How to transition your career into Data Science — even if you don’t code today.
(Don’t have time to read it all? I’ve summarized the entire article in the conclusion section!)
During my PhD in Organizational Psychology I always found myself reading about Data Science — thinking, it’d be really cool to be a data scientist... But, at the time, I couldn’t write a lick of code, so I chalked it up as a dream I’d achieve in another lifetime.
My worries about learning data science came in two forms: 1) Data Scientists code, I don’t, I’ll never become one, and 2) I’m doing a PhD in an unrelated topic, why throw it away?
It was around this time I was perusing LinkedIn and noticed an update, my friend who had been working as a recruiter for 5 years took a position at Google.
Curious to learn the secrets of scoring a job as a software developer at Google without having a related degree from Stanford or MIT, I sat down with him for a coffee.
I was excited.
Maybe this is where I could learn the secrets to a career transition into Data Science?!
I brought along my favorite pen and notebook.
I expected to hear the equivalent of the “5 easy ways to become a Data Scientist at Google” and have a come to Jesus moment.
I heard no such thing.
He engaged with communities at freeCodeCamp and started with basic tutorials on how to code. He learned the basics of coding and then moved to building small projects. He further immersed himself in the community and began to build bigger and badder products. Two years later, and after committing a few hours a week, he was able to land his dream job at Google.
Instead of feeling defeated because there was no secret to transitioning my career, I felt inspired.
So I made a contract to myself.
I’d commit 5 hours of my time a week to learn to code, to read about data science and to shut down the self-doubt I had about doing all this.
With all that being said, here are the 5 tips I found most helpful along my journey to becoming a Data Scientist.
Tip 1: Learn to speak and think like a Data Scientist
The most important first step is to speak and think like a Data Scientist. What does that mean? First, learn how data scientists speak. What terms do they throw around frequently (e.g., scikitlearn, matrix-factorization, eigenvectors)? Don’t be afraid, just take notes on the words you don’t understand. Why? Learning the vocabulary is the first step in learning and communicating data science.
Learning data science vocabulary will optimize your learning rate (..get the pun..? ha..ha?)
By knowing the vocabulary, you’ll be able to utilize Google to its fullest extent. For example, if you’re learning about Principal Component Analysis (PCA)and you become frustrated because all the articles you’re reading are too technical, knowing that PCA is a dimension reduction technique and running a new Google search will net you entirely new results on a Google search.
Often times, your ability to learn is limited by what you don’t know (…confusing eh?).
Finding an article that is one level lower and less abstract is key to resolving the gaps in your knowledge.
Knowing multiple terms for the same technique/idea is key to developing a broad understanding of the topic.
Here’s a list of resources I used to learn how to speak like a Data Scientist:
- https://www.reddit.com/r/dataisbeautiful: This was my most inspirational resource to becoming a Data Scientist. Redditors post cool apps they’ve built and talk about how they did it. Sometimes, they post their Github!
- https://www.kaggle.com/c/ga-customer-revenue-prediction/kernels: If you’ve spent anytime reading about Data Science, then you’ve most likely heard about Kaggle. The most important part of Kaggle to an aspiring Data Scientist is the “Kernals” section. Here, fellow Kaggler’s post their solutions to the problems posed by the competition. Spend at least an hour of your time, TYPING and CODING out their solution — practice typing each line, line-by-line in your own Jupyter Notebook. Run the code and see what happens (e.g., you’ll run into errors because you won’t have certain libraries or dependencies installed, which brings me to my third bullet).
- https://stackoverflow.com/: Anytime you have a question about Data Science related questions, head to Stackoverflow and run a search. Learn how people ask questions about Data Science related questions. At this level, most likely all your questions have answers, so running a search should be sufficient.
Tip 2: Engage in Kaggle Challenges
I eluded to this a bit earlier but, learning by doing is ultimately the best way to learn. Spend time looking at the kernals in Kaggle competitions to learn from how other Kaggler’s approached the competition. At first, this will be extremely daunting, you won’t understand 95% of the code you’re reading, let alone, you probably won’t be able to run the code on your own computer even after you’ve cloned it.
This is where you need to be persistent.
You aren’t going to learn anything if you get frustrated, so ease yourself into engaging with these challenges and soon enough you’ll be able to understand the kernals you read.
Remember, when setting goals, be realistic about them (e.g., SMART goals): Specific, Measurable, Attainable, Realistic, Time-Bound (SMART).
