Welcome to the webpage for the Data Science course, taught at GI INSA in 2017.
Schedule Please check the schedule weekly, to find the uploaded material and other information.
|#||Date||Lecture||TP (Salle Blueberry)||Material|
||Lecture 01 Slides (pdf)|
||Lecture 02 Slides (pdf)|
||Lecture 03 Slides (pdf)|
||Lecture 04 Slides (pdf)|
||Lecture 05 Slides (pdf)|
||Lecture 06 Slides (pdf)|
Evaluation There will be one exam at the end, with closed books. Duration: 1h50.
Prerequisites Familiarity with: probabilities and statistics; programming experience in a modern language (we’ll use python, but experience in java, c++, R, or scala should be enough).
Textbooks There is no required textbook for the course. However, you encouraged to consult at least one of the following textbooks if you need more information on a subject.
- Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Springer series in statistics, Second edition, 2008.
- Bishop, Christopher M. Pattern recognition and machine learning. Springer, 2006.
- Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian data analysis. Boca Raton, FL: CRC press; 2014.
By email: firstname.lastname@example.org
Use this format for your subject: ‘dsc17 [your topic here]’
Office Hours Mondays 08h30-10h00. Please book appointment by email, first.
Material for ‘Data Science’ Course at GI-INSA, 2017 by Michael Mathioudakis is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License