• A
  • A
  • A
  • АБB
  • АБB
  • АБB
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта
Магистратура 2023/2024

Основы программирования в R и Python

Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Статус: Курс обязательный
Направление: 01.04.02. Прикладная математика и информатика
Когда читается: 1-й курс, 1 модуль
Формат изучения: без онлайн-курса
Охват аудитории: для своего кампуса
Прогр. обучения: Прикладная статистика с методами сетевого анализа
Язык: английский
Кредиты: 3
Контактные часы: 28

Course 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

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

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

Course Contents

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

Assessment Elements

  • blocking Project
  • blocking Final project
Interim Assessment

Interim Assessment

  • 2023/2024 1st module
    0.6 * Final project + 0.4 * Project
Bibliography

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.