Многомерный анализ данных

2026/2027

Статус: Маго-лего

Кто читает: Международная лаборатория прикладного сетевого анализа

Где читается: Факультет социальных наук

Охват аудитории: для своего кампуса

Преподаватели: Оганян Артём Ашотович, Павлова Ирина Анатольевна

Язык: русский

Кредиты: 3

Контактные часы: 28

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Аннотация

This course takes a modern, data-analytic approach to the multivariate data. Multivariate data analysis (MVA) encompasses statistical techniques that are used to analyze several variables at once. The course covers some basic notions of statistics with the development into several domains: cluster analysis, principle component analysis, factor analysis, canonical corelation analysis, discriminant analysis. All the topic of the course are supplemented by the examples of MVA application to different types of data. This course serves as an important prerequisite for the course in structural equation modeling.

Цель освоения дисциплины

The goal of the course is to ensure that students understand topics and principles of applied linear models on an advanced level.
The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.

Планируемые результаты обучения

Have the skill to work with statistical software, required to analyze the data.
Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
Have the skill to meaningfully develop an appropriate model for the research question.
Be able to criticize constructively and determine existing issues with applied linear models in published work.
Be able to explore the advantages and disadvantages of various linear modeling instruments, and demonstrate how they relate to other methods of analysis.
Be able to work with major linear modeling programs, especially R and SAS, so that they can use them and interpret their output.
Have an understanding of advanced methods of linear models and related multivariate extensions.
Know complex methods of aggregating data and dimensionality reduction.
Know innovative, effective methods for presenting the results from statistical investigations of empirical data.
Know new insights into the regression analysis.
Know various modern extensions to the traditional linear model.

Содержание учебной дисциплины

Introduction to multivariate data analysis
Basic statistics
Some basic notations
Graphical representation of multivariate data
Cluster analysis
Principal component analysis
Factor analysis
Canonical correlations
Discriminant analysis

Элементы контроля

Project 1
The first Project assignment deals with basic descriptive data exploration and construction of simple analytical variables. It constitutes 30% of the Final Grade.
Project 2
The second Project assignment deals with conducting complex data reduction and aggregation using the Principle Component Analysis and cluster analysis methods. The grade for this Project constitutes 40% of the Final Grade.
Project 3
The third Project assignment allows students to conduct and evaluate the results of either Factor or Discriminant Analysis. The grade for this Project constitutes 30% of the Final Grade.

Промежуточная аттестация

2026/2027 4th module
0.3 * Project 3 + 0.3 * Project 1 + 0.4 * Project 2

Список литературы

Авторы

Ермолаева Аполлинария Александровна
Павлова Ирина Анатольевна

Программа дисциплины