DSS
Decision Sciences & Systems
Technical University of Munich
 

 

Paul Sutterer M.Sc., Dr. Paul Karänke

Tutorials in WS 19/20

Business Analytics (IN2028)

Back to the lectures info

Latest announcements:

Registration for the tutorial groups starts on 14.10.2019 at 2pm

The registration period ends on 17.10.2019 at 1pm

Students can register for the different tutorial groups by submitting their preferences to the Matching-System. The corresponding link is published in the moodle course.
  
The outcome of the matching will be announced on the morning of the 18.10.2019
 
Information on the matching system can be found here:
 
 

Dates and general information:

Tutorial groups:

  • Group 1 / Mo 14:00 - 16:00
  • Group 2 / Mo 16:00 - 18:00
  • Group 3 / Tu 10:00 - 12:00 [No Tutorial on 12.11.]
  • Group 4 / Tu 14:00 - 16:00
  • Group 5 / We 08:30 - 10:30 [Tutorial on 30.10. moved to 28.10. 08:00 - 10:00 am!]
  • Group 6 / We 10:30 - 12:30 [Tutorial on 30.10. moved to 28.10. 10:00 - 12:00 am!]
  • Group 7 / Thu 14:00 - 16:00
  • Group 8 / Thu 16:00 - 18:00
  • Group 9 / Fr 12:30 - 14:30 [No Tutorial on 01.11.]
  • Group 10 / Fr 14:30 - 16:30 [No Tutorial on 01.11.]
 
Tutorial dates:
 
21.10. 25.10. Tutorial 01: R Tutorial
28.10. 31.10. Tutorial 02: Inferential Statistics Revisited
04.11. 08.11. Tutorial 03: Multiple Linear Regression and Panel Data (Fixed and Random Effects)
11.11. 15.11. Tutorial 04: Generalized Linear Models (Logit and Poisson Regression)
18.11. 22.11. Tutorial 05: Naïve Bayes and Bayes Nets
25.11. 29.11. Tutorial 06: Decision Tree Learners
02.12. 06.12. Tutorial 07: Data Preparation
09.12. 13.12. Tutorial 08: Evaluation of Classifiers
16.12. 20.12. Tutorial 09: Clustering
07.01. 10.01.  ---------No Tutorial--------
13.01. 17.01. Extra Tutorial: Analytics Cup - Introduction & Support
20.01. 24.01. Tutorial 10: Principal Component Analysis
27.01. 31.01. Tutorial 11: Association Rule Mining and Recommenders
03.02. 07.02. Tutorial 12: Neural Networks


Regular participation in the exercises and preparation of the work sheets is strongly recommended for the successful exam. Each participant should work on the exercises independently.

Structure of the final grade:

Students are offered to participate in the Analytics Cup. This is a course intern graded project where students get to analyze a realistic data set. If the grade in this project is better than the exam grade, it will be weighted by 33% and the exam by 67%. Therefore, participating students can only improve their grades. For example:

Exam grade (2.0), Analytics Cup grade (2.7) -> Final grade: 2.0

Exam grade (2.0), Analytics Cup grade (1.0) -> Final grade: 0.67*2.0+0.33*1.0=1.67 -> 1.6

The Analytics Cup grade is taken into account up to the subsequent exam and expires in WS19/20. The bonus will be forfeited after one of the exams is failed. Final grade can not be higher than 1.0.

Exam:

General information
Grading You need to pass the final exam to pass the course.The tutorials are not a prerequisite for the final exam, but help you prepare.
You can participate in the data mining cup.
Process
Every exam participants have to bring for the exam a valid identity card/passport and a valid student ID. The Immatrikulationsbescheinigung is not enough.
The exam will be in English.
You can have only non-programmable calculators on the exam.
Registration Registration for the exam is via TUMonline. Be sure to check the application deadlines and registration details for the different courses on the website of Prufüngsamtes.
Results The results of the exam will be announced on TUMonline.
 
 
Final exam
Date t.b.a.
Length 90 min
Room tba
Content
The exam covers the material from lectures and tutorials (also homework).
Klausureinsicht will be announced by e-mail/moodle forum
 
Repeat exam
Date t.b.a.
Length 90 min
Room tba
Content The exam covers the material from lectures and tutorials (also homework).
Klausureinsicht will be announced by e-mail

Paul Sutterer M.Sc. (Teaching Assistant)
Room 01.10.055
Phone: 289-17507
E-Mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Nils Kohring M.Sc. (Teaching Assistant)
Room 01.10.055
Phone: 289-17506
E-Mail: nils.kohringzzin.tum.de

Stefan Heidekrüger, M.Sc. 
Room 01.10.056
E-Mail: stefan(.)heidekrueger(at)in(.)tum(.)de

Dr. Paul Karänke (Teaching Assistant)
Room 01.10.057
Phone: 289-17504
E-Mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

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