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Regular version of the site
Master 2020/2021

Statistics with R Specialization

Area of studies: Fundamental and Applied Linguistics
Delivered by: School of Linguistics
When: 2 year, 3 module
Mode of studies: distance learning
Master’s programme: Linguistic Theory and Language Description
Language: English
ECTS credits: 3

Course Syllabus

Abstract

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
Learning Objectives

Learning Objectives

  • learn to analyze and visualize data in R
  • learn to perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions
Expected Learning Outcomes

Expected Learning Outcomes

  • understands Bayesian Statistics
  • understands linear regression
Course Contents

Course Contents

  • Statistics with R
    In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
Assessment Elements

Assessment Elements

  • non-blocking Online course
  • non-blocking Discussion with a HSE instructor
  • non-blocking Online course
  • non-blocking Discussion with a HSE instructor
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    Рассчет оценки в соответствии с баллами, набранными на онлайн-платформе: 95-100 %: 10; 85-94 %: 9; 75-84 %: 8; 65-74 %: 7; 55-64 %: 6; 45-54 %: 5; 35-44 %: 4; 25-34 %: 3; 15-24 %: 2; 5-14 %: 1; < 4 %: 0;
Bibliography

Bibliography

Recommended Core Bibliography

  • Мастицкий С.Э., Шитиков В.К. - Статистический анализ и визуализация данных с помощью R - Издательство "ДМК Пресс" - 2015 - 496с. - ISBN: 978-5-97060-301-7 - Текст электронный // ЭБС ЛАНЬ - URL: https://e.lanbook.com/book/73072

Recommended Additional Bibliography

  • Джеймс Г., Уиттон Д., Хасти Т. - Введение в статистическое обучение с примерами на языке R - Издательство "ДМК Пресс" - 2017 - 456с. - ISBN: 978-5-97060-495-3 - Текст электронный // ЭБС ЛАНЬ - URL: https://e.lanbook.com/book/93580