Data Science Methods

2019-2020

Course Objective

The course covers all important aspects of data modeling with a number
of methods and techniques. The emphasis is on both theory and practice.
The objective is to help the students to develop a deep understanding of
the methods, recognize their strengths and limitations, and acquire the
practical skills to analyze complex datasets and critically interpret
the results.

Course Content

This course covers:
(1) statistical methods for analyzing data;
(2) machine learning techniques;
(3) efficient implementation of statistical and machine learning
methods;
(4) validity and methodological aspect of data analysis.

Teaching Methods

Lectures and tutorials

Method of Assessment

Exam and group assignment

Entry Requirements

Basic knowledge of probability, statistics and programing.

Literature

Recommended books:
- Friedman, J., Hastie, T., & Tibshirani, R. (2009), The Elements of
Statistical Learning: Data
Mining, Inference, and Prediction (2nd edition). Springer Series in
Statistics.
- Hardle, W. K., Muller, M., Sperlich, S., & Werwatz, A. (2012),
Nonparametric and semipara-
metric models. Springer Science & Business Media.

General Information

Course Code E_EOR2_DSM
Credits 6 EC
Period P5
Course Level 200
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator dr. P. Gorgi
Examiner dr. P. Gorgi
Teaching Staff dr. P. Gorgi

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Study Group
Target audiences

This course is also available as: