Analytics Tool Belt

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by Billy Caughey

Grandpa’s Tool Belt

Growing up, I worked with my Grandpa in residential HVAC. On the first day of the job, I arrived at Grandpa’s workshop at the designated hour. On the workbench was a sturdy, but well used, tool belt. Already in the tool belt were the tools I would need to be successful in residential HVAC: claw hammer, panning hammer, screwdrivers, tin snips, a metal marker, measuring tape, screws, and nails. Grandpa told me I would be able to do my job if I remembered to take my belt with me.

That lesson has stayed with me throughout the years. Each place I have worked, endeavor I have taken, and goal I have chased have all required a tool belt to be successful. Analytics is no different. Some of the biggest tool belts I have ever seen have been worn by those engaged in analytics. Due to the depth and width of analytics, I would argue no two tool belts have the same exact tools. With that said, almost all tool belts have some basic items.

Basic Analytics Tool Belt

Much like the tool belt my Grandpa gave me, I’d like to point out some tools you will need in your tool belt to be successful. These tools will be general and seem basic, but I promise you these tools carrier a lot of power. I would argue these tools will get you through 60-80% of the work you are going to do. The other 20-40% is most likely going to be where your specialized tools come into play.

The first tool I would suggest are descriptive statistics. Yup, the measures of centrality and dispersion. These tools are tried and tested. When I crack open a new data set, one of the first tools I reach for are these measures of centrality and dispersion. It’s a quick way to understand where data is centered, is there a skew, is there a lot of deviation, and other big questions get answered with these tools. 

The second tool are data visualizations. One of the fastest ways to share information is through a visualization. Communication through simple visuals like histograms, scatterplots, and line charts is universal. If done correctly, a visual can be handed to any domain leader and the information communicated. Additionally, visualizations lead to a lot of answers and, more importantly, a lot more questions.

The third tool, known battle wagons in analytics, are regression and correlation tools. A lot of analyses have been performed by computing regressions. These tools related several variables to each other and try to explain how they work in concert. If you don’t know what these tools are, never fear! There are loads of places to learn these tools. I’m also fairly certain these topics will be covered in this blog as well.

The final tool is a programming language. In order to do analytics in today’s world, a computer is necessary. Learning a programming language allows you to interact with huge data sets and resource. The tools we listed before can be performed in most programming languages. For the purposes of this blog, I recommend one of two languages: R or Python. Both of these languages are open source and have great communities to interact with. If you haven’t worked with either of these languages before, I would tell you to try them both. The one which feels right and you work best with, is the right language for you!

Here we talk about the other 20-40% of the tool belt. This portion of the tool belt is dependent upon your area of expertise. You will need to do some ground workout to understand the tools used in your area. Once you figure these out, master them. Make them so familiar that when you need them, you can just pull them out ready for work!

Conclusion

In order to be successful at analytics, you need to have a tool belt. Some of the tools may be the same as others you with. Some of the tools will be completely different. No matter what area of expertise you work in, there is a tool belt with all the necessary tools waiting for you!

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