Book: The C++ Programmer’s Mindset
Author: Sam Morley
ISBN: 978-1-83588-842-1
Chapter 11 is the computational core of the rubber duckies project. All those file readers were building toward one question: where on Earth are these sightings clustering? Morley implements k-means from scratch. Choosing k and embedding geographic data correctly are the hard parts, not the iteration loop itself.
Book: Data Science: The Hard Parts
Author: Daniel Vaughan
ISBN: 978-1-098-14647-4
Publisher: O’Reilly Media (2024)
I’m starting a walkthrough of Daniel Vaughan’s Data Science: The Hard Parts. Over the next few weeks I’ll post one chapter at a time, retelling what the book says and adding my own take.
Nineteen posts. Sixteen chapters. One book. And here we are at the end.
When I started this retelling of Data Science Foundations: Navigating Digital Insight by Stephen Mariadas and Ian Huke (ISBN: 978-1-78017-6994, BCS 2025), I was not sure how it would go. Some books lose steam halfway. Some start strong and fizzle. But this one stayed consistent from first chapter to last.
So we made it through the whole book. And honestly? It was worth the ride.
The biggest thing Scavetta and Angelov got right is the framing. They didn’t write a “Python is better” or “R is better” book. They wrote a “both are useful, here’s when to use which” book. And that’s the mature take.
So we made it. Chapter 16 is the conclusion of Data Science Foundations by Stephen Mariadas and Ian Huke. And like most good conclusions, it does not introduce anything new. Instead it steps back and asks: what did we learn, and where is all of this going?
The appendix of “Python and R for the Modern Data Scientist” is basically a bilingual dictionary. It runs about 40 tables long and covers everything from package management to indexing. You could spend a whole afternoon reading through it.
Theory is nice. But does any of it work in the real world? Chapter 15 of Data Science Foundations by Stephen Mariadas and Ian Huke answers that with five case studies. Real people, real problems. One of them technically failed. And that is part of the point.
The whole book has been building to this. Six chapters of philosophy, syntax comparisons, and interoperability tricks. Now Chapter 7 drops a real project on the table. Build it with both languages. Together. Start to finish.
Everyone has an opinion about AI. Your coworker worries robots will take his job. Your cousin swears ChatGPT wrote his college essay. Chapter 14 of Data Science Foundations by Stephen Mariadas and Ian Huke explains what machine learning and artificial intelligence actually are. And how data science connects to all of it.
Chapter 6 is where the book finally delivers on its promise. All that talk about using both languages together? This is where it actually happens. Rick Scavetta walks through the nuts and bolts of making Python and R talk to each other in the same project.
You did the hard work. You collected data, cleaned it, tested your models. And now you need to tell someone what you found. This is where Chapter 13 of “Data Science Foundations” by Stephen Mariadas and Ian Huke comes in. Communication. The part that separates useful data science from data science that nobody cares about.
Chapter 5 is where Boyan Angelov gets practical about the question everyone dances around: which language should you actually use for which job?
You built a model. It runs. It gives you numbers. But does it actually work? That is what Chapter 12 of “Data Science Foundations” by Stephen Mariadas and Ian Huke is about. Building a model is one thing. Trusting it is something else.
Chapter 4 is where the book stops teaching you the languages and starts telling you when to use which one. This is Part III, “The Modern Context,” and Boyan Angelov takes the lead here. The question is simple: given a specific data format, which language gives you a better experience?
You ran the analysis. Got your numbers. Built a model. Now you need to show people what you found. And here is where most data people trip up. They pick the wrong chart, overload it with details, and the audience walks away confused.
Chapter 2 showed Pythonistas how to pick up R. Chapter 3 flips the script. Now it’s the R user’s turn to step into Python territory. Rick Scavetta writes this one, and he does a good job easing R folks into a world that feels messier at first glance.
This is Part 2 of 2 for Chapter 10. In Part 1 we covered how to pick the right model and looked at regression. Now we get into the rest: time series, classification, clustering, and association analysis.
In Part 1 we covered R basics: setting up your environment, installing packages, working with tibbles, and understanding R’s type system. Now we get to the good stuff. Lists, factors, finding things in your data, and the iteration patterns that make R feel so different from Python.
You have data. You have a question. But which model do you actually use?
