Course Overview

In today’s competitive business environment, organisations can no longer rely solely on intuition when making decisions. The ability to analyse data, model uncertainty, and apply analytics to real-world business challenges has become an essential capability across industries.

The Business Analytics: Data and Decisions course by Transformentors Academy is a practical 5-day programme designed to equip professionals with the analytical skills and decision-making techniques needed to transform raw data into meaningful business insights and strategic actions.

Throughout the course, participants will explore statistical reasoning, probability, descriptive and predictive analytics, and Python programming to solve real business problems. The programme also covers decision modelling, optimisation techniques, and linear programming methods used in budgeting, planning, and resource allocation.

Through practical exercises, interactive discussions, and real-world case studies, participants will strengthen their critical thinking, data interpretation, and business decision-making capabilities to lead with confidence and evidence-based insights.

Agenda

Day — 1 Maths and Statistics Primer

• Understanding the importance of data analytics in business decision-making
• Understanding the role of data in supporting business decisions
• Introduction to probability theory and probability models
• Exploring conditional probability and applications of Bayes’ Theorem
• Understanding the Law of Total Probability
• Introduction to probability distributions and binomial distributions
• Understanding the Central Limit Theorem and its implications
• Exploring techniques for manipulating normal distributions

Day — 2 Python Primer

• Overview of operating systems and their role in programming environments
• Understanding methods of working with variables in Python
• Exploring the process of creating, managing, and using lists in Python
• Understanding techniques for working with numerical lists and tuples
• Exploring dictionaries and their applications in Python
• Defining Boolean variables and their practical uses
• Understanding tools for efficient use of conditional statements and variables in Python
• Understanding the importance of functions in Python programming
• Demonstration of Python coding and techniques for code manipulation

Day — 3 Descriptive Analytics

• Understanding data and its characteristics
• Understanding the role of data in informed decision-making
• Exploring methods for estimating statistics from datasets
• Understanding techniques for Maximum Likelihood Estimation (MLE)
• Identifying and measuring correlations between variables
• Analysing outliers in datasets
• Understanding linear regression for predictive modelling
• Case Study: Applying descriptive analytics to real-world scenarios

Day — 4 Predictive Analytics

• Understanding the machine learning workflow
• Exploring the basics of supervised learning techniques
• Differentiating between forecasting and inference
• Understanding applications of nearest neighbours in problem classification
• Exploring the steps involved in applying regression trees for predicting business outcomes
• Understanding the methodology of classifying data using Support Vector Machines (SVM)
• Defining data cluster similarities and clustering concepts
• Exercise: Real-world applications of machine learning
• Understanding machine learning applications in real-world business environments

Day — 5 Foundations of Linear Programming

• Understanding major optimisation challenges and their definitions
• Exploring efficient strategies for addressing production planning issues
• Understanding capital budgeting and investment decision-making
• Defining and identifying constraints in capital budgeting
• Exploring methods for finding optimal solutions to business problems
• Using Excel to solve optimisation problems
• Understanding the stages of modelling business scenarios as linear programming problems
• Exploring optimisation models for business decision-making
• Understanding practical techniques for improving business decisions
• Case Study: Linear programming applications in real-world scenarios

Learning Outcomes

By the end of this course, participants will be able to:

• Understand how data analytics supports fact-based business decision-making
• Apply statistical concepts, probability, and probability distributions to model business uncertainty
• Use Python programming to manage datasets, variables, conditional logic, and reusable functions
• Conduct descriptive analytics using statistical techniques and data visualisation methods
• Apply predictive analytics and machine learning techniques for forecasting, classification, and decision support
• Utilize linear programming and optimisation techniques for business challenges such as budgeting and resource allocation
• Develop and interpret analytical solutions for marketing, finance, operations, and strategic planning decisions

Who Should Attend

This course is ideal for professionals looking to strengthen their analytical thinking and apply data-driven decision-making techniques, including:

• Business Analysts and Data Analysts seeking to enhance their technical capabilities
• Operations Managers, Financial Analysts, and Product Teams using data for strategic decision-making
• Marketing Professionals applying analytics to understand customer behaviour
• IT and Data Science Professionals supporting business insights across departments
• Project Managers and Consultants working with data-driven solutions
• Anyone interested in transitioning into a business analytics role