Case Study in Data Science

2019-2020

Course Objective

To build on the knowledge of data science methods and the effective
communication of results from big data analyses into written reports.

Course Content

The rapid growth of digitalized data and the computer power available to
analyze it has created immense opportunities for econometrics,
statistics and machine learning. This course treats advanced data
science methods. Topics include cross validation, regression trees,
random forest, support vector machines, boosting and bagging. The course
emphasizes on the foundations of the methods, the selection of
appropriate methods and justification of choice, and use of programming
for implementation of the method.

Teaching Methods

Each week, 4 hours lectures and 2 hours tutorials.

Method of Assessment

Written Exam plus Assignments.

Recommended background knowledge

Courses in Statistics and Econometrics.

General Information

Course Code E_EORM_CSDS
Credits 6 EC
Period P3
Course Level 400
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator dr. ing. S.A. Fries
Examiner dr. ing. S.A. Fries
Teaching Staff

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

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