Credit: Anna Logue
Data Mining (FSS 2018)
The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:
- Goals and Principles of Data Mining
- Data Representation and Preprocessing
- Clustering
- Classification
- Regression
- Association Analysis
- Text Mining
- Systems and Applications (e.g. Retail, Finance, Web Analysis)
The course consists of a lecture together with accompanying practical exercises as well as student team projects. In the exercises the participants will gather initial expertise in applying state of the art data mining tools on realistic data sets. The team projects take place in the last third of the term. Within the projects, students realize more sophisticated data mining projects of personal choice and report about the results of their projects in the form of a written report as well as an oral presentation.
Exam Review
The review for the first and second exam will take place on Friday, 28 September, at 13:00 in Room B6 C101.
Time and Location
- Lecture: Wednesday, 10.15 – 11.45, Room A5, B144
- Exercise 1: Thursday, 10.15 – 11.45, Room B6, A1.04 (RapidMiner)
- Exercise 2: Thursday, 12.00 – 13.30, Room B6, A1.04 (Python)
- Exercise 3: Thursday, 13.45 – 15.15, Room B6, A1.04 (RapidMiner)
Note: there are three parallel exercise groups, you are supposed to only attend one.
Final exam
- 60 % written exam
- 40 % project work
Contact Person
- If you have any questions, please contact Oliver Lehmberg (oli(at)informatik.uni-mannheim.de).
Registration
- The course is open to students of the Master Business Informatics, Lehramt Informatik and Mannheim Master in Data Science (MMDS).
- The course is restricted to 80 participants.
- Registration will be opened Wednesday, 7 February 2018, 10:15 am.
- Registration is done via ILIAS using this link (once the registration is open)
- Allocation of places is done by FCFS (limit 80 students)
- We offer three alternative times (Thursdays 12.00, 13.45 and 15.30) for the exercise session. Sign-In to one of the three groups within ILIAS after you have registered for the course. The groups are restricted to 30 students each.
Outline
Week Wednesday Thursday 14.02.2018 Introduction to Data Mining Introduction to RapidMiner/ Python 21.02.2018 Lecture Clustering Exercise Clustering 28.02.2018 Lecture Classification 1 Exercise Classification 07.03.2018 Lecture Classification 2 Exercise Classification 14.03.2018 Lecture Classification 3 Exercise Classification 21.03.2018 Lecture Regression Exercise Regression – Easter Break - 11.04.2018 Lecture Text Mining Exercise Text Mining 18.04.2018 Introduction to Student Projects
and Group Formation (Attendance obliatory)Preparation of Project Outlines 25.04.2018 Lecture Association Analysis Exercise Association Analysis 02.05.2018 Project Work Feedback on demand 09.05.2018 Project Work Feedback on demand 16.05.2018 Project Work Feedback on demand 21.05.2018 Project Work Submission of project results 24.05.2018 - Presentation of project results Literature
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson.
- Vijay Kotu, Bala Deshpande: Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner. Morgan Kaufmann.
Software
Videos and Screen Casts
Video recordings of the Data Mining I lectures and screen casts of the exercises are available here.