The Bean Counter Strategy: How taking an accounting course will boost your data scientist skills 

Cartoon of Jake carrying a very large quarter and his small piggy bank freaking out at the idea of fitting it inside by Philip Riggs.

Alcatraz Island in San Francisco Bay, California, started off in humble origins. It was planned simply to house a lighthouse. 

But it’s isolation made “The Rock” perfect to be set apart as a prison with no easy escape. First, it was used for military prisoners, but in 1933 it changed to being the best place to hold notorious bank robbers and murderers who caused trouble at other prisons.

That isolation and housing of the most notorious of criminals has given Alcatraz a certain mystique. 

Like Alcatraz Island, our data organizing skills also start out humble

We may pick up some ways of building tables in a database, create summary tables using SQL, and make a few graphs or tables. But to set yourself apart in the marketplace, you need to develop these skills to a higher level. But how?

If you want to create your own Alcatraz mystique as a data wrangler, set yourself apart by learning accounting. 

Wait … what? Why accounting instead of a database course?

You might think that taking a database course would help your data organization skills

But I’ve found that accounting has helped me develop data organizational skills better than a database course. That may not make sense. After all a class in database design should teach you everything you’d want to know about database structure.

Here’s the issue I’ve run into. Database people are looking to optimize storage. That means a heavy dose of normalization … breaking apart tables into smaller tables to minimize the amount of repetition in the data. 

Don’t get me wrong. I think it’s important to maintain lookup tables for certain information. It makes updating values easy, and it helps reduce errors in the data.

But when it’s time to perform analytics, you have to reassemble all those tables back into a single data table

That’s why all that emphasis on storage isn’t where a data analyst should be spending their time. For most projects I work on, the data isn’t large enough to worry about minimizing the storage footprint or reducing repetition. The important factor is making the data easily accessible for analysis.

In contrast to the database focus on normalizing data and optimizing queries, I’ve found my background in accounting more useful. 

Long before I was a data scientist, I was an accounting major

Accounting will teach you how to find and correct data entry errors, summarize data in better ways, and present data mre effectively.

And I actually graduated with an accounting degree. I used to think that accounting degree was a waste of time. But over time, I’ve realized just how much of that accounting knowledge all the time in my data wrangling. 

What in the world can accounting teach you that a database course doesn’t?

Accounting teaches you to organize and summarize data in a very applied way

In accounting, you must keep a record of transactions. Everything you do is kept in a table of transactions. It is the raw data of your business finances. Keeping that record teaches many important data skills. Here are a three skills I know I use very often … 

First, you learn about different types of data entry errors

Some of the accounting data entry errors are transposition of numbers (the error of switching numbers from the correct order), errors of omission (leaving an entry out), errors of commission (when a transaction is recorded more than once, creating duplicate entries), and other data entry errors. 

Hunting for penny differences teaches you to spot mistakes.

Second, you learn about summarizing data

It’s easy to push a button in a database and product a general report. But it’s a whole other skill to take raw data and transform it in several different ways to get answers to focused questions. 

In accounting, you use the list of transactions to pull out specific information for cash flow statements (how money comes in and goes out), income statements (how much income and expenses you have), and balance sheets (your assets, liabilities, and equity). 

Third, you learn about data presentation

All the data summaries above are presented in a well-formatted table. On top of tables, you can create graphs. That’s the visual format, but you also learn about condensing a lot of data into a few items that give a good overview of what happened in a time period. 

I find these skills to be in high demand from researchers I work with

They don’t have that accounting foundation and all the skills that are developed in accounting courses. They want accuracy in data, fixes to data that are wrong, why data are wrong, good summaries of what is in the data, and those summaries presented in a user friendly way … all skills I developed in accounting.

And all skills most researchers haven’t developed to as high a level, if at all.

So, do your data scientist career a favor … take an accounting course

You will develop a targeted, valuable set of skills that can set you apart from the crowd. When it comes time to find an error, summarize data, or present data, you’ll have a unique skill set that contributes a lot to any project you’re on. And in the process, you’ll create a data wrangling mystique that sticks into peoples’ minds like Alcatraz.


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