Бакалавриат
2021/2022
Статистический анализ и визуализация данных в среде R и Python
Статус:
Курс по выбору (Управление бизнесом)
Направление:
38.03.02. Менеджмент
Кто читает:
Департамент маркетинга
Где читается:
Высшая школа бизнеса
Когда читается:
3-й курс, 3 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
16
Охват аудитории:
для своего кампуса
Преподаватели:
Дас Гуру Рамеш Рошан
Язык:
английский
Кредиты:
4
Контактные часы:
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 potentials. 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 “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 to introduce you to the fundamental of R and Python including an overview of how to write basic commands, understand different data types and data structures in R and Python, use packages and analyze data. We will start with the basics and gradually proceed to employ this tool to perform simple statistical analysis and visualization of data. For reference few courses available at the Coursera platform would provide integral support in better understanding of this course: https://www.coursera.org/learn/r-programming https://www.coursera.org/learn/python-for-data-visualization
Learning Objectives
- Get an overview of the R and Python: Understand why these are the best tool 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 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
- Connect these programming skills and use them to implement some of the fundamental statistical analysis and data visualizations
- Employ the basics of R/Python and computational thinking in solving real world problems
- Introduction/revisit to various basic programming techniques (including algorithms, control structures, functions) applicable in R/Python
- Introduction/revisit to various data types and structures available in R/Python and how exactly this knowledge can be helpful in utilization of the tool properly
- Learners will be introduced to two of the most powerful data analysis tool including a brief overview and basics to install/use i
- ntroduction/revisit to 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
- Day 1-Introduction to R
- Day 2- Function and Programming in R – Algorithms, Control structures, Functions
- Day 3- Statistical Analyses and Data Visualization using R
- Day 4: Statistical Analyses and Data Visualization using R/Introduction to Python
- Day 5: Exploring Python Further: Data types, Operators, Functions, Modules, Tuples
- Day 6: Statistical Analyses and Data Visualization using Python
- Day 7: Statistical Analyses and Data Visualization using Python
- Day 8: Final Class
Assessment Elements
- Participation in the classTo 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 ExamAn 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.
- Group projectsStudents in a group of 3/4 students will work together during the course in a business project. In the Day 3 of this course, 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.
Interim Assessment
- 2021/2022 3rd module0.1 * Participation in the class + 0.5 * Final Exam + 0.4 * Group projects
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.