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Data Science

Data Science (cf. the Wikipedia definition of data science) describes an attitude towards treating problems with a set of capabilities that is not located in any classic community, but it is a set of capabilities that cross-breed between disciplines, such as physics, biology, social sciences and economics. It uses elaborate computer science paradigms and needs a background in statistics. It feeds the new as well as the classical economy as well as the medical field.  

Data scientists: IT's new rock stars

(Preliminary) Lecturing Schedule

Lecture: TBA

Tutorial: TBA

 

Date Title Lecturer
19.04 Intro to DS Claudia
26.04. Probability Theory and Statistics Claudia
  Exercise: Python stats Claudia
03.05. Hypothesis testing & Descriptive Statistic Claudia
  Exercise: Probability Theory, p-values Claudia
10.05 CI Claudia
  Exercise: Hyp testing Claudia
17.05. Hypothesis testing & Nonparametric Stats Claudia
  Exercise Claudia/CCK
24.05. Bayesian Statistics CCK
  Exercise CCK
31.05. Relationships & Regression Claudia
  Exercise CCK
07.06 (Public holiday) -
14.06. Regression Claudia
  Exercise CCK
21.06. Causality Claudia
  Exercise CCK
28.06. Graphical Models 1 Claudia
  Exercise CCK
05.07. Graphical Models 2 CCK
  Exercise CCK
12.07. Sampling Methods? Inference Methods? CCK
  Exercise CCK
19.07. Visualizations and Telling Data Stories Claudia
  Exercise CCK
26.07. (No lecture) -
02.08. Exam Claudia/CCK

Exercises

The exercises will be done in groups of X students. For taking part in the exam, solutions for all but one exercise have to be submitted. For this, each group will get an own SVN repository.

Programming will be in IPython with IPython notebooks :)

Literature

  1. Vasant Dhar. Data Science and Prediction. In: Communications of the ACM, December 2013, Vol. 56, No. 12, pp. 64-73
  2. Anand Rajaraman, Jeffrey Ullman, Jure Leskovec, Mining of Massive Datasets, Cambridge University Press (free download)
  3. Jeffrey Stanton, Introduction to Data Science (free download)
  4. John Hopcroft. Foundations of Data Science.
  5. http://www.wolframscience.com/thebook.html
  6. Peter Norvig, Alon Halevy, Fernando Parreira. The unreasonable effectiveness of data. In: IEEE Intelligent Systems, March/April 2009.

 

Beteiligte: 

JProf. Dr. Claudia Wagner

clwagner@uni-koblenz.de

Publications:
N/A

Dr. Christoph Kling

ckling@uni-koblenz.de

Publications:
N/A