Кафедра проблем безопасности

This course is designed to open the doors of the world of business analytics. Nowadays a lot of organizations make their decisions based on data-driven approach. How to make the right decision? Which methods are used in multinational companies? This course is about demonstrating the diversity of real cases and applications of methods, techniques, and theories in various areas.Each week of this course is a piece of a puzzle where you will meet different experts from the industry who will share with you best practices from the market. Bringing together all the pieces you will understand the key definitions used in business analytics and will learn about data ana-lytics techniques which can be applied in marketing, sales, PR, HR, and finance. “Business Analytics: Diversity of Practical Applications” aims to help you to navigate in the variety of career opportunities which are opened for business analysts.

Learning Objectives

To reproduce the key definitions used in business analytics and and be able to apply data analytics techniques in marketing, sales, PR, HR, finance, and other areas.

Expected Learning Outcomes

To reproduce key terms in business analytics and role of business analyst in modern organizations
Be familiar with digital landscape and key digital trends
To reproduce types of analytical approaches according to Gartner
Be able to apply Agile way of working
Be familiar with basics of Data management
Be familiar with the many definitions of performance
To reproduce the approaches to measuring performance
To reproduce the difference between effectiveness and efficiency
To apply the modeling approaches to measuring performance
Be able to build model for measuring efficiency - Data Envelopment Analysis model - in R
Be familiar with the basic concepts and metrics of Social Network Analysis
Be able to visualize, describe and analyze networks in Gephi
To apply the capabilities of Social Network Analysis in business management
Be familiar with main areas of consumer and market research for making business decisions
Be able to go through the analytical process from problem to the solution
Be aware of Open Data sources
Be able to extract the data using API
To identify key opportunities of natural language processing
To reproduce types of possible tasks, solved by text mining
Be familiar with contemporary text mining models and approaches
Be able to apply text mining in your own tasks
To apply key terms of business process management
Be able to draw basic processes using BPMN 2.0 notations
To detect the role of financial modelling in a decision-making process

Course Contents

SESSION ONE: Introduction to Business Analytics
SESSION TWO: Performance Evaluation
SESSION THREE: Social Network Analysis: Applications for Organizations
SESSION FOUR: Analytical Process: from Business Problem to Solution
SESSION FIVE: Contemporary Text Analysis
SESSION SIX: Business Process Management and Financial Modelling

Assessment Elements

тесты по завершению разделов
после просмотра видео необходимо пройти тест
Итоговый проект, взаимное оценивание
Готовится итоговый проект по выбранной тематике и загружается для взаимного оценивания.

Interim Assessment

2025/2026 2nd module
0.6 * тесты по завершению разделов + 0.4 * Итоговый проект, взаимное оценивание

Bibliography

Recommended Core Bibliography

Business analytics : data analysis and decision making, Albright, S. C., 2020
S. Christian Albright, & Wayne L. Winston. (2019). Business Analytics: Data Analysis & Decision Making, Edition 7. Cengage Learning.
S. Christian Albright, Wayne L. Winston, Mark Broadie, Peter Kolesar, Lawrence L. Lapin, William D. Whisler, & Jack W. Calhoun. (n.d.). Data Analysis and Decision Making, Fourth Edition. Http://Www.Cengagebrain.Com/Content/Albright76125_0538476125_01.01_toc.Pdf.

Recommended Additional Bibliography

A practitioner's guide to business analytics : using data analysis tools to improve your organization's decision making and strategy, Bartlett, R., 2013

Authors

SEDOVA NATALIA Sergeevna
Kropanev Vadim Georgievich

Аналитик деловой разведки

Business Analytics: Diversity of Practical Applications

Преподаватели

Course Syllabus

Abstract