Chapter 10 of “Data Science Foundations” by Stephen Mariadas and Ian Huke is the biggest chapter in the book. So big I split this retelling into two parts. This is Part 1. It covers the types of analytics, understanding your data and hypothesis, and how to pick the right model.
Chapter 2 is where the book gets hands-on. Rick Scavetta takes the wheel and walks Python developers through R. Not from scratch, but with the assumption you already know how to code. The chapter is big, so I split it into two posts. This is the first half.
Math scares people. I get it. You hear “standard deviation” and suddenly you are back in high school staring at the board. But here’s the thing. Chapter 9 of Data Science Foundations by Stephen Mariadas and Ian Huke covers the math basics you actually need for data science. And none of it is that hard.
Chapter 1 is titled “In the Beginning” and it’s written by Rick Scavetta. He opens with a tongue-in-cheek Dickens reference, saying it’s just the best of times for data science. But to understand where we are, we need to look at where Python and R came from. Their origin stories explain why they feel so different today.
You know that feeling when you buy fresh ingredients for dinner, and then spend 80% of your time washing, cutting, and peeling? The actual cooking takes 20 minutes. Data science is exactly like that. The cooking is the model. The prep work is this chapter.
The preface of “Python and R for the Modern Data Scientist” sets up the whole book in a few pages. And it does something rare for a tech book. It actually defines what it means by its own title.
I picked up “Python and R for the Modern Data Scientist” by Rick J. Scavetta and Boyan Angelov a while back. It’s an O’Reilly book from 2021, and it caught my eye because it doesn’t pick sides in the Python vs R debate. Instead, it argues you should use both.
You have a great hypothesis. Your stakeholders are on board. But none of it matters without the right data.
Chapter 7 of “Data Science Foundations” by Stephen Mariadas and Ian Huke is about sourcing. Where do you get data? How do you collect it? How do you know if it is any good?
Chapter 6 of Data Science Foundations by Stephen Mariadas and Ian Huke is about something that sounds boring but really is not. Properties of data. What kind of data are you working with? And why does it matter so much?
You got a data science project. Great. But before you touch any data, before you write a single line of code, you need to stop and think. That is what Chapter 5 of “Data Science Foundations” by Stephen Mariadas and Ian Huke is about. The discovery phase. The part most people want to skip. And it is the part that saves you from wasting months on something that never had a chance.
Imagine your company asks you to build a model that predicts health outcomes for people. Sounds great, right? Better treatments, healthier population, maybe even lower costs. But what if your health data gets shared? What if your insurance premiums go up because of something the model found? What if you get denied a service?
Chapter 2 was about stakeholders. Now Chapter 3 asks a very practical question: how do you actually get a data science project done?
You built a model. It works. The numbers look great. But nobody uses it.
Because you forgot the people around it. Chapter 2 of “Data Science Foundations” by Stephen Mariadas and Ian Huke is about exactly that. Stakeholders. The humans who care about your project, fund it, get affected by it, or can shut it down.
You probably watched Moneyball. Brad Pitt, baseball, small team beats the rich guys using numbers. Good movie. But what the movie really shows is something bigger. It shows what happens when you take data seriously. And that is basically what data science is about.
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.
Previous: Scientific Computing with Python and Hadoop
If Python is the general-purpose king of data science, R is the specialized wizard of statistics. While Python is great for building pipelines and apps, R was built by statisticians, for statisticians. In Chapter 5, Sridhar Alla shows us how to bring that statistical power to the massive datasets sitting in Hadoop.
Before you start building complex machine learning models, you need to understand the data you’re actually working with. In Chapter 7 of Data Analytics for Finance Using Python, we go back to the basics: Descriptive Statistics.
Previous: Advanced MapReduce: Joins and Filtering Patterns
Java and MapReduce are great for the heavy lifting, but when it comes to actually exploring data and building models, Python is where it’s at. Chapter 4 of Sridhar Alla’s book shifts the focus to how we can use Python’s massive ecosystem to analyze big data.
Previous: Setting Up Your Hadoop 3 Cluster: A Step-by-Step Guide
Now that we’ve got a cluster running, let’s talk about why we bother with all this complexity in the first place. Chapter 2 of Sridhar Alla’s book takes a step back to look at the big picture of data analytics.