2022/2023
Business Analytics: Diversity of Business Applications
Type:
Mago-Lego
Delivered by:
Практико-ориентированные магистерские программы факультета экономических наук
When:
2 module
Online hours:
20
Open to:
students of one campus
Instructors:
Elena Stegnii
Language:
English
ECTS credits:
3
Contact hours:
8
Course Syllabus
Abstract
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
Interim Assessment
- 2022/2023 2nd module#1 Quiz 5% #2 Quiz 10% #3 Quiz 20% #4 Quiz 10% #5 Quiz 10% #6 Quiz 15% #7 Final Project 30%
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