Statistical Analysis of Business Data

2024/2025

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Economics

Course type:

Compulsory course

When:

1 year, 2 module

Instructor

Kislitsyn, Dmitry

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 Project
  Students will work in groups to analyze a dataset related to a specific business domain and present their findings.

Interim Assessment

• 2024/2025 2nd module
  0.4 * Final Group Project + 0.3 * Final Test + 0.3 * Quizzes

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.

Authors

• BRODSKAYA NATALYA NIKOLAEVNA