George Mount - Advancing into Analytics: From Excel to Python and R

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, Isuggest Excel2019 Bible by Michael Alexander et al. (Wiley).

Like many in our field, my route to analytics was circuitous. In school, mathematics became a subject I actively avoided; too much of it seemed entirely theoretical. I did have some coursework in statistics and econometrics that caught my interest. It was a breath of fresh air to apply mathematics to some concrete end.

This exposure to statistics was admittedly scant. I attended a liberal arts college, where I picked up solid writing and thinking skills, but few quantitative ones. When I got to my first full-time job, I was floored by the depth and breadth of the data I was entrusted with managing. Much of this data lived in spreadsheets and was hard to get much value out of without intense cleaning and preparation.

Some of this data wrangling is to be expected; the New York Times has reported that data scientists spend 50% to 80% of their time preparing data for analysis. But I wondered if there were more efficient ways to clean, manage, and store data. In particular, I wanted to do this so I could spend more time analyzing the data. After all, I always found statistical analysis somewhat palatablemanual and error-prone spreadsheet data preparation, not so much.

Because I enjoyed writing (thank you, liberal arts degree), I started blogging about tips I picked up in Excel. Through good grace and hard work, the blog gained traction, and I attribute much of my professional success to it. You are welcome to stop by at stringfestanalytics.com; I still post regularly on Excel and analytics more generally .

As I began to learn more about Excel, my interest spread to other analytics tools and techniques. By this point, the open source programming languages R and Python had gained significant popularity in the data world. But while I made my way through grasping these languages, I felt unnecessary friction in the learning path.