Starting a Book Retelling: Data Science Foundations by Mariadas and Huke

I have been working in tech for over 20 years. Seen a lot of trends come and go. But data science is not a trend. It is here to stay. And honestly, I wanted a book that explains the whole thing from the ground up without assuming I already know everything.

That is how I found Data Science Foundations: Navigating Digital Insight by Stephen Mariadas and Ian Huke (ISBN: 978-1-78017-6994, published by BCS in 2025).

Why This Book

I have a shelf full of data science books. Some are too academic. Some skip the basics and jump straight into code. And some try to cover everything but explain nothing.

This one felt different. It is written for people who want to understand the full picture. Not just the math. Not just the tools. The whole lifecycle of a data science project, from figuring out what problem to solve all the way to presenting your findings to people who make decisions.

The book has 16 chapters and covers a lot of ground. Stakeholders, project delivery, ethics, data sourcing, preparation, statistics, model selection, visualizations, model evaluation, communication, machine learning, AI, and real case studies. It wraps up with a conclusion chapter that ties everything together.

Here is the thing. I am not a data scientist by title. But I have spent years working with data, building systems, and making sense of numbers. This book caught my attention because it does not try to make you a data scientist overnight. It gives you a solid foundation. And that is exactly what the title promises.

What I Am Going to Do

I am going to read this book chapter by chapter and write my thoughts on each one. Think of it as a retelling. Not a summary, not a review, but my take on what each chapter covers and why it matters.

Here is the rough plan:

• Chapter 1: Introduction
• Chapter 2: Stakeholders
• Chapter 3: Project Delivery
• Chapter 4: Ethics and Lawfulness
• Chapter 5: Discovery
• Chapter 6: Properties of Data
• Chapter 7: Sourcing
• Chapter 8: Preparation
• Chapter 9: Basic Concepts (statistics, probability, the fun stuff)
• Chapter 10: Model Selection
• Chapter 11: Visualizations
• Chapter 12: Model Evaluation
• Chapter 13: Communication
• Chapter 14: Machine Learning and AI
• Chapter 15: Case Studies
• Chapter 16: Conclusion
• Final post: My closing thoughts on the whole book

That is 17 more posts after this one. One chapter at a time.

Who Is This For

If you are curious about data science but do not know where to start, follow along. If you already work with data and want to fill some gaps, this might help too. I will keep things simple and skip the jargon where I can.

I grew up in a country where textbooks were dry and hard to read. So I always try to explain things the way I wish someone had explained them to me. Plain words. Real examples. No fluff.

Let Us Get Started

I am genuinely looking forward to this. The book has a nice structure and the authors clearly put thought into making it approachable. Over the next few weeks, I will work through each chapter and share what I learn along the way.

If you want to read the book yourself, grab a copy and read along. If not, that is fine too. These posts will give you a good sense of what is inside.

Let us begin.

Next in series: Chapter 1: Introduction