Course Overview

The Data Science for Business Intelligence course by Transformentors Academy is a practical 5-day programme designed to help participants develop the foundational skills required to work confidently with data and support data-driven decision-making. Using widely adopted tools such as Microsoft Excel and Google Sheets, participants will learn how to collect, clean, prepare, analyse, and visualise data to generate actionable business insights.

The course also introduces key concepts in data science, business intelligence, and machine learning, helping participants understand how modern analytical techniques are applied to solve real-world business challenges. Through practical exercises and hands-on activities, learners will gain the confidence to work with data effectively and support informed decision-making within their organisations.

Whether you are new to data analysis or looking to transition into a more analytical role, this course provides a structured and accessible pathway to understanding data science and business intelligence in a practical business context.

Agenda

Day — 1 Introduction to Data Science and Business Intelligence

• Introduction to the fundamentals of Data Science and Business Intelligence
• Exploring the key differences between Data Science and Business Intelligence
• Understanding the roles and skills required within Data Science and BI teams
• Overview of key tools and techniques used in Data Science and Business Intelligence
• Understanding how Data Science and Business Intelligence collaborate within business environments
• Exploring real-world applications of Data Science in Business Intelligence

Day — 2 Data Collection and Cleaning

• Understanding different data sources and methods of data collection
• Determining the importance of clean, accurate data for effective analysis and decision-making
• Introduction to spreadsheets and basic data manipulation using Microsoft Excel or Google Sheets
• Exploring techniques for cleaning, formatting, and preparing data for analysis
• Understanding how to use functions and formulas to structure, validate, and organise data
• Exercise: Cleaning and preparing a messy dataset using Excel or Google Sheets

Day — 3 Data Visualisation and Analysis

• Introduction to the fundamentals of data visualisation and its role in data storytelling
• Exploring key principles for designing effective charts, graphs, and dashboards
• Introduction to creating charts and graphs using Microsoft Excel or Google Sheets
• Understanding how to identify trends, patterns, and outliers through visual analysis
• Exploring techniques for communicating data insights effectively to different business audiences
• Understanding best practices for selecting the most appropriate chart type for different data sets
• Exercise: Creating basic data visualisations and identifying meaningful patterns within data

Day — 4 Introduction to Machine Learning

• Overview of machine learning and its role in Business Intelligence
• Exploring the fundamental concepts and principles of machine learning
• Introduction to supervised learning and commonly used algorithms
• Introduction to unsupervised learning and its business applications
• Examining real-world business use cases of machine learning and predictive analytics
• Exploring techniques for applying basic machine learning concepts using Microsoft Excel or Google Sheets
• Exercise: Performing a simple linear regression analysis using Excel or Google Sheets on a sample dataset

Day — 5 Business Intelligence Applications and Conclusion

• Exploring real-world applications of Data Science and Machine Learning in Business Intelligence
• Understanding the business impact of data-driven decision-making and analytics-driven strategies
• Discussing common challenges and limitations of Data Science and Business Intelligence in practice
• Reviewing key course takeaways and recommended learning pathways for continued development
• Exploring next steps for building a career in Data Science and Business Intelligence
• Exercise: Business case review and development of a data-driven strategy proposal

Learning Outcomes

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

• Understand the roles of Data Science and Business Intelligence in supporting effective business decision-making
• Describe the processes of collecting, cleaning, and preparing real-world data that may be incomplete, inconsistent, or unstructured
• Use Microsoft Excel and Google Sheets to perform basic calculations and execute common data analysis tasks
• Create clear and effective charts, graphs, and dashboards to communicate insights and trends to stakeholders and decision-makers
• Understand the fundamentals of machine learning and how predictive models can be used to identify patterns and forecast outcomes
• Recognize practical applications of Data Science and Business Intelligence and identify pathways for further career development in the field

Who Should Attend

This course is ideal for professionals looking to bridge the gap between business and data-driven decision-making, including:

• Business Professionals and Business Analysts seeking to understand how data supports strategic decisions
• Team Leaders and Project Managers looking to monitor, measure, and communicate performance more effectively
• Career Changers exploring opportunities in Data Science, Data Analytics, or Business Intelligence roles
• Early-Career Professionals and Graduates seeking practical, tool-based experience with data analysis
• Anyone who works with spreadsheets and wants to develop the mindset and skills of a data analyst