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Course Syllabus

Master 2022/2023

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: 2 year, 1, 2 module
Mode of studies: distance learning
Online hours: 8
Open to: students of all HSE University campuses
Instructors: Yulia A. Kuznetsova
Master’s programme: Международный менеджмент
Language: English
ECTS credits: 4
Contact hours: 32

Course Syllabus

Full Syllabus

Abstract

The course addresses a variety of theoretical and practical aspects of Business Analytics (BA) applying to problem solving. The case applications, methods and instruments are considered to discover advantages of BA for the business performance improve. Analytics is something every business needs to stay competitive in today’s data-filled environment. The course focuses on Data Analytics as a popular approach to get insights from digital world. The course assignments are designed to teach students to use open data resources, BI services to extract, manipulate, analyze and visualize data with the end goal of making better, data-informed decisions. The course is taught using the combination of lectures, data processing exercises, case analysis, discussions, quizzes and individual assignment for homework. Lectures discover the main theoretical aspects for BA and supplemented by the additional reading sources. Short quizzes as the assessment activities will be given after every theoretical part to make sure of the material clearance. Exercises are designed to gain students’ practice methods and tools of BA using different types of sources (datasets, online data, open data), of data (structured, semi structured, textual data), to explore the ability of PC desktop and SaaS technologies. Case based on actual data of business problem solving is included into 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 Participation
    Solving in-class assignments. Participating discussions.
  • non-blocking Practice
    Group practical assignments.
  • non-blocking Presentation
    A 7-10 minutes presentation on the topic of the course.
  • non-blocking Test
    Electronic test in LMS for 10 minutes.
  • 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

  • 2022/2023 2nd module
    0.1 * Test + 0.4 * Project + 0.2 * Presentation + 0.1 * Participation + 0.2 * Practice
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