Advancing into Analytics: FAQ’s about the book

I live by a little rule called Mount’s Law of Content Marketing: If I’m asked about the same thing three times, and I could my answer into a blog post, then I should.

In that spirit, this post answers frequently-asked questions about my O’Reilly Media book, Advancing into Analytics: From Excel to Python and R:

Why did you write the book?

Good question! I actually wrote a whole blog post answering this. That post expands on five reasons, which are:

1. To help “past me”
2. To provide a clear learning path
3. To properly situate analytics tools
4. To properly situate analytics techniques
5. To curate my best material

To learn more, check out the original post.

What is the objective of the book?

Reading a technical book is not a light commitment, so I want to provide readers a guarantee about what they’ll get out of Advancing into Analytics. The book’s learning objective, from the Preface, is as follows:

By the end of this book, you should be able to conduct exploratory data analysis and hypothesis testing using a programming language.

Who is this book written for?

This book is written for people who primarily interact with data using spreadsheets (namely, Excel) and would like to learn more advanced data analysis techniques. The Excel audience is huge, but there’s not much content on how they can embrace a fuller data analytics stack by picking up not just tools like Python and R, but conceptual techniques like exploratory data analysis and hypothesis testing.

I think a lot of this has to do with a grudge against Excel from the technical elites, which is such a missed opportunity for them to teach and spreadsheet users to learn. I hope this book can fill that market gap.

Are there any prerequisites?

It sounds ambitious to guarantee a reader will learn to code in R, and Python, and learn statistical techniques like exploratory data analysis and hypothesis testing in a 250-page book.

But knowledge of Excel offers a massive head start in getting into analytics. That said, I do ask the reader to come to the book with some intermediate knowledge of Excel:

• Absolute, relative, and mixed cell references
• Conditional logic and conditional aggregation (IF() statements, SUMIF()/
  SUMIFS(), and so forth)
• Combining data sources (VLOOKUP(), INDEX()/MATCH(), and so forth)
• Sorting, filtering, and aggregating data with PivotTables
• Basic plotting (bar charts, line charts, and so forth)

If you would like more practice with these topics before getting started, I suggest Excel 2019 Bible by Michael Alexander et al. (Wiley). With this foundation assumed, I can help you “pivot” readers’ existing skills into statistics, programming, and related topics.

What’s with the bird on the cover?

(This may in fact be the most common question.)

First of all, animal illustrations on covers are O’Reilly’s thing for reasons explained at this post. My book follows in a great tradition in particular of bird covers: this one the Clark’s Nutcracker (named, in fact, after William Clark of Lewis & Clark fame).

This bird, which is native to the Rocky Mountains, is known for burrowing away thousands of pine seeds. It also has a penchant for being bold around humans and is known as a “camp robber” for stealing food.

I don’t know how much of this was intentional by the illustrator, but the analogies between this bird and the book’s topic is worth blogging about, so stay tuned.

Why do you teach both R and Python?

One common surprise or objection to the book is that it covers both R and Python. I had originally planned just to cover the former in this book, but the editors asked for the latter as well. In retrospect, it turned out well: there was plenty of real estate in the book’s 250 pages to teach both.

Readers are also well-served to know both languages: on a pragmatic level, they’re covering their bases in response to varying work needs. But it’s not just ticking a box: each language has its own unique features, and it’s becoming increasingly common to combine them in building data products, as this upcoming O’Reilly book documents.

Why do you even teach R or Python?

Now, the title of this book is Advancing into Analytics, but R and Python are often usually seen more as data science tools than data analytics. In a world of Power BI, Tableau, and other self-service BI tools, why would an analytics book cover R or Python, let alone both?

I openly admit in the book that some data analysts get away without R or Python in their toolkit. At the same time, data analysts need some familiarity with statistical modeling, and I find these tools the most conducive for doing it.

But it’s not just for statistics — learning to code is a great skill to have, not just as a data analyst but as a modern professional. I know we keep hearing about these low- and no-code tools which use AI and other technology to lower technical barriers, but I remain skeptical — at least in the analytics space.

At the same time, BI tools are increasingly designed to work with rather than instead of statistical programming software. For example, you can use Python in Power BI to manipulate and visualize data, as well as build predictive models.

So I don’t see these skills as superfluous or soon-to-be-obsolete for data analysts.

Where can I get the book?

The book is available in both paperback and digital versions at most major booksellers, as well as O’Reilly Media’s Online Learning subscription platform. Learn more about how to access the book here.

How can I support the book?

Well, I don’t know this is an FAQ 😼, but if you are so inclined to spread the message of the book, I offer five ways to support Advancing into Analytics without even buying it:

1. Order it from your library
2. Promote it synchronosly
3. Promote it asynchronously
4. Leave a review
5. Subscribe to my newsletter

To get the specifics, head to the post.

Did I miss a question?

These are the questions I’ve heard repeatedly so far, but do you have others? You’ll need two of your friends for me to write it here, in the spirit of my Law of Content Marketing… just kidding. Please, ask away in the comments, or by contacting me directly.

I look forward to sharing this book with the community.

Advancing into Analytics is now available.