Data Analysis: Advanced Level

2026/2027

Учебный год

ENG

Обучение ведется на английском языке

6

Кредиты

Кто читает:

Департамент социологии

Статус:

Курс обязательный

Когда читается:

2-й курс, 1, 2 модуль

Преподаватель

Ткач Сергей

Course Syllabus

Abstract

The course is continuation of the Data Analysis course. This course prepares students to gather, describe, and analyze data using advanced statistical tools. Topics of the first module include advanced data manipulation, treatment of missing data, survival analysis and choice modeling. The second module is centered around issues of causality and causal inference. The course consists of lectures by the instructor, practical assignments for each class, and two individual projects. Grading is based on practical assignments, two projects and final exam

Learning Objectives

• This course aims to provide an overview of advanced statistical techniques that arise in data analytic applications. In this class, you will learn and practice advanced data analytic techniques. One or more practical applications associated with each technique will also be discussed.

Expected Learning Outcomes

• At the end of the course, the students should be able to: - identify properties of the data, detect potential problems - treat data problems: sample bias, missings, excessively small/large samples
• - be able to conduct time-series analysis and analysis of choice
• - Define causal effects using potential outcomes
• - Implement causal inference methods (matching, instrumental variables, regression discontinuity, difference-in-difference, fixed effects) - Identify which causal assumptions are necessary for each type of statistical method
• - Express assumptions with causal graphs

Course Contents

• 1. Data problems: what can be wrong?
• 2. Correction of sample bias.
• 3. Missing data treatment.
• 4. Survival analysis
• 5. Choice modeling
• 6. Basics of causal inference
• 7. Causal Diagrams.
• 8. Statistical instruments for causal inference.

Assessment Elements

• Homework
  Students receive homework for each topic studied . The assignment is assessed on a 10- point scale The assignment must be completed within one week . In case of late submission, the penalty is 1 point for each week of delay.
• Project 2
  The exam is conducted in the form of independent homework—an ongoing project . Assignments submitted after the deadline are penalized by 1 point for each day late.
• Project 1
  Students will be required to demonstrate their skills in applying cause and effect inference methods and justify their choice of method.

Interim Assessment

• 2026/2027 2nd module
  Projet 1* 0.200 + Homework: Practical work * 0.600 + Project 2 * 0.200

Bibliography

Recommended Core Bibliography

• 9780205849574 - Barbara G. Tabachnick; Linda S. Fidell - Using Multivariate Statistics, 6th Edition - 2013 - Pearson - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1418064 - nlebk - 1418064
• 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

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.

Authors

• Ivaniushina Valeriia Aleksandrovna