Магистратура
2022/2023
Анализ данных в социальных науках
Статус:
Курс обязательный (Политика. Экономика. Философия / Politics. Economics. Philosophy)
Направление:
41.04.04. Политология
Кто читает:
Департамент политики и управления
Где читается:
Факультет социальных наук
Когда читается:
1-й курс, 2, 3 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
20
Охват аудитории:
для своего кампуса
Прогр. обучения:
Политика. Экономика. Философия
Язык:
английский
Кредиты:
6
Контактные часы:
64
Course Syllabus
Abstract
The goal of this course is to introduce students the main principles of data analysis methods and procedures commonly used in social science, with an accent on social problems and corresponding scientific challenges. Students are to familiarize with a variety of data analysis methods, which should be useful in quantitative research. It is aimed at developing a data-driven, as well as theory-driven logic through understanding data fundamentals, main application areas. Students will be able to understand limitations, values added and heuristic mechanisms of different data analysis methods. During the course, students will acquire practical skills to be able to gather, generate, visualize and analyze quantitative data in social science research. All the learning process is based on R language and RStudio.
Learning Objectives
- The main goal of the course is to familiarize students with a variety of data analysis methods which should be useful in quantitative research. The course is aimed at developing a datadriven mentality through understanding data fundamentals, as well as areas of application for different analytical methods and approaches. Students should be able to understand the limitations, value added and heuristic mechanisms of different data analysis methods.
Course Contents
- Data analysis: an introduction
- Data sources and databases
- Data visualization
- Random variables: an application of statistics to social science data
- Data Structure and Clustering
- Confidence intervals and hypothesis testing
- Statistical inference: correlation and crosstabulation
- Hidden data structure and Factor Analysis
- Regression models in social sciences
- Network analysis in social sciences: basic concepts
- Quantitative modelling in social sciences
- Text as data in social sciences
- Introduction to R
