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Course module: 500189-B-6
500189-B-6
Introduction to Data Science
Course info
Course module500189-B-6
Credits (ECTS)6
CategoryBA (Bachelor)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Social and Behavioral Sciences; TSB: Methodology and Statistics; Methodology and Statistics;
Is part of
B Psychology (Dutch)
B Psychology (English)
Minor Applied Advanced Research Methods
Lecturer(s)
Lecturer
dr. K.M. Lang
Other course modules lecturer
Lecturer
dr. I. Schwabe
Other course modules lecturer
Academic year2020
Starting block
BLOK 4
Course mode
Full-time
RemarksCaution: this information is subject to change
Registration openfrom 29/03/2021 up to and including 20/08/2021
Aims
After completing this course:
  1. The student can describe the main principles of a “data science” approach to behavioral and social science research.
  2. The student can compare and contrast the “data science” approach with the traditional statistical paradigms used in behavioral and social science research. 
  3. The student can use principal component analysis and cluster analysis to find structure in complex behavioral and social science data.
  4. The student can use linear regression and logistic regression to build predictive models that generalize to unseen data.
  5. The student can use a trained linear regression or logistic regression model for prediction or classification, respectively.
Content
Our society is turning into a data-driven society. According to a May 2013 article in ScienceDaily, “A full 90 percent of all the data in the world has been generated over the last two years.” We can learn a great deal from these massive amounts of data, and a "data science" approach can help us do so. Data science methods are used to derive knowledge from data in academic research, companies, governmental agencies, and any other organization that wants to make data-based decisions. This course offers an introduction to the use of data science methods for social and behavioral science research. Upon completing this course, students will have acquired the skills necessary to apply statistical data science techniques to summarize and visualize complex data, discover patterns, and predict outcomes and trends for unseen data. Topics include prediction, classification, clustering, dimension reduction, shrinkage approaches, and more.

Specifics
During the course, students will complete two group assignments in which they will apply their data science skills to real behavioral and social science data. The assignments will be performed using the open-source statistical software platform R. The course will be completed with a written exam.

This course is compulsory for students of the major Psychological Methods and Data Analysis.

Recommended Prerequisites
Familiarity with basic statistics, in particular linear regression, is assumed.

Required Prerequisites
N/A

Recommended Reading
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: Springer.
 
Course available for exchange students
Available for all levels
Contact person
dr. K.M. Lang
Timetable information
Introduction to Data Science
Written test opportunities
DescriptionTestBlockOpportunityDate
Schriftelijk Tentamen / Written ExamEXAM_01BLOK 4114-06-2021
Schriftelijk Tentamen / Written ExamEXAM_01BLOK 4212-07-2021
Written test opportunities (HIST)
DescriptionTestBlockOpportunityDate
Required materials
-
Recommended materials
Literature
An electronic copy of ISL is freely available here: http://www-bcf.usc.edu/~gareth/ISL/index.html
ISBN:978-1-4614-7137-0
Title:An Introduction to Statistical Learning
Author:Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani
Publisher:Springer
Edition:1
Tests
Group Assignment 1

Group Assignment 2

Written Exam

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