In other words, don’t think you’ll be reading Kaggle kernals within a week. Give yourself a specific, realistic and time-bound goal —
“I’ll be able to understand how train/test/split works by the end of this week”.
Set small goals, write them down and check them off when you achieve them. When you feel frustrated, go back to these checkmarks and see how far you’ve come since yesterday.
Tip 3: Find your own Data Science Project
Find a project you’re passionate about, whether it be a problem you’d like to solve or a library you’d like to learn — turn this into a project that you’ll put onto your github as a portfolio piece.
Finding a problem is best done through conversations. Engage with your community, your friends or… Even strangers. Find out what bothers them, or talk to them about ideas you’ve always had.
Hash out your idea, make it simple. Your project isn’t going to change the world. The most important part here is to start on one.
Once you find your idea you’d like to build, tell a friend or make an open commitment to your community (e.g., Medium blog) that you’ll be building it. Most importantly, highlight the features of your app and the time it’ll take for you to have it done (e.g., 1 month to build this app out).
I relied on my commitment to my peer group to build this cannabis recommendation app that generates revenue today.
Read about how I built it on Medium here: How I built it
If you’re looking for a project, I come across companies looking for pro-bono work all the time. Connect with me on LinkedIn and I’ll find the best project for your goals!
Tip 4: Apply for Data Science Jobs
Wait… wait... what?!
You’re probably thinking that you just started learning Data Science and now I’m telling you to apply to jobs. What the…?!
If you’re looking to score a Data Science job, you need to learn how to interview as a Data Scientist.
Yes… It’s a skill. The recruitment and selection of Data Scientists today isn’t great, and you’ll have to learn how get good at interviewing before you can land a job, so begin ASAP.
IMO, you’re ready to interview when you can speak like a data scientist.
Don’t worry about live coding assessments or Data Science related questions just yet, you’re 99% likely to fail these interviews at this point
You want to fail.
You want to take notes on the questions you get.
You want to learn how interviews are conducted.
Your goal here is to get good at interviewing, because you’re still far out from landing a job given your competencies in Data Science.
Before you comment about this tip, hear me out!
You should be speaking like a data scientist within 6 months of spending 5 hours a week. You’re still probably 1.5 years away from landing a job as a data scientist. Applying to jobs now gives you an understanding of how the entire process works. You’ll learn what you need to learn to interview well later on. Further, you aren’t burning any bridges.
Re-applying to a job where you failed an interview is often flattering to an interviewer especially if you come back and are 100x better than you were the year before.
For instance, during one of my first interviews for a data science position, I was asked “What is a negative R-Squared?”. I thought… Wtf? R-Squared can’t be negative. So I proceeded to ramble to the interviewer about what I thought it was.
Fast forward 2 years later, I received the same question from another interviewer.
Because I had looked up the answer 2 years back, I was able to smirk and provide an answer.
The interviewer later told me that he’d never heard an answer that was so succinctly and clearly communicated.
Having your own project will motivate you to complete it as it can sit in your portfolio and show potential companies your competencies.
Tip 5: Network…Network… And Network
Your chances of scoring a job as a data scientist improves exponentially based on your network size.
In-person networking is the best way to expand your network in a meaningful way, however, it’s not always possible to make it out to networking events.
The second best scenario is LinkedIn.
Creating meaningful connections on LinkedIn is as simple as finding people in your industry, sending them a message and keeping up to date with their happenings.
The crucial piece people miss about LinkedIn opportunities is that they don’t let others know they are open to opportunities.
I found great success by adding: “Open to new opportunities” on my LinkedIn title.
Further, Medium supports a great network of Data Scientists that, I’m sure would be happy to connect. Read a cool article? Find the author on LinkedIn and chat with them about prospective opportunities!
I work as a Data Science consultant and come across many opportunities (mostly in Toronto). Let me know who you are on LinkedIn and I’d be happy to connect you to a company that is interested!
Find me here: LinkedIn
A career transition is never easy, especially if you’ve just begun your journey. During my transition, I kept this quote close to my heart:
“The best time to start was yesterday, the next best time is NOW.”
The fact that you’ve read this entire article and are engaging with this sentence today, should show yourself you’re ready to start your transition.
I’m always happy to help a fellow transitioning data scientist, so let me know how I can!
To summarize the entire article in a few bullet points:
- Learn to Speak and Think like a data scientist
- Engage in Kaggle challenges
- Find your own Data Science project
- Apply for Data Science roles… Interview, fail… Rinse & repeat