Introduction to programming and data analysis in R 2600-ABdz1PPADR
The course introduces participants to the R working environment, the installation of external packages, and the fundamentals of using scripts written in this language. Popular functions, data analysis tools, and methods of presenting results will be discussed. Students will master techniques for working with datasets, including creating new data structures, defining objects and variables, and applying commands essential for business analytics. The basics of object-oriented programming, data structures, and functions will also be presented. By the end of the course, participants will have acquired the skills necessary to design and implement programs that make full use of the R language’s potential in business analytics.
In addition to in-class sessions, students will complete a project in the field of business analytics. The course concludes with a written exam.
Topics covered include:
• Installation of software and libraries, use of the interface, principles of operation, and creating and saving scripts
• Basic functions and objects, syntax fundamentals, and script commenting
• Importing and exporting data in common formats and preparing data for analysis
• Working with real-world datasets and basic principles of data cleaning
• Loops and conditional statements
• Fundamentals of writing functions
• Data visualization with basic graphics libraries
• Descriptive statistics
• Correlation analysis and Student’s t-test
• Simple and multiple regression: conducting analyses and checking assumptions
• Analysis of variance (ANOVA) and post-hoc tests
• Cluster analysis: hierarchical, k-means, and modern approaches
• Basics of the grammar of graphics and creating visualizations with ggplot2
• Preparing and presenting basic quantitative analyses in business analytics
Student Workload:
• 30 hours of in-class instruction
• 15 hours of practical assignments completed at home
• 30 hours for preparing the final project
• 15 hours for exam preparation
Type of course
Course coordinators
Learning outcomes
K_U03 – adapt existing methods and tools or propose new ones based on them, using advanced information and communication technologies and appropriate sources to identify, diagnose, and solve problems related to data analysis in both the internal and external environment of an organization.
K_U04 – formulate and test hypotheses related to simple research problems.
K_U05 – propose solutions to tasks carried out under unpredictable conditions.
K_U06 – effectively present analysis results in the field of management to diverse audiences using specialized terminology, and engage in debate, including in English.
K_U09 – enhance acquired qualifications, support others in this regard, and demonstrate the ability for self-directed learning
Assessment criteria
Practical assignments – 20% of the final grade.
Project – 30% of the final grade.
Written exam – 50% of the final grade.
A minimum of 60% of the total course points and at least 50% on the written exam are required to pass the course.
Practical placement
-
Bibliography
Literature presented during class
Wickham, H., & Grolemund, G. (2017). R for data science. Sebastopol: O'Reilly.
Wickham, H. (2016). ggplot2: elegant graphics for data analysis. Cham: Springer international publishing.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: