Basic Tools for Data Analysis

2026/2027

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Political Science and International Affairs

Course type:

Elective course

When:

1 year, 2 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
• Functions and Code Modularity
• Data Visualization
• Working with Packages and Reproducible Workflows
• Conditional Statements and Loops

Assessment Elements

• 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.
• Class Participation
  Actively engage in discussions and in-class exercises. Participation reflects attentiveness, contribution to group activities, and willingness to ask and answer questions.

Interim Assessment

• 2026/2027 4th module
  0.3 * Class Participation + 0.3 * Homework Assignments + 0.4 * Final Test

Bibliography

Recommended Core Bibliography

• Bayramova, A., & Edwards, D.J. (2026). A study of objectivity and subjectivity in research methodology selection. Quality & Quantity, 60(2), 5905-5931. https://doi.org/10.1007/s11135-025-02517-9

Recommended Additional Bibliography

• Paul, Justin and Kaurav, Rahul Pratap Singh. Research Methods in a Nutshell: What, Why, When, Where, Who, and How?. International Journal of Consumer Studies 49, no.6 (Nov.2025): n/a-n/a. doi:10.1111/ijcs.70134.

Authors

• ARKATOV DMITRII ALEKSANDROVICH