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Regular version of the site

Data Analysis

2021/2022
Academic Year
ENG
Instruction in English
6
ECTS credits
Course type:
Compulsory course
When:
1 year, 1-3 module

Instructors

Course Syllabus

Abstract

Methods for preparing and analyzing data will be discussed during the course.Students will be introduced to the principles of data critical analysis, focused on the study of cultural, ethical and socio-technical problems at the intersection of social sciences, informatics and society.In addition to the methods of analysis, MA students will develop a critical approach to topics such as big data, data science, data ethics, privacy, tracking and analyzing how data systems and algorithms can help solve social problems.
Learning Objectives

Learning Objectives

  • Students will acquire from theoretical and practical standpoints: - a solid foundation in applied statistical methodology - the basic toolkit of quantitative research
Expected Learning Outcomes

Expected Learning Outcomes

  • Able to use modern empirical databases (including foreign ones) in scientific and project activities, independently create databases for the implementation of research and practical tasks
  • Able to analyze empirical data (political, economic and sociological research) using modern qualitative and quantitative methods and using appropriate software
  • Able to build and analyze mathematical models of socio-political systems and processes
  • Able to organize and conduct political analysis of socially significant projects
  • Able to reflect (evaluate and process) mastered scientific methods and methods of activity
  • Able to independently master new research methods, change the scientific and production profile of his activities
  • Able to assess, model and predict socio-political processes at the global, international, national, regional and local levels based on the methodology of theoretical and empirical research
Course Contents

Course Contents

  • Exploratory data analysis
  • Univariate and bivariate analyses (common stat tests)
  • Factor analysis
  • Linear, Logistic, GLM (if we have time), Multi-level - next year if possible
  • Classifiers / Dimensionality reduction
  • Cluster analysis for exploratory analysis
  • Dealing with missing data
Assessment Elements

Assessment Elements

  • non-blocking group project 1
    This project is accomplished after the first module of studies. It aim to examine student's abilities to prepare the data and conduct basic analysis. There are three basic features assessed: correct calculations and correct code (syntax); correct interpretations – students must describe trends properly, assess significance of the results, and predict values of dependent variable correctly; and produce correct graphics, with proper types of plots and formatting applied.
  • non-blocking project 2
    The second project is dedicated to the topic of social and political attitudes research. Students will have to accomplish a predictive study discivering the specific effects of/ on political or social attitudes in a chosen country. There are three basic features assessed: correct calculations and correct code (syntax); correct interpretations – students must describe trends properly, assess significance of the results, and predict values of dependent variable correctly; and produce correct graphics, with proper types of plots and formatting applied.
  • non-blocking exam
    Exam is conducted in the take-home format. Students have to prepare a project containing the analysis which is needed for their term paper. The exam project is a draft of the empirical part of the term paper
  • non-blocking Practical tasks
    After each seminar, students are assigned a practical task which is similar to the taks discussed in class
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.3 * exam + 0.15 * group project 1 + 0.25 * Practical tasks + 0.3 * project 2
Bibliography

Bibliography

Recommended Core Bibliography

  • Bertail, P., Blanke, D., Cornillon, P.-A., & Matzner-Løber, E. (2019). Nonparametric Statistics : 3rd ISNPS, Avignon, France, June 2016. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2044916
  • Crawley, M. J. (2014). Statistics : An Introduction Using R (Vol. Second edition). Chichester, West Sussex, UK: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=846213
  • Tabachnick, B. G., & Fidell, L. S. (2014). Using Multivariate Statistics: Pearson New International Edition (Vol. 6th ed). Harlow, Essex: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418064

Recommended Additional Bibliography

  • Field, A. V. (DE-588)128714581, (DE-627)378310763, (DE-576)186310501, aut. (2012). Discovering statistics using R Andy Field, Jeremy Miles, Zoë Field.