2024/2025
Statistical Analysis of Business Data
Type:
Mago-Lego
Delivered by:
Department of Economics
When:
2 module
Open to:
students of one campus
Instructors:
Dmitry Kislitsyn
Language:
English
ECTS credits:
3
Course Syllabus
Abstract
This course is designed to equip students with the ability to analyze critical business metrics across various sectors using statistical analysis techniques in R. Each lecture will focus on the key metrics pertinent to a specific business domain, accompanied by practical applications where students will analyze relevant datasets in R. Emphasizing hands-on experience with real-world data, this course aims to enable students to make informed decisions grounded in statistical reasoning.
Learning Objectives
- Upon successful completion of this course, students will be able to: 1. Apply statistical analysis techniques in R to interpret business data accurately. 2. Analyze and visualize datasets relevant to financial, marketing, operational, human resource, customer, and supply chain metrics. 3. Critically assess business scenarios using statistical methodologies to derive actionable insights. 4. Collaborate effectively in teams to conduct analyses and present findings based on business metrics.
Expected Learning Outcomes
- apply statistical analysis techniques in R to interpret business data accurately
- analyze and visualize datasets relevant to financial, marketing, operational, human resource, customer, and supply chain metrics.
- сritically assess business scenarios using statistical methodologies to derive actionable insights
- collaborate effectively in teams to conduct analyses and present findings based on business metrics
Course Contents
- 1. Introduction to Business Metrics
- 2. Financial Metrics
- 3. Marketing Metrics
- 4. Operational Metrics
- 5. Human Resource Metrics
- 6. Customer Metrics
- 7. Supply Chain Metrics
Assessment Elements
- Quizzes
- Final Test
- Final Group ProjectStudents will work in groups to analyze a dataset related to a specific business domain and present their findings.
Bibliography
Recommended Core Bibliography
- Kolaczyk E. D., Csárdi G. Statistical analysis of network data with R. – New York : Springer, 2014. – 207 pp.
- Statistical analysis of financial data in R, Carmona, R., 2014
Recommended Additional Bibliography
- Nisbet, R., Miner, G., & Yale, K. (2017). Handbook of Statistical Analysis and Data Mining Applications: Vol. Second edition. Academic Press.