Python All-in-One vs Data Science Programming All-in-One For Dummies: Which First?

If you want to learn to code so that you can work with data, two For Dummies books will turn up in every search: Python All-in-One For Dummies and Data Science Programming All-in-One For Dummies. Both are big yellow All-in-One volumes, both cost about the same, and both mention Python and data science on the cover. They are not interchangeable, though, and buying them in the wrong order is the most common way to stall in chapter two.

The short version: Python All-in-One teaches you Python and then shows you what you can point it at, data included. Data Science Programming All-in-One assumes you can already write a little code and spends its pages on the analysis itself, in Python and R. If you are starting from nothing, the first is the first book.

The two books side by side

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What Python All-in-One For Dummies actually is

Python All-in-One For Dummies is a Python book first and foremost. Its seven mini-books run from getting started and the language's building blocks, through working with libraries, to artificial intelligence, data science, talking to hardware and building robots. The early sections assume you have never written code; the later ones are there for when you want to do something with it.

The 3rd edition came out in April 2024 and adds AI-assisted coding with GitHub Copilot, which makes it the more current of the two by four years. That matters less for the core language, which changes slowly, and more for the tooling chapters.

What it is not is a data science course. The data science mini-book is one seventh of the volume, enough to show you the shape of the work and the main libraries, not enough to make you comfortable building and checking a model.

What Data Science Programming All-in-One For Dummies actually is

Data Science Programming All-in-One For Dummies is the opposite way round. It treats programming as the tool and data science as the subject. Across 768 pages it works through getting data into shape, visualising it, and then the actual modelling: linear and logistic regression, machine learning, neural networks, recommender engines and cross-validation, with a running thread on how to tell a clear story with the results.

It also covers both Python and R, and helps you decide which suits which job. That is useful in practice, because plenty of statistics and research work still runs on R, and a book that only knows Python will not prepare you for that.

The trade-off is age and pace. It was published in December 2019, so some library examples will have moved on, and you should expect to check current documentation when code does not run as printed. It also moves faster than a beginner's book. You will get much more from it if loops, functions and importing a library already feel familiar.

Which should you buy first?

You have never written code and want to work with data: Python All-in-One first. Learn the language properly, use its data science mini-book as a taster, and come back for the data science book once you can write a short script without looking everything up.

You can already write basic Python and want to do real analysis: Data Science Programming All-in-One. You will skip far less, and the regression, machine learning and model-checking chapters are where its value is.

You will be working with R, or alongside people who use it: Data Science Programming All-in-One. It is the only one of the two that teaches R at all. If R is the main language rather than a second one, the R All-in-One For Dummies goes further into it.

You want Python for automation, the web, hardware or AI tools rather than analysis: Python All-in-One, and you may never need the other.

You are changing career into data: buy both, in that order. Between them they cover the route from first line of code to a working model, and they share the same format, so the second feels like a continuation rather than a fresh start.

How long each takes to be useful

Neither is designed to be read cover to cover. With Python All-in-One, the first two mini-books are the core; work through them properly, typing every example, and most people can write useful small scripts within a few weeks of regular evenings. The later mini-books are references you dip into when a project needs them.

With Data Science Programming All-in-One, the payoff comes from working its examples on a real dataset of your own, a spreadsheet from work, your bank export, or a public dataset. Reading about regression teaches you the words; fitting one to data you understand teaches you the idea.

If you are not sure data is your goal

If you are still deciding what you want to do with code, neither of these is the best first book. Our programming books for beginners guide compares the broader starting points, including Coding All-in-One and Beginning Programming All-in-One, and explains how to choose by goal rather than language. You can see every book we list on the programming books page.

Frequently asked questions

Is Python All-in-One For Dummies good for complete beginners? Yes. The early mini-books assume no programming experience and build up from the basics, so it works as a genuine first book as well as a reference later on.

Can a beginner start with Data Science Programming All-in-One For Dummies? You can, but it is harder going. It explains data science from the ground up, yet it moves quickly through the programming itself. Most beginners do better learning basic Python first, then coming to it.

Does Data Science Programming All-in-One For Dummies teach R? Yes. It covers both Python and R and helps you decide which suits a given task. Python All-in-One covers Python only.

Is Data Science Programming All-in-One For Dummies out of date? It was published in December 2019. The statistics, modelling ideas and workflow have not changed, but some code examples rely on library versions that have moved on, so expect to check current documentation now and then.

Which edition of Python All-in-One For Dummies is the latest? The 3rd edition by John C. Shovic and Alan Simpson, published in April 2024, which adds AI-assisted coding with GitHub Copilot.

Do I need both books? Only if you want to go from no coding to doing data science properly. If you just want to automate tasks or learn Python generally, Python All-in-One is enough on its own.