Master
2022/2023
Introduction to Programming in R and Python
Type:
Elective course (Master of Business Analytics)
Area of studies:
Finance and Credit
Delivered by:
Практико-ориентированные магистерские программы факультета экономических наук
Where:
Faculty of Economic Sciences
When:
2 year, 2 module
Mode of studies:
distance learning
Online hours:
20
Open to:
students of one campus
Instructors:
Majid Sohrabi
Master’s programme:
Магистр аналитики бизнеса
Language:
English
ECTS credits:
3
Contact hours:
8
Course Syllabus
Abstract
This course is geared towards the development of practical skills. This course will not focus on syntactic and semantic constructs of R and Python, but on how these languages and their fea-tures can help with solving real-world research tasks and problems. This course will specifically focus on data acquisition and preprocessing, as well as the presentation of analysis results.
Learning Objectives
- This course is geared towards the development of practical skills. This course will not focus on syntactic and semantic constructs of R and Python, but on how these languages and their fea-tures can help with solving real-world research tasks and problems. This course will specifically focus on data acquisition and preprocessing, as well as the presentation of analysis results.
Expected Learning Outcomes
- • Use Python and R to work with JSON data • Use Python to create your own package • Use Python and R to work with JSON data
- • Use RStudio to organize your R code into projects • Use basic R data types to work with data
- • Use RMarkdown to create documents combining code and natural-language text • Use dplyr to import data from the most common file extensions, do the basic data manipulations • The basics of this discipline should be used in all other program related courses
- • Use R to create simple histograms, line plots, scatterplots, barplots and boxplots and save them to disk • Use R to customize your plots • Use R to display the relationship between analyzed variables • Use R to save and load your models
- • Use Python to create your own package
Course Contents
- SESSION ONE: Basic R syntax
- SESSION TWO: Basic R graphics
- SESSION THREE: Working with Tidyverse
- SESSION FOUR: R graphics with ggplot2
- SESSION FIVE: Introduction to Python
- SESSION SIX: Serialization in Python
Bibliography
Recommended Core Bibliography
- Horton, N. J., & Kleinman, K. (2015). Using R and RStudio for Data Management, Statistical Analysis, and Graphics (Vol. Second edition). Boca Raton, FL: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=957543
- Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2020). Data Mining for Business Analytics : Concepts, Techniques and Applications in Python. Newark: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2273611
Recommended Additional Bibliography
- A Tutorial on Machine Learning and Data Science Tools with Python. (2017). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E5F82B62
- Campbell, M. Learn RStudio IDE: Quick, Effective, and Productive Data Science. - Apress, 2019. - ЭБС Books 24x7.