Basic Tools for Data Analysis

2024/2025

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, 1 module

Instructor

Zubarev, Nikita

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

• The class aims to introduce students to foundations of exploratory data analysis, vizualization, data wrangling, and basics of statistical inference.

Expected Learning Outcomes

• The ability to create a PowerPoint presentation that will follow current design trends
• Students are familiar with navigating in Excel, filtering, and sorting data. Basic functions: sum, average, count, max, min, Absolute and relative references, Subtotal, if, sumif, Syntaxis of functions
• Students are able to use the following features of excel: AND, OR, NOT, IFS, and nested IF functions; Pivot tables, slices; Vlookup function; Visualise data; Merging tables.
• Students are familiar with syntaxis of R, ways of getting help; notions of objects, vectors, and types of data in R. They can code basic contingency tables. Students can use main function from dplyr.
• Students are able to use main dplyr functions. Students are capable of creating main plot types using ggplot2. Students can calculate main descriptive statistics.
• Students are familiar with open source data such as survey projects (WVS, EVS, ESS, Barometers) and government statistics (Rosstat). Students can use function from the R package rvest to scrape tables and textual data from the web. They can use join function to combine data from different tables.
• Students are familiar with applications of ggplot2 in the context of working with textual data. Students can pre-process and explore a text corpus using basic text statistics and tidytext package.
• Students can identify appropriate data for fitting an OLS model. Students can check statistical assumptions for variables to be included in statistical analysis. Students can fit and interpret a basic OLS model in R. Students can present results of an OLS regression using stargazer and jtools packages.

Course Contents

• Introduction to Excel 1
• Introduction to Excel 2
• Introduction to R and Exploratory Data Analysis in R 1
• Data manipulation and vizualization
• Open source data and data scraping
• Introduction to text mining in R
• OLS Regression
• PowerPoint

Assessment Elements

• Data exploration and manipulation tests
  Students will have 4 at-home tests that cover 4 main topics of this course: 1) excel, 2) exploratory data analysis in R, 3) text mining and data scraping, 4) OLS Regression
• Exam
  Take home project where students have to work on a problem using Excel and R. Students will provide Excel spreadsheets and R scripts for evaluation. Results of data analysis from Excel and R should be organized in a PowerPoint presentation.

Interim Assessment

• 2024/2025 1st module
  0.6 * Data exploration and manipulation tests + 0.4 * Exam

Bibliography

Recommended Core Bibliography

• Discovering statistics using R, Field, A., 2012
• R in action: Data analysis and graphics with R, Kabacoff, R.I., 2015

Recommended Additional Bibliography

• Quantitative finance: a simulation-based introduction using excel, Davison, M., 2014

Authors

• Zubarev Nikita Sergeevich