Course Overview

In today’s data-driven business environment, organisations rely on data to improve decision-making, enhance operational performance, and uncover new opportunities for growth. As the volume and complexity of data continue to increase, professionals across industries need a practical understanding of how data science can transform raw information into meaningful business insights.

The Data Science for Business Intelligence Programme by Transformentors Academy is a five-day introductory course designed to provide participants with a solid foundation in data science concepts, tools, and techniques. The programme explores the complete data lifecycle, from data collection and preparation to analysis, visualisation, and predictive modelling.

Through practical examples and hands-on exercises, participants will learn how to organise, analyse, and interpret data to support business decisions. The course also introduces fundamental machine learning concepts and demonstrates how data-driven approaches can be used to identify patterns, forecast outcomes, and generate actionable insights.

By the end of the programme, participants will understand how data science supports business intelligence initiatives and will be equipped with the foundational knowledge required to apply data-driven thinking within their organisations or pursue further development in data science and analytics.

Agenda

Day — 1 Introduction to Data Science and Business Intelligence

• Understanding the fundamentals of data science and business intelligence.
• Exploring the role of data in supporting business decisions and organisational performance.
• Identifying the key differences and relationships between data science and business intelligence.
• Understanding the data lifecycle and the process of transforming data into actionable insights.
• Exploring common data science methodologies and analytical approaches.
• Introducing popular tools and technologies used in data science and business intelligence.
• Examining real-world applications of data-driven decision-making across industries.

Day — 2 Data Collection and Cleaning

• Understanding the process of collecting data from various business and operational sources.
• Identifying common data quality issues that affect analysis and decision-making.
• Applying data cleaning techniques to improve data accuracy, consistency, and completeness.
• Exploring spreadsheet tools for organising and preparing data for analysis.
• Performing basic data manipulation using Microsoft Excel or Google Sheets.
• Understanding data formatting, validation, and preparation best practices.
• Hands-on Exercise: Cleaning, formatting, and preparing datasets for analysis using spreadsheet tools.

Day — 3 Data Visualisation and Analysis

• Understanding the fundamentals of data visualisation and its role in communicating insights.
• Exploring techniques for presenting data clearly to support business decision-making.
• Creating charts, graphs, and visual reports using Microsoft Excel or Google Sheets.
• Understanding how to select appropriate visualisation methods for different types of data.
• Analysing datasets to identify trends, patterns, and key business insights.
• Interpreting visual data to support stakeholder communication and reporting.
• Hands-on Exercise: Creating basic data visualisations and analysing patterns within datasets.

Day — 4 Introduction to Machine Learning

• Understanding the fundamentals of machine learning and its role in business intelligence.
• Exploring real-world applications of machine learning in business decision-making and predictive analytics.
• Understanding the differences between supervised and unsupervised learning techniques.
• Examining common machine learning algorithms and their practical business applications.
• Understanding how machine learning can be used to identify patterns, trends, and opportunities within data.
• Exploring the process of preparing data for machine learning analysis.
• Hands-on Exercise: Applying simple machine learning techniques using Microsoft Excel or Google Sheets.

Day — 5 Business Intelligence Applications and Conclusion

• Exploring real-world applications of data science and machine learning in business intelligence.
• Examining how organisations use data-driven insights to support strategic and operational decision-making.
• Understanding the business value and impact of data science across different industries and functions.
• Identifying opportunities to apply analytics and machine learning to solve business challenges.
• Discussing the limitations, risks, and practical considerations associated with data science initiatives.
• Evaluating the role of data-driven decision-making in improving organisational performance.
• Exploring career pathways and professional development opportunities in data science and business intelligence.
• Programme recap, key takeaways, and next steps for further learning and career advancement.

Learning Outcomes

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

• Understand the fundamental concepts, principles, and applications of data science in business intelligence.
• Identify key data science techniques, tools, and processes used to support data-driven decision-making.
• Collect, clean, organise, and prepare data for analysis using appropriate data management techniques.
• Perform basic data manipulation and preprocessing using spreadsheets and other analytical tools.
• Apply data visualisation techniques to present information clearly and communicate insights effectively.
• Interpret data trends, patterns, and business metrics to support strategic and operational decisions.
• Understand the principles of supervised and unsupervised machine learning techniques.
• Apply basic machine learning models to business problems and evaluate their outcomes.
• Use data science approaches to generate actionable business insights and improve decision-making processes.

Who Should Attend

This programme is designed for individuals seeking a foundational understanding of data science and its application in business intelligence, including:

• Business Professionals looking to leverage data for improved decision-making and business performance.
• Data Analysts seeking to expand their knowledge of data science and machine learning concepts.
• Business Intelligence and Reporting Professionals.
• Project Managers and Functional Managers working with data-driven initiatives.
• Students and Graduates interested in pursuing careers in data science, analytics, or business intelligence.
• Professionals from any discipline who want to understand how data science can support organisational objectives.
• Individuals seeking to build a foundation for further study in data science, analytics, and machine learning.