How To Build Your Data Science Career Change Action Plan

White typewriter and coffee

Your Job’s A Joke. You’re Broke. Your Love Life’s DOA.

You’ve spent the last ten years grinding it out at a job that you don’t exactly swoon over when the alarm clock goes off. It’s starting to feel like life is passing you by.

And yet, you know there must be something out there that you can really get your teeth into. But which direction do you go to find it?

Looking for a new life…in all the right places

So you read some articles online about Data Science being the sexiest new career around. Well, since the last sexiest new career anyway.

And it starts to sound interesting. Who wouldn’t want to find out more and see if this whole data world could open up for them?

I’ll be honest now. Coming into the data field at age 30 or above without any prior experience or tech background can be tricky. To short-circuit the whole “how do I even get started?” problem I always suggest planning backwards.

What is Backwards Planning?

Backwards Planning is a great concept I learned from Amy Hoy and Alex Hillman from Essentially it means looking at where you want to get to as an end product (e.g. writing a book, building a data product…hooking a data analyst job), and working backwards from there.

For example:

  • What does the end result look like?
  • What’s essential and what’s nice to have?
  • What do I need to prepare up front?
  • What has to come first? Etc. etc.

All questions to take you backwards from your end goal to where you are now so you can fill in the blanks in between. (You can read more about using Backwards Planning in Amy and Alex’s book Just Fucking Ship).

When it comes to approaching a career change you need to look at getting that new data analyst job as your ultimate end goal. If you know your end goal then you can plan backwards to find the steps you need to take between there and now to get you to that place.

These five steps in fact.

The 5 Step Guide To Backwards Planning Your Career Change.

1) Research your targets

  • First up, go to a jobs site like Linkedin or Indeed and search for “junior data analyst” jobs in your hometown.
  • Go through the wordy job descriptions and make notes on what they are asking for.
  • Pay particular attention to the technical skills, education and soft skills sections.

When you’ve got enough data together, you can start to pick out the actual skills and requirements that will get you in the door at more of these jobs.

Got those skills already?

Great, you’re one step ahead. If not then it’s time to work out how to get them.

2) Technical Skills

I’ll go out on a limb here but you will probably see Excel and SQL pop up most often in Required Skills. These are the fundamentals of working in a BI or analytical role in the vast majority of companies, large and small.

If you don’t have these skills then you know where to start your learning. You won’t have to be the world’s best Excel jockey or SQL superstar either.

You’d be surprised how many people get tripped up at the first hurdle on these entry requirements. Make sure that isn’t you.

Excel and SQL really are two givens in terms of necessary knowledge in the broadest range of companies so it makes sense to pick those off first.

More bang for your buck.

At this level there’s no sense in spending a fortune on training when you have so many excellent courses available for free (including my own FREE SQL Crash Course).

I’ve put together a list of the best free and paid online courses for learning SQL from scratch. Check it out and save yourself a LOT of legwork: Best SQL Online Courses, Training and Tutorials.

Have a look on there and jump on in. I’m a big believer in getting the Pareto Principle and making sure you get the basics right.

If you can nail 80% of the most important SQL commands or Excel functions you’ll be streets ahead of the entry level competition.

Even those graduates from fancy colleges. In fact, ESPECIALLY those graduates from fancy colleges.

Don’t I need to know how to code from Day One?

You can get into a coding language like Python or R afterwards. Having a curiosity to dig into datasets, thinking of questions and then finding the answers from the data is far more important than listing 20 different programming languages at this stage of a career change.

Those of us who scan CVs and resumes when hiring analysts can tell immediately that you are a bullshit artist if you rhyme off a shopping list of programming languages and database technologies. Especially if you are straight out of university.

Better to know the important ones well.

3) Education

Do you need a 2 year college course or bootcamp in Analytics to learn Excel and SQL? No. Not at all.

Despite that, not having even a bachelor’s degree will tend to be a barrier to getting in the door of many organisations. I don’t agree that they SHOULD require it.

I’ve worked with plenty of excellent folks who don’t have them and it hasn’t stopped them being damn fine analysts but HR departments often disagree. If you don’t have one, search out the jobs that don’t ask for it and see what they want instead.

Or just apply anyway. Let them weed you out if they want but don’t rule yourself out unnecessarily.

Actually, STOP and write that last piece of advice down right now. It’s a big one.

