Basic Tools for Data Analysis

2025/2026

Academic Year

ENG

Instruction in English

Delivered at:

Department of Political Science and International Affairs

Course type:

Elective course

When:

1 year, 1 module

Instructor

Arkatov, Dmitry

Course Syllabus

Abstract

This course introduces students to the fundamental tools and techniques used in data analysis, providing a solid foundation for understanding and interpreting data. Through hands-on activities and practical exercises, participants will learn how to collect, clean, analyze, and visualize data using popular software tools such as Excel, R, and Python.

Learning Objectives

• Develop proficiency in R programming and RStudio, including data manipulation, cleaning, visualization, and application of basic statistical methods to real-world datasets
• Build the ability to write modular, reusable, and well-structured R code, enabling efficient analysis and clear presentation of data insights.

Expected Learning Outcomes

• Demonstrate proficiency in R programming, including working with vectors, lists, matrices, data frames, and factors to manipulate and manage data efficiently.
• Import, clean, and preprocess real-world datasets using R, applying filtering, transformation, and merging techniques to prepare data for analysis.
• Create informative and visually appealing static and interactive plots using base R, ggplot2, and plotly to communicate data insights effectively.
• Develops modular, reusable, and well-structured R code, and applies best practices for project organization and reproducible workflows using RStudio and RMarkdown.

Course Contents

• Introduction to R and RStudio
• Basic Data Structures
• Data Frames and Data Import
• Data Cleaning and Preprocessing
• Conditional Statements and Loops
• Functions and Code Modularity
• Data Visualization
• Working with Packages and Reproducible Workflows

Assessment Elements

• Class Participation
  Actively engage in discussions and in-class exercises. Participation reflects attentiveness, contribution to group activities, and willingness to ask and answer questions.
• Homework Assignments
  Complete practical exercises and projects outside of class. Homework is designed to reinforce programming skills, data manipulation, visualization, and application of concepts covered in classes.
• Final Test
  A comprehensive assessment evaluating understanding of R programming, data handling, visualization, functions, and workflow organization. Includes theoretical questions and practical coding tasks.

Interim Assessment

• 2025/2026 1st module
  0.3 * Class Participation + 0.4 * Final Test + 0.3 * Homework Assignments

Bibliography

Recommended Core Bibliography

• An introduction to R : a programming environment for data analysis and graphics, Venables, W. N., 2009

Recommended Additional Bibliography

• Medeiros, K. (2018). R Programming Fundamentals : Deal with Data Using Various Modeling Techniques. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1904978

Authors

• Arkatov Dmitrii Aleksandrovich