Бакалавриат 2020/2021

Программирование на языке R

Статус: Курс по выбору (Маркетинг и рыночная аналитика)

Направление: 38.03.02. Менеджмент

Кто читает: Департамент маркетинга

Где читается: Высшая школа бизнеса

Когда читается: 3-й курс, 4 модуль

Формат изучения: с онлайн-курсом

Преподаватели: Дас Гуру Рамеш Рошан

Язык: английский

Кредиты: 3

Контактные часы: 20

Course Syllabus

Abstract

In this data-driven world, marketers have realized that its only through a regular and proper anlyses of data, the strengths, weaknesses, opportunities and threats of a company can be identified. It is highly important for firms to decide on a tool for data-analysis and in the last decades R has emerged as an attractive option for the same. In this course on R-Programming, we will explore this open-source programming language which has emerged as a tool with multiple attributes and a powerful tool for data analysis. This course is designed to introduce you to the fundamental of R including an overview of how to write basic commands, understand different data types and data structures in R, use packages and analyze data. We will start with the basics and gradually proceed to employ this tool to perform simple marketing analysis like multiple regression or factor analysis.

Learning Objectives

• Get an overview of the R: What is R and why is it amongst the best tool for data analysis?
• Getting an insight of the basic data types used in R
• Explore the various data structures used in R (vectors, matrices, data frames and lists)
• Understand the basics of programming in R (control structures, functions, coding standards, packages)
• Learn to apply some of these programming skills in practical analysis like regression, multiple regression and factor analysis
• Making sense of data: Learn to run through a 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

• Able to determine when to use R|Python as a data analysis tool for business and markeitng analytics
• Employ the basics of R-programming and computational thinking in solving real world problems
• Use programming skills to implement some of the fundamental marketing analysis techniques like regression, multiple regression and factor analysis
• Able to apply various basic programming techniques (including algorithms, control structures, functions) applicable in R|Python
• Know the various data types and structures available in R|Python and how exactly this knowledge can be helpful in utilization of the tool properly

Course Contents

• Introduction to R
  Brief overview of the course organization and the course plan. Introduction to R: Installing R/R-Studio, Exploring the console and getting used to of the platform including setting up directory, accessing data etc.
• Data types in R
  Explore various data types available in R and explore their limitations and advantages in data analysis
• Data Structures in R
  Explore most important data structures in R including vector, list, matrix, data frame, and factors
• Using functions and packages
  Understand the implementation of pre-defined functions and packages
• Programming in R – Algorithms, Control structures, Functions I
  Explore the basics of computer programming in R using computational thinking by developing an algorithm for a problem. Evaluate the various control structures (like if-else, ef-, while-, for- etc.) which can be employed in the problem solving.
• Programming in R – Algorithms, Control structures, Functions II
  Learn to recognize pattern and how a problem can be decomposed and solved using pre-defined functions
• Applying R-Programming in Marketing Analysis
  Understand how R-programming can be used to explore various marketing analysis techniques learnt in the course. We will start with exploring relationships between two variables including correlations and regressions and proceed to multivariate analysis methods like multiple regressions and factor analysis
• Final Class
  This will be a class meant for providing guidance regarding the continuation of the modules if not completed and summarizing and systemizing the results of the offline and online classes.

Assessment Elements

• Participation in the class
  To encourage the active participation of our students, 10% of final course grade will be attributed to your attendance and class participation. Apart from attending the lectures, this participation element would be based on 2 small online quizzes with multiple choice objective questions.
• Final 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 the seminar classes.
• Group projects
  Students in a group of 4 students will work together during the seminars in a business project. In the last four weeks of this course, groups will explore a real-life marketing data and implement the marketing analysis lessons and come up with business decisions meant for the firm in a short word document (1-3 pages maximum).

Interim Assessment

• Interim assessment (4 module)
  0.4 * Final Exam + 0.5 * Group projects + 0.1 * Participation in the class

Bibliography

Recommended Core Bibliography

• Ren, K. (2016). Learning R Programming. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1409189

Recommended Additional Bibliography

• An introduction to R for spatial analysis & mapping, Brunsdon, C., 2015
• Gillespie, C., & Lovelace, R. (2016). Efficient R Programming : A Practical Guide to Smarter Programming. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1435808
• The art of R programming : a tour of statistical software design, Matloff, N., 2011