2025/2026
Анализ данных
Статус:
Маго-лего
Кто читает:
Департамент социологии
Где читается:
Санкт-Петербургская школа социальных наук
Когда читается:
3 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Зубарев Никита Сергеевич
Язык:
английский
Кредиты:
3
Контактные часы:
32
Course Syllabus
Abstract
The course will discuss methods of data preparation and analysis. Students will become familiar with the principles of critical data analysis, focused on the study of cultural, ethical and socio-technical issues at the intersection of social sciences, computer science and society. The course is aimed at developing students' critical approach to topics such as big data, data ethics, privacy, algorithms for solving social problems using data systems.
Learning Objectives
- Be able to perform statistical analysis of data, as well as solve research and practical problems using various modeling techniques.
Expected Learning Outcomes
- Ability to identify the appropriate data analysis paradigm for a specific study, navigate modern approaches to data analysis, formulate research hypotheses and research objectives, and select appropriate data analysis methods
- Ability to interpret the results of applying linear regression, use linear regression in relevant problems, perform modeling in cases of violation of the assumptions of OLS using GLS and recalculation of standard errors of coefficients.
- Can visualize data and interpret plots, look for patterns in data using visualization, and work with missing values
- Be able to apply classical machine learning methods to solve classification and regression problems
- The student is familiar with basic text processing methods and tokenization techniques. They are able to work with language models and integrate them into their tasks.
Course Contents
- Introduction to Data Analysis
- Exploratory data analysis
- Linear regression
- Classical machine learning methods for classification and regression
- Introduction to NLP
Bibliography
Recommended Core Bibliography
- 9781491981627 - Silge, Julia; Robinson, David - Text Mining with R : A Tidy Approach - 2017 - O'Reilly Media - http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1533983 - nlebk - 1533983
- Applied regression analysis & generalized linear models, Fox, J., 2016
- Discovering statistics using R, Field, A., 2012
- R in action: Data analysis and graphics with R, Kabacoff, R. I., 2015
- Yang, X.-S. (2019). Introduction to Algorithms for Data Mining and Machine Learning. Academic Press.
Recommended Additional Bibliography
- Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.BC6A6457
- Usuelli, M. (2014). R Machine Learning Essentials. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=918191