Кто читает:: Международная лаборатория прикладного сетевого анализа

Статус:: Курс обязательный

Когда читается:: 1-й курс, 1 модуль

Преподаватели

Капустина Лика Владимировна

Семенова Анна Михайловна

Full Syllabus

Abstract

Students who have never programmed are afraid that it is difficult. This course is designed to introduce them to the basics of programming languages such as R and Python. This course will discuss the difference between these languages, the strengths of each of them. Students will learn the basics of programming and working with these languages.

Learning Objectives

to provide students with the basic R and Python skills that will be required in other courses in the programme

Expected Learning Outcomes

be able to create and work with vectors, matrices and lists
be able to upload files to R space
be able to visualize data
have skills on performing descriptive statistics, exploratory data analysis
know how to build simple and basic models

Course Contents

Data formats
Starting working with data
Exploratory data analysis
Visualization
Basic linear regression
R Basics

Assessment Elements

Quizzes
Homework assignments
Final project

Interim Assessment

2024/2025 1st module
0.5 * Final project + 0.2 * Homework assignments + 0.3 * Quizzes

Bibliography

Recommended Core Bibliography

W. N. Venables, & D. M. Smith. (2012). D.M.: An Introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics Version 2.15.0. R-project.org.

Recommended Additional Bibliography

Simon N. Wood. (2017). Generalized Additive Models : An Introduction with R, Second Edition: Vol. Second edition. Chapman and Hall/CRC.

Authors

Павлова Ирина Анатольевна
SEMENOVA ANNA MIKHAYLOVNA
Капустина Лика Владимировна

Магистерская программа «Аналитика данных и прикладная статистика / Data Analytics and Social Statistics»

Programming in R and Python