In fact, that’s probably the most important piece of advice I can give for your job search. Companies employ masses of HR people who are only fit to scan for buzzwords on application forms. If they have a strict policy like only hiring graduates for entry level analyst jobs or only hiring from top level colleges then it’s their loss.

Angela Bassa is the Director of Data Science at iRobot. She covered this very subject in a Tweet thread about how she wouldn’t have passed the Master’s or higher graduate-level degree bar herself on a team that SHE set up. And she went to MIT!

It’s a ridiculous example of artificial gatekeeping. But in a crowded job market it happens.

Not all hiring departments are the same.

Some companies are a little more forward thinking however. They give their recruiting managers a little more leeway when it comes to who they bring in for an interview.

As more people change careers in their 30s, 40s and 50s than ever before, it makes sense to not rule out picking up a diamond that may have otherwise been discarded.

TOP TIP: Don’t limit yourself by deciding you aren’t fit to apply. That’s someone else’s job, not yours. Fortune favours the brave. If that’s you then the next step will be where you can really shine.

4) Soft Skills

Let’s say you’ve worked as a chef. If there is a more highly stressful, on-demand, pressurised working environment I’ve yet to see it. You should have learned plenty of skills running your kitchen that can transfer over to a BI analyst role.

See what the job listings ask for (e.g. “Ability to be self-managed, work independently as well as to collaborate within an agile, fast-paced, dynamic team environment.”).

Things to think about in your own background are:

  • Have you ever worked with other people?
  • Have you worked on something on your own?
  • Been under pressure on the job? How did you deal with it?
  • Was there something broken in the process you were meant to follow? How did you fix it?

These are the kind of open-ended questions that really show how you will deal with situations in the real world. It’s not all about the technical chops, especially for folks with some previous life experience in other arenas.

You mightn’t be able to quote the exact dictionary definition of what a SQL window function is but if you can show me you know how to Google things, I’ll respect that more.

Tell us a little about yourself.

Prepare some examples of when you have displayed these soft skills in your career and life to date. Don’t be afraid to use non-work related examples either.

I’ve had people use their experience in sports clubs or other organisations to display their soft skills at interview. I’m always happy to explore those as it shows they can transfer the skills beyond just the office work environment.

NOTE: I should say that I hate the connotations of these highly valuable skills being termed “soft skills”. It’s as if they are an afterthought or not quite as useful as the (presumably) “hard” technical skills of programming and working with databases.

Anyone who has ever had to manage a team of other grown-up human beings will know this is horse shit.

5) Show us what you got

When you’ve built up your skills a little, it’s time to show people what you can do.


Write blog posts documenting how you approached a project. There is a common misconception that, seeing as everything in the world has already been written about, there is no point you doing the same thing.

That. Is. Bullshit.

Write about what you have learned regardless of how basic you think it is. Get used to communicating your process and what you’ve found.

HINT: these are REALLY important skills to have as a professional data analyst.

Analyze this, analyze that.

Or get a dataset, analyze it and present what you’ve found. (Just don’t use pie charts. Please.)

Interested in sports? You’ll find plenty of sports data online, jump right in.

Got a book on your coffee table full of the US Government’s notes on Italian-American gangsters of the mid-20th century? Source the data, cleanse it, wrangle it and analyze it (just like a project I’m currently in the middle of writing up).

Learn from the experts.

For your online portfolio, Google and read as much as you can from other data science folks who have posted theirs online. Unfortunately a lot of us aren’t able to discuss actual projects we’ve done on the job due to NDAs etc. but there are still a lot of great resources out there to pick over.

See what the format is like. How did their voice come over in the writing? How did they present it? Github/Jupyter Notebook/R Markdown?

It’s all research, all analysis. Even the searching and researching is building your data skills. Learn from everyone, pick the best elements and plunder all you can to learn more.

The Final Pep Talk.

It might look like a lot of work but this is a great field to get into. Career progression through the many different areas of data science is good and well compensated. Getting a foot in the door is the hard part but it’s well worth it and there is no better time to start than today.

Overall, I’d say just work hard on picking up just enough new skills to be able to demonstrate you can use them. Don’t wait until you consider yourself an expert to get applying for jobs.

The real learning, as in any field, is done on the job as you are adapting to real world situations. And, on that front, in the immortal words of The Carpenters, “we’ve only just begun…”

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