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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

 

Second Exam (Nachklausur): 11th of October, 2pm, room M 201

Lecture: 4pm - 5.30pm  E 313

Tutorial: 5.45pm - 7.15 pm  E 313

 

Date Title Lecturer
19.04 Intro to DS Wagner
  Tutorial: Intro & Logistics Wagner
26.04 Descriptive Statistics & Probabilities (Exercise 1 & allbus data & description) Wagner
03.05. Hypothesis testing, p-values & (Exercise 2) Wagner
  Tutorial: Probabilities & descriptive statistics Wagner
10.05 Power of  Test, Effect Sizes (Exercise 3) Wagner
  Tutorial: Hypothesis testing Wagner
17.05. CI, Nonparametric Stats, Likelihood (Exercise 4) Wagner
  Tutorial Wagner
24.05. MLE, MAP and Bayesian Inference (Exercise 5 corrected 30.5.) Kling
  Tutorial Kling
31.05. Relationships & Regression (Exercise 6) Wagner
  Tutorial Kling
07.06 (No lecture) -
14.06. Regression (Exercise 7) Wagner
  Tutorial notebook Kling
21.06. Regression & Causality (Exercise 8) Wagner
  Tutorial notebook Kling
28.06. Causality (Exercise 9) Wagner
  Tutorial Kling
05.07. Graphical Models 1 notebook (Exercise 10) Kling
  Tutorial Kling
12.07. Graphical Models 2 (Exercise 11) Kling
  Tutorial (notebook) Kling
19.07. Advanced Inference Methods (notebook) Kling
  Tutorial (notebook) Kling
26.07. (No lecture) -
02.08. Exam (4pm Room E011) Wagner

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.
  7. topicmodels.info

 

Beteiligte: 

JProf. Dr. Claudia Wagner

clwagner@uni-koblenz.de