How to Tell if Data Analytics is the Right Career For You

Data Science and Analytics are still buzzing, going on 10 years, as the “sexiest” job of the 21st century. It’s well paid, it’s interesting, and there’s a ton of demand for talent. As a result, it’s still attracting tons of people interested in breaking into the field. But a common question is … how do I know if I’ll like it?

As someone who has been working in analytics and data science for 5+ years, has a masters degree in data science, and pivoted from a career path that I didn't enjoy (marketing), here is my view.

If you can honestly say “yes” to all of the following, then there is a good chance you’ll enjoy working in this field.

Do you like math?

And how do you feel about learning new math? (If you haven’t already taken college-level courses in statistics, and maybe calculus and linear algebra.) There are some folks who claim that you don’t need to be “good” at math to do analytics, because “the computers do all the math for you!” While that’s true —most of us aren't writing out mathematical proofs on the job — you still need to be comfortable enough with math to know which math to tell the computer to do. You need to be able to look at a data set and figure out what is the numerator and what is the denominator for the rate or percentage you need to calculate. If you want to do any machine learning, you need to have a grasp of the math behind your code, and also how to interpret the performance of your model — which is all evaluated using math.

Do you like solving problems?

Even with vague or no instructions? The job of a data analyst or data scientist isn’t to write SQL code or Python code or create machine learning models.

The job is to solve problems.

SQL, Python, machine learning — those are just tools. Tools that you use to do your job. But they are not your job.

All day, every day, you will be bombarded with people who need your help to solve their problems. It can get overwhelming at times, even for someone who likes to solve problems.

A lot of times they won’t even identify the correct problem to solve. So you need to first figure out what is the problem that I’m even solving, and then how do I do it?

If you think the job is merely jumping into writing code immediately, with clear instructions or goals… you’ll be in for a surprise.

When solving a problem, do you look at it from all angles?

When you are presenting your solution/insights/whatever to your stakeholders, or to leadership, you should be prepared for someone to ask “well did you look at XYZ?”

There are usually multiple ways to solve a problem, and a good analyst or scientist will consider all of those when determining the best solution. Because if you don’t, someone you're presenting to will ask if you did. And it’s a bad look if you routinely say “well, no…” It’s a better look to say “yes, and it wasn’t as good as [this solution] for XYZ reasons.”

Do you find that you are a very literal person?

This job is very literal. It’s very black and white. That’s not to say there is only ever one right answer for every problem — no, the job is finding the best answer of all the possible answers, considering various tradeoffs. But if you are very literal and straightforward, then you might enjoy this work. This is one of the reasons why I personally enjoy working in this field more than I did working in marketing.

Have you ever used Excel or Google Sheets — did you enjoy it?

Spreadsheets are often the gateway to analytics. It’s something that anyone with a computer can access and start digging in and learning. If you’re someone who enjoys using spreadsheets, and you’ve been able to figure out on your own how to do some cool things — pivot tables, visuals, formulas, etc — then that’s a good sign that you’ll do well in this field.

If you’ve never used spreadsheets, my recommendation is to create a personal budget for yourself in Excel or Google Sheets. Download all your spending data, clean and join it together, then categorize everything, aggregate it in a way that is useful for you, create visuals that tell a story, and then reflect on what insights you’ve gained from the process.

Not only is this a good, basic, end-to-end analytics project, but if you can figure it out all on your own and create something useful with clear insights, then that’s a good sign you’ll enjoy this work.

Do you figure things out on your own?

When faced with a problem or question, do you dive in and try to figure out the answer on your own, or do you wait for someone else to do it for you?

Finding answers and solutions is the job. Sometimes the job starts a step before that, and you have to figure out the questions and problems as well.

People come to you with vague problems. Or they don’t, but you identify problems to solve. And then you have to solve them. Usually, you have to figure out the solution on your own, there often isn’t an example or tutorial that you can follow exactly. If you intuitively are already the type of person who will seek out your own solutions or answers, then that’s another sign that you will do well in this field.

If you’re the type who will wait for someone else to find the solution, or will ask someone else for the answer or solution before seeking it out on your own, or you just give up pretty quickly, then you might have trouble succeeding in this field.

Can you handle a lack of structure?

Many folks interested in data analytics or data science come from a software engineering background. Typically, an engineering team has a lot of structure — they work in 2-week sprints, and everyone has a clear plan for exactly what they are going to do during those 2 weeks. There is structure around how work is prioritized, how problems are solved, how work is presented, how things are tested and validated, what kind of milestones to set, etc.

Many analytics and data science teams lack that kind of structure.

I’ve worked on teams that have planned by quarter, by 6-month cycles, and by year. Never shorter time periods.

Additionally, a lot of the projects and tasks are brand new. No one on the team has ever tackled a project like that before. You have to figure out the scope, timeline, deliverables, milestones, etc. And then hold yourself accountable to all of those.

Do you mind staring at a computer for 8 hours a day?

I know it’s obvious, but there is no other way to do this job. I say this because I occasionally see folks interested in transitioning from teaching or a blue collar role, and for some folks, the thought of sitting for 8-ish hours per day, 5 days per week, and staring at a screen doesn’t sound great. But it is what it is.

What do you think? Do you work in analytics, and do you agree or disagree? Anything else you’d add? Any other questions about a career in analytics?

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