Data Smart, 2nd Edition

11Moving on From Spreadsheets

When the first edition of this book came out in 2013, there was an ongoing battle in the data space. You see, if you were in any quantitative field, your options were limited in terms of software. Many people and organizations used Excel, not because everybody loved it (though executives surely did—see the Introduction for more), but because Excel was immediately and easily accessible. If you wanted to do quantitative programming, your free and cheap options had been limited until recently: you could use SAS, MATLAB, or SPSS if you worked at an institution or company with money and resources; or Java and Octave if you needed something free (or just wanted to be different). Excel was considered the best option by executives albeit not necessarily the favorite among quants if you wanted to throw something together quicky.

But near the start of the 2010s, attention began to focus on two major free languages that could seemingly do more than Excel—R and Python. These languages have built‐in version control, collaborative development, and the ability to create actual software beyond a spreadsheet file.

You can’t do that in Excel. In the original edition of this book, John Foreman argued that it’s time to move on to bigger things. In the early 2010s, Excel was barely a competitor to other languages. Back then, you were an Excel person, a Python person, or an R person. You planted your flag in one preferred language.

Today, we see the value of all of ...