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Обычная версия сайта

2022/2023
Учебный год
ENG
Обучение ведется на английском языке
6
Кредиты
Статус:
Курс обязательный
Когда читается:
2-й курс, 1, 2 модуль

#### Преподаватели

Пестова Алена Сергеевна

### 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

• Practical Assignments
For each topic, there will be a practical assignment. Students have to complete it either at class or as a homework. Each task wil be graded as 1 (done) or 0 (not done). Maximum grade for this part is 8, if all the tasks are completed.
• Project 1
This project is assigned at the end of the first module. Students have to demonstrate their abilities to detect potential data problems and fix these problems. Two elements of grading are correct coding and correct interpretation.
• Project 2
The project is assigned at the end of the second module. Students have to demonstrate their skills to implement a causal inference method and to rationalize their choice of the method.
• Exam
Exam is conducted in a form of take-home project. Students have to apply a set of methods studied during the course to get an answer for the given research question. Students have 48 hours to individually prepare and submit the paper

#### Interim Assessment

• 2022/2023 2nd module
0.2 * Practical Assignments + 0.3 * Exam + 0.25 * Project 2 + 0.25 * Project 1

#### 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