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Course Syllabus

Master 2023/2024

Business Analytics as a Tool for Effective Management

Type: Compulsory course (Master in International Management)
Area of studies: Management
Delivered by: Department of Business Informatics
Where: Graduate School of Business
When: 1 year, 3 module
Mode of studies: offline
Open to: students of all HSE University campuses
Instructors: Svetlana Arkhipkina
Master’s programme: Международный менеджмент
Language: English
ECTS credits: 3
Contact hours: 24

Course Syllabus

Full SyllabusAsk Question

Abstract

The course covers both theoretical and practical aspects of business analytics (BA) and AI tools, in the context of real business problems. In today’s data-rich environment, businesses need analytics to remain competitive. The course emphasizes data analytics, a popular method for gaining insights from digital data. It also incorporates AI tools into business analysis. Assignments in the course teach students how to utilize open data sources and BI services for extracting, manipulating, analyzing, and visualizing data. The goal of the course is to assist students in making better decisions based on the data. Lectures, data analysis, case studies, discussions, and a group project are all part of the course. Lectures focus on the main theoretical concepts of BA, supported by supplementary reading materials. The group project allows students to practice BA techniques and tools using various data sources (datasets, online datasets, open datasets), data types (structured, semi-structured, unstructured data), and AI capabilities. Group project allows students to develop strategies for integrating AI into business processes, chatbots, applications, and consider the advantages of AI and ML for enhancing business performance. A case study based on real-world business data is included in the course.
Learning Objectives

Learning Objectives

  • The course is aimed at building skills in applying business analytics to real-world business and industrial problems.
Expected Learning Outcomes

Expected Learning Outcomes

  • Student applies business analytics to real-world business and industrial problems.
  • Student uses a variety of data sources and tools in business analytics process.
  • Student is able to find, extract, evaluate and prepare data for analysis.
  • Student creates clear visualizations of data and prepares presentation.
  • Student creates interactive dashboards and reports using business analytics tools.
  • Student selects appropriate business analytics tools and methods for solving business task.
  • Student interprets the findings based on business analytics.
Course Contents

Course Contents

  • Introduction to business analytics
  • Data sources and data preparation for business analytics
  • Analytical reporting and dashboards
  • Data mining and machine learning
Assessment Elements

Assessment Elements

  • non-blocking Practice
    Group practical assignments.
  • non-blocking Test
    Electronic test in LMS for 10 minutes.
  • non-blocking Presentation
    A 7-10 minutes presentation on the topic of the course.
  • non-blocking Participation
    Solving in-class assignments. Participating discussions.
  • non-blocking Project
    This is an oral examination: project presentation and defense (15 slides presentation). The computer application and written report must be submitted beforehand. Students carry out a project to apply the models and methods studied in the course to solve business problems. Students independently choose a subject area, form a problem statement, select the necessary data, perform analysis and interpret results.
Interim Assessment

Interim Assessment

  • 2023/2024 3rd module
    0.1 * Participation + 0.2 * Practice + 0.2 * Presentation + 0.4 * Project + 0.1 * Test
Bibliography

Bibliography

Recommended Core Bibliography

  • Nabavi, M., & Olson, D. L. (2019). Introduction to Business Analytics. New York: Business Expert Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1922612
  • S. Christian Albright, & Wayne L. Winston. (2019). Business Analytics: Data Analysis & Decision Making, Edition 7. Cengage Learning.
  • 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

  • Laursen, G. H. N., & Thorlund, J. (2016). Business Analytics for Managers : Taking Business Intelligence Beyond Reporting (Vol. Second edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1367899

Authors

  • ARKHIPKINA SVETLANA VLADIMIROVNA