Master 2024/2025

Modern Methods of Data Analysis

Type: Elective course (Data Science)

Area of studies: Applied Mathematics and Informatics

Delivered by: School of Data Analysis and Artificial Intelligence

Where: Faculty of Computer Science

When: 1 year, 3, 4 module

Mode of studies: offline

Open to: students of all HSE University campuses

Instructors: Dmitry I. Ignatov

Master’s programme: Data Science

Language: English

ECTS credits: 6

Course Syllabus

Abstract

This is a course in basic methods for modern Data Analysis. Its contents are heavily influenced by the idea that data analysis should help in enhancing and augmenting knowledge of the domain as represented by the concepts and statements of relation between them. This view distinguishes the subject from related courses such as applied statistics, machine learning, data mining, etc. Two main pathways for data analysis are: (1) summarization, for developing and augmenting concepts, and (2) correlation, for enhancing and establishing relations. Visualization, in this context, is a way of presenting results in a cognitively comfortable way. The term summarization is understood quite broadly here to embrace not only simple summaries like totals and means, but also more complex summaries: the principal components of a set of features and cluster structures in a set of entities. Similarly, correlation here covers both bivariate and multivariate relations between input and target features including classification trees and Bayes classifiers. Another feature of the class is that its main thrust is to give an in-depth understanding of a few basic techniques rather than to cover a broad spectrum of approaches developed so far. Most of the described methods fall under the same least-squares paradigm for mapping an “idealized” structure to the data. This allows me to bring forward a number of mathematically derived relations between methods that are usually overlooked.