Bachelor 2023/2024

Statistical Analysis and Data Visualization in R and Python

Type: Elective course (Business Administration)

Area of studies: Management

Delivered by: Department of Marketing

Where: Graduate School of Business

When: 3 year, 4 module

Mode of studies: offline

Open to: students of all HSE University campuses

Instructors: Дас Гуру Рамеш Рошан

Language: English

ECTS credits: 3

Contact hours: 30

Course Syllabus

Abstract

In this data-driven world, business decisions need to be backed by the insights one can retrieve from the regular and proper analyses of data at hand. Data Science has emerged as an interesting discipline lately with huge potential. For getting started in data analysis, one of the most important skills is proficiency in a statistical programming language and in the last decades R and Python have emerged as most sought-after tools when it comes to cleaning, manipulating, analyzing, and visualizing data. In this course “Statistical Analysis and Data Visualization in R and Python”, we will explore these two open-source programming languages which can handle just about any data analysis task, and are considered relatively easy languages to learn, especially for beginners. This course is designed for undergraduate students with no prior experience in programming or data analysis. The course will provide an introduction to statistical analysis and data visualization using R and Python, with a focus on basic concepts and techniques. The course will be divided into 7 lecture sessions and 8 seminar sessions. Each lecture will be followed by a related seminar, where students will work on practical projects to reinforce the concepts learned in the lecture.

Learning Objectives

• Get an overview of R and Python: Understand why these are the best tools for data analysis
• Getting an insight of the basic data types used in R and Python
• Explore the various data structures used in R and Python (vectors, matrices, data frames and lists)
• Understand the basics of programming in R and Python (control structures, functions, coding standards, packages)
• Learn to apply some of these programming skills in practical analysis like basic statistics and visualization of data using graphs, plot etc.
• Making sense of data: Learn to run through real-life marketing data, do simple projects covering techniques learnt in class and eventually use the performed data analysis to make business decisions

Expected Learning Outcomes

• Understanding basic statistical concepts and their applications in marketing research
• Importing, manipulating, and visualizing data using R and Python
• Performing descriptive and inferential statistical analyses using R and Python
• Understanding basic programming concepts and writing simple programs in R and Python
• Integrating R and Python to perform advanced data analysis

Course Contents

• Introduction to statistical analysis and data visualization
• Exploring R
• Analyzing data using various statistical methods in R
• Data-driven decision-making using R and some advanced data visualization
• Exploring Python
• Analyzing data using various statistical methods in Python
• Data-driven decision-making using Python and some advanced data visualization
• Integrating R and Python

Assessment Elements

• Participation in the class
  Participating in activities and assignments, and answering questions discussed in seminars
• Group project
  Students in a group of 4/5 students will work together during the course in a business project. Groups will get access to a real-life marketing data and associated challenge which will demand simple statistical analysis learnt in the course. Students will conduct the necessary data analysis using R and Python to come for a solution to the marketing challenge. Eventually, every group will submit a short word document (2-3 pages maximum) explaining the suggested solution backed by data analysis and visualization.
• Exam
  An online exam would be conducted at the end of the course. Students will be asked to answer questions exploring their theoretical and practical knowledge based on the class lectures and the project work they will be doing in this course.

Interim Assessment

• 2023/2024 4th module
  0.5 * Exam + 0.4 * Group project + 0.1 * Participation in the class

Bibliography

Recommended Core Bibliography

• Alain Zuur, Elena N. Ieno, & Erik Meesters. (2009). A Beginner’s Guide to R. Springer.
• Chapman, C., & Feit, E. M. (2015). R for Marketing Research and Analytics. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=964737

Recommended Additional Bibliography

• Lutz, M. (2009). Learning Python : Powerful Object-Oriented Programming: Vol. 4th ed. O’Reilly Media.