Syllabus

Title
1952 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
09/28/15 to 10/06/15
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Data Mining and Decision Support Systems
Dates
Day Date Time Room
Wednesday 10/07/15 09:00 AM - 11:30 AM D2.0.392
Wednesday 10/07/15 12:00 PM - 01:45 PM LC.-1.038
Wednesday 10/14/15 09:00 AM - 11:30 AM D2.0.392
Wednesday 10/14/15 11:30 AM - 01:45 PM D2.-1.019 Workstation-Raum
Wednesday 10/21/15 09:00 AM - 11:30 AM TC.4.14
Wednesday 10/21/15 12:00 PM - 01:45 PM LC.-1.038
Wednesday 10/28/15 09:00 AM - 11:30 AM D2.0.382
Wednesday 10/28/15 12:00 PM - 01:45 PM LC.-1.038
Wednesday 11/11/15 09:00 AM - 11:30 AM D2.0.030
Wednesday 11/11/15 12:30 PM - 01:45 PM LC.-1.038
Wednesday 11/18/15 09:00 AM - 11:30 AM D2.0.392
Wednesday 11/18/15 12:00 PM - 01:45 PM TC.-1.61
Wednesday 11/25/15 09:00 AM - 11:30 AM D2.0.392
Wednesday 11/25/15 12:00 PM - 01:45 PM TC.-1.61
Wednesday 12/02/15 09:00 AM - 11:30 AM D2.0.030
Wednesday 12/02/15 12:00 PM - 01:45 PM TC.-1.61
Wednesday 12/09/15 09:00 AM - 11:30 AM D3.0.225

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)
Theory will be covered in a classic lecture combined with some interactive examples. Students are required to prepare reading material for each lecture. For the exercise unit, each student will be given access to an online iPython web notebook which is used in class and must be used to complete the homework assignments. After the last session, groups of three students will be assigned a project where students apply an appropriate data mining method to a real world data set. 
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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: Essential reading for all students
Type: Book
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Availability of lecturer(s)
nils.loehndorf@wu.ac.at
Last edited: 2015-04-14



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