Starting off in Data: All the Advice I Can Think Of (Part 1)

Note: Links have been corrected. Thanks to Natalia Sadkov for pointing out my mistake!

I’ve been quiet for a bit because I bought a fixer-upper house and it’s time-consuming! But somewhere in there I found time to write an email to someone asking for career advice for starting off in his data journey. I do this periodically and after writing several long emails, I decided it would make more sense to put it into a post about it for everyone to see. It’s long enough that I’m going to split it into two posts. So here is a summary of ways to start learning, which I’ve also written somewhat about before.

Start Exploring

One of the neat things I’ve found about data careers is that there are so many resources out there to learn. I got my feet wet by taking classes in Python on Coursera, and then did a data science certificate there (I review it here). It was pretty introductory but helped give me a sense of what was out there and gave me language I needed to research it more and talk about it. EdX and Datacamp are also good sources of MOOCs.

Apply Your Skills as Soon as You Can

But MOOCs are a little bit like high school Spanish (you took three years and all you know is que te gusta la biblioteca?). What you really need is someone to drop you in the middle of Ecuador for two months so you are forced to remember how to say you are hangry and at least order some food. Similarly, where I really began to understand data concepts I was learning was by doing a self-directed side project in Python. I thought of an interesting question and found data to analyze (you can read about it here). This is so much harder than following prompts in online classes, so it really drives it home. I put my project on Github and showed it to a few people for feedback (networking helps with this). I recommend starting a side project as soon as possible, even when you don’t feel quite ready. You will learn as you go. Here are some tips for preparing.

Ideally, you’ll also get to apply your learning at work, no matter what you’re doing now. One useful tip I read from someone on the data science subreddit is that if you are just starting out, find a way to work data analysis into whatever job you have. Get known in the org as someone who is good with Excel or makes a nice graph or can use data to solve problems. Find out what data-related problems other people have at work, and figure out how to solve them. Use that to build experience that can lead to your next job.

Applying for Jobs

I don’t need to say much in this department because there is a lot of great advice already out there. This post from star blogger Ask a Manager rounds up a whole bunch of thoughts on resumes, cover letters, and interviews. Advice specific to data roles can be found on Towards Data Science and is a frequent question on the data science subreddit, which has a weekly thread specifically for people new to the field. And Datacamp offers resume review and interview prep in addition to loads of training courses.

Next Up

In the next post I’ll talk about how to consider an analytics job offer, tips for starting a new job, and ways to keep learning. In the meantime, you may also find some useful nuggets here: The Ultimate Guide to Getting Started in Data Science.

What else would you add to this?