How to transition into data analysis

How to transition into data analysis

A guide on how to transition into data analysis when you are coming from a non-technical background (e.g. marketing).

Thinking about transitioning into data analysis is daunting

One of the most frequent questions I get asked is how to break into data analysis. Or, more specifically: How do you make the transition into data analysis, especially when you do not have a formal technical background? That is, when you do not have a background in CS/maths/statistics, but instead, you have, for example, a marketing background.

As I, too, have been there, I know how daunting the journey into data analysis can seem. There are tons of other questions you’ll ask yourself:

  • What resources should I choose to learn the necessary skills?
  • Do I need to learn to code? Does it really take years to get up to speed before I can land my first job?
  • I’ve read that I should build a portfolio of data projects. Is it really necessary? And what if I can’t find any projects?
  • Do I even stand a chance against candidates with a more technical background?

Not knowing the answers to those questions can be incredibly frustrating because it presents a huge roadblock to get into data analysis.

And repeatedly asking yourself said questions results in you thinking: “Well, those are huge tasks and steps; I should have started a year ago!”

And guess what: you end up doing nothing… until another year passes and you once again ponder: “I want to get into data analysis, but I probably should have started the journey a year ago…” and so on and so forth. This is an endless and frustrating loop resulting in you not making a move towards data analysis.

Imagine if you knew exactly what the steps are to transition into data analysis

Let me tell you a secret: Your lack of action is caused by not only your uncertainty about what to do, but also because these seemingly difficult steps are somewhat intimidating. If you had a clear sense of precisely what steps to take and how they would look, your path to data analysis would be simple and clear! And you would know…

  • What skills you need so you wouldn’t waste any time on irrelevant ones
  • How to find side projects to build your portfolio
  • And, how to position yourself to compete against peers with a more technical background

Imagine if you had a “battle plan” (i.e. guide) telling you how to get into data analysis!

A couple years ago, when I transitioned from being a marketer into a more data focused role at Google as a Data & Insights Lead, I wish I had such a guide to help me get there faster and eliminate any confusion. Unfortunately, it did not exist back then and I had to find my way through trial and error.

I am more than happy to share the path that worked for me. It is a blueprint you can replicate for your own journey into data analysis.

So let’s see what the battle plan includes.

A three-way approach for transitioning into data analysis

The “battle plan” consists of three distinct areas which we’ll discuss step-by-step. First of all, I’ll tell you what data analysis skills you need and how to acquire them. Secondly, I’ll show you how to find side projects to which you can apply those skills and build a portfolio. And lastly, we’ll talk about how you can play up your strengths and position, for example, during interviews for analytical roles.

1. Learn the relevant skills

What skills should you have when you want to start a career in data analysis? When you don’t have a technical background and want to transition from another field, the answer is not obvious.

Similar to many other in-demand professional fields, data analysis is a field that’s been ravaged by spammy information and bad ebooks, so it’s difficult to know where to start and what’s worth learning.

Every time this topic is discussed, I see so many misconceptions on what’s important and what your learning path should be.

We’ll dispel those myths and instead show you a clear path on what skills to learn.

A guide to learning data analysis skills

2. Build a portfolio of data projects

A popular recommendation when someone without analytical experience wants to transition into data analysis is to look for opportunities within your current job and start doing a bit of data analysis work here and there. This is a good start to building your portfolio (on the side).

And I highly agree!

This is the best way to apply and show your data skills. But how do you find these kinds of projects? And what do you do if you can’t find any?

How to build a portfolio of data analysis projects

3. Position yourself to stand out among peers

Those who want to transition into data analysis from a non-technical or traditional professional data background, such as marketing are often held back by one concern: “Can I ever compete against those with a technical skill set?”

This answer is: Yes!

And you even have some advantages compared to those with a purely technical background.

Another misconception when seeking an analytical role is that you are inferior to those with a technical background.

We’ll discuss how you can play up your strengths as somebody coming from the business side (and which data skills to learn and how to build a project portfolio).

Building a personal brand for data analysis