Bachelor 2023/2024

Business analytics, applied modelling and prediction

Type: Compulsory course (Data Science and Business Analytics)

Area of studies: Applied Mathematics and Information Science

Delivered by: Big Data and Information Retrieval School

Where: Faculty of Computer Science

When: 3 year, 3, 4 module

Mode of studies: offline

Open to: students of one campus

Instructors: Шабанова Диана Сундаровна

Language: English

ECTS credits: 4

Contact hours: 56

Course Syllabus

Abstract

Modelling is an important tool which all good managers should appreciate. The course extends and reinforces existing knowledge and introduces new areas of interest and applications of modelling in the ever-widening field of management.

Learning Objectives

• the mechanics of building applied business models
• managerial decision making
• producing and critiquing forecasts

Expected Learning Outcomes

• apply modelling at varying levels to aid decision-making
• understand basic principles of how to analyse complex multivariate datasets with the aim of extracting the important message contained within the large amount of data which is often available
• demonstrate the wide applicability of mathematical models while, at the same time, identifying their limitations and possible misuse
• use BI systems to create data storytelling and visualisation

Course Contents

• Introduction to Business analytics
• Data collection. Sampling
• Descriptive statistics. Confidence intervals
• Hypothesis testing
• Regression analysis
• BI System Intro
• KPIs and their hierarchies. Business metrics and their prioritization
• Introduction to Tableau
• Building Dashboards
• Time-series analysis and forecasting
• Optimization models
• Monte Carlo simulation models
• Data storytelling

Assessment Elements

• Homework 1
  Group work. Data analytics and interpretation.
• Homework 2
  Group work. Data analytics and interpretation.
• Group Case study
  Group work. Identify KPIs for a company or process.
• Group Project
  Building dashboard in Tableau and getting insights. Datasets – choice of students.
• Written exam
  In-class paper-based exam

Interim Assessment

• 2023/2024 4th module
  0.1 * Group Case study + 0.3 * Group Project + 0.15 * Homework 1 + 0.15 * Homework 2 + 0.3 * Written exam

Bibliography

Recommended Core Bibliography

• Business analytics : data analysis and decision making, Albright, S. C., 2020

Recommended Additional Bibliography

• Knaflic, C. N. (2015). Storytelling with Data : A Data Visualization Guide for Business Professionals. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1079665