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 9 of Laura Funderburk’s book takes a step back from building things and looks forward. What’s coming next for NLP and LLM systems? Where are the bottlenecks? What’s changing?
Chapter 5 is where Boyan Angelov gets practical about the question everyone dances around: which language should you actually use for which job?
You learned the individual tools. You learned the deployment strategies. Now Chapter 11 of Data Engineering with Python by Paul Crickard puts it all together. This is the chapter where you build a complete, production-grade data pipeline from start to finish.
This is where everything from Chapter 8 comes together. We’ve built NER pipelines, text classification tools, sentiment analyzers. Now Funderburk wires them into a multi-agent graph that can handle complex queries end to end.
Nobody ever got promoted for building the cheapest data pipeline. But plenty of people have gotten uncomfortable phone calls from their CFO after a runaway BigQuery bill. Chapter 11 is about the money side of GCP, and I think this is one of the most practical chapters in the book.
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.
In 2016, hackers stole personal data of 57 million Uber users and drivers. How? Someone left API credentials in a private GitHub repo. The attackers grabbed those keys, got into AWS, and downloaded everything. Uber didn’t even notice for a year. When they finally found out, they paid the hackers $100,000 to delete the data and kept quiet about it.
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?
In Part 1 we covered how data governance breaks into three pillars (usability, security, accountability) and went through metadata, Dataplex search, access control in BigQuery, and the Sensitive Data Protection service for finding PII. Now let’s pick up where we left off: understanding what SDP actually finds, and then moving into the accountability pillar.