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Data Analysis in Social Science
- Class 30
- Practice 30
- Independent work 90
Total
150
Course title
Data Analysis in Social Science
Lecture type
Elective
Course code
21-02-586
Semester
2
ECTS
5
Course overview
This master'ls level statistics and data analysis course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. It begins with essential notions of probability and statistics and proceeds to cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research.
Minimal learning outcomes
- Demonstrate understanding of reasoning logic in probability and statistical models
- From selected use case, formulate summary and describe data sample
- Demonstrate understanding of selected methods of evaluating social programs
- From selected use case, demonstrate presenatation of results in a compelling and thruthful way
- From selected tool/program language, demonstrate data analysis concepts and techniques
Preferred learning outcomes
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4 semesters (2 years)
Professional Master in Computer Engineering, sub-specialization in Data science
IT SMF – ITIL Foundation
Introduction to Programming Using Python
Tableau Desktop Qualified Associate
Professional Master in Computer Engineering, sub-specialization in Data science