

Each session combines classic lectures with interactive examples, which provides students with an immediate hands-on experience of data mining and machine learning methods. Students are required to prepare reading material for each lecture and complete homework exercises after each session. All student will be given access to an online Jupyter web notebook which is used in class and must be used to complete the homework assignments. Alongside the course, students will work in groups on a data mining project of their choice, for which separate coaching sessions will be offered and which will be presented to class for peer discussion at the end of the course. 

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Assessment
  • Exercises: 30%
  • Group Project: 30%
  • Final exam: 40%
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Prerequisites for participation and waiting lists
Basic programming skills, statistics and linear algebra.
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Readings
1 Author: James, Witten, Hastie, Tibshirani
Title: An Introduction to Statistical Learning

Publisher: Springer
Edition: 1st ed
Year: 2013
Content relevant for class examination: Yes
Recommendation: Essential reading for all students
Type: Book
2 Author: Hastie, Tibshirani, Friedman
Title: The Elements of Statistical Learning

Publisher: Springer
Edition: 2nd edition
Year: 2008
Content relevant for class examination: Yes
Recommendation: Reference literature
Type: Book
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Availability of lecturer(s)
nils.loehndorf@wu.ac.at
Last edited: 2017-05-05



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