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Stringfest Analytics

Analytics & AI for Modern Excel

Advancing into Analytics: From Excel to Python and R (O’Reilly)

On this page you can learn more about my book with O’Reilly Media, Advancing into Analytics: From Excel to Python and R. If you are a spreadsheet user looking to level up your data analysis skills, this book is for you.

Advancing into Analytics Cover Image

If you’d like to host a custom workshop based on the book at your organization, have me present at your meetup, or use it in the classroom, check out these pages:

Where to buy

The book is now available in paperback and as an ebook at these and other booksellers:

Reviews, please ⭐⭐⭐⭐⭐

Whether you pick up a paperback, read on your Kindle, or head to O’Reilly Online Learning, please leave a book review. This helps tremendously with the book’s success. If you’re not sure what to include in your review, here are some ideas:

  • How did you benefit from this book?
  • Who else could benefit from this book and why?
  • What did this book offer that other resources have not?
  • What are two or three takeaways you have after reading?

Book description:

Data analytics may seem daunting, but if you’re familiar with Excel, you have a head start that can help you make the leap into analytics. Advancing into Analytics will lower your learning curve.

Author George Mount, founder and CEO of Stringfest Analytics, clearly and gently guides intermediate Excel users to a solid understanding of analytics and the data stack. This book demonstrates key statistical concepts from spreadsheets and pivots your existing knowledge about data manipulation into R and Python programming.

With this practical book at your side, you’ll learn how to:

  • Explore a dataset for potential research questions to check assumptions and to build hypotheses
  • Make compelling business recommendations using inferential statistics
  • Load, view, and write datasets using R and Python
  • Perform common data wrangling tasks such as sorting, filtering, and aggregating using R and Python
  • Navigate and execute code in Jupyter notebooks
  • Identify, install, and implement the most useful open source packages for your needs
  • And more

Learning objective

A good product expresses and delivers on a promise to its audience. To that end, I include a learning objective in the preface of Advancing into Analytics.

By the end of this book, you should be able to conduct exploratory data analysis and hypothesis testing using a programming language. Exploring and testing relationships is core to analytics. With the tools and frameworks you’ll pick up in this book, you will be well positioned to continue learning more advanced data analysis techniques.

We’ll be using Excel, R, and Python because these are powerful tools, and because they make for a seamless learning journey. Few books cover this combination, even though the progression from spreadsheets into programming is common for analysts, myself included.

Prerequisites

To meet the book’s objectives, I make some technical and technological assumptions:

Technical Requirements

Advancing into Analytics was written on a Windows computer with the Office 365 version of Excel for desktop. As long as you have a paid version of Excel 2010 or greater for either Windows or Mac installed on your machine, you should be able to follow along with the majority of the instruction in this book, with some variations, particularly with PivotTables and data visualization.

R and Python are both free, open source tools available for all major operating systems. I address how to install them later in the book.

Technological Requirements

This book assumes no prior knowledge of R or Python; that said, it does rely on moderate knowledge of Excel to flatten that learning curve.

The Excel topics you should be familiar with include the following:

  • 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 moving on, I suggest Excel 2019 Bible by Michael Alexander et al.

Table of contents

I. Foundations of Analytics in Excel

1. Foundations of Exploratory Data Analysis
2. Foundations of Probability
3. Foundations of Inferential Statistics
4. Correlation and Regression
5. The Data Analytics Stack

II. From Excel to R

6. First Steps with R for Excel Users
7. Data Structures in R
8. Data Manipulation and Visualization in R
9. Capstone: R for Data Analytics

III. From Excel to Python

10. First Steps with Python for Excel Users
11. Data Structures in Python
12. Data Manipulation and Visualization in Python
13. Capstone: Python for Data Analytics
14. Conclusion and Next Steps

What people are saying:

Selfie of author with book
I’m obviously pleased with the book, but don’t take my word for it! Check out these reviews:

This book is uniquely poised to be used as both a reference and a primer on business and data analytics.

Aiden Johnson, Data Scientist & Mentor at Breakthrough Data Science

George lays out exactly what you need to do to bridge from Excel into data science and analytics.

Jordan Goldmeier, Microsoft Excel MVP and Head of Anarchy Data

I benefited from this book by learning the technical progression from Excel into Python and R languages. I also took conceptual knowledge of statistics into these playgrounds.

Joe Balog, Data Analyst at joesphbalog.com

The book was written in a way that helps readers transfer their knowledge about data into new tools and techniques in an approachable manner.

Nicole LaGuerre, Learning & Development Consultant at s1mplythebest.com

Not only will the reader become comfortable with using the most popular data analysis programming languages, they will also learn about techniques to wrangle, explore, and visualize data so that it comes to life and tells a story.

John Dennis, Data Analytics & Data Science Consultant

I have been looking for the most direct line to acquire the essential skills required to perform in a data analytics/data science role at an affordable price point.  This book provides the closest thing to the roadmap that I’ve been looking for.

Barry Lilly, Claims Team Manager at Liberty Mutual Insurance

This book is a gold mine for anyone who wants to level up their analytics game. It’s an excellent review of major analytical concepts and also a great starting point for learning how to munge data programmatically using R or Python.

Tobias Zwingmann, Senior Data Scientist and Co-founder of RAPYD.AI

Companion repo

The companion repository for the book is publicly available on GitHub. This contains all datasets, workbooks, scripts, exercise solutions, and other files used in the book.

You can download a compressed folder of the files or, if you are familiar with GitHub, clone it to your computer.

Updates/errata

5/19/2021 (This issue appears to have been resolved by Microsoft): “Mr. Excel” Bill Jelen heard reports of a bug affecting the Data Analytics ToolPak (used in the book) and kindly pointed to an alternative: the XLMiner Analysis ToolPak. This free add-in includes all menus and features as used in the book.

Watch the following video to learn more about XLMiner. Thanks, Mr. Excel!

9/1/2021: Issue with Figure 1-11: this screenshot of the Descriptive Statistics menu in Excel should have had “Summary statistics” checked on. It was not which would cause an error. Thanks MG for pointing this out! The screenshot has been redone on O’Reilly’s website.

Translations

Learn more:

Check out the following posts to learn about the book, why I wrote it and how I designed it:

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