Read This Book to Snag Your First Data Science Job
By: Amanda West
Tags: Data Science, Books, Work, Interview, Jobs

Read This Book to Snag Your First Data Science Job

5 tips from the book that changed the way I approached data science and inspired my journey as a Medium writer.

Photo by Rashtravardhan Kataria on Unsplash

Let’s just get these facts out of the way: it’s a weird time to be alive right now. For job seekers especially, it may be hard to focus on seemingly trivial things like drilling 10 more technical interview questions, earning another online certification or building your data science portfolio, as you also apply and interview for jobs (I’m convinced the only reason medieval kings didn’t use the job interview as a form of torture was because they hadn’t thought of it yet).

While it can be tempting to give in to the collective slump, consuming low-calorie ice cream by the pint and watching TikTok until your mind melts, I’m here to make you aware of something better. Honestly, it felt like I had found The Great Shortcut when I was reading it.

Published just 2 months ago, Build a Career in Data Science by Emily Robinson and Jacqueline Nolis adventures into the A-Z of the data science vocation. Coming in at 281 pages (+30 pages of technical interview questions with explanations), this book was a thorough mix of practical knowledge and inspiration for anyone just getting started.

These are the top 5 tips that totally enlightened me on my first steps into the data science profession. If you like these, feel free to check out the book (use code buildbook40% for 40% off) and give the authors a shoutout on Twitter for their awesome work!

Disclaimer: I do not know the authors nor make money on the sales of this book. All thoughts and personal anecdotes are my own.

1) Not Being “Cutting Edge” Enough Isn’t the Problem ✂️

Many people get scared off by data science because it’s so broad, to learn everything about everything would mean an impossible learning curve. Nobody can do it. Therefore, it’s more important to drill the basics down deep than waste energy by jumping into the complex stuff too soon.

Especially just starting out, you do not have to worry about being a “data science unicorn” (i.e. someone who comes into a company and can perform every vaguely data science-related task that is thrown at them). Most of the work day most of the time, data scientist are not doing those fancy, high-level tasks, but are executing database queries and preparing data for analysis. For this reason, the way to be a fantastic data scientist is by being able to perform simple tasks with great speed and accuracy.

I recently read a statistic that suggested 80% of a data scientists work day was working with data cleaning and organization, with only 20% spent on actual analysis. Furthermore, interview questions often involve writing SQL queries, having a basic understanding of Python or R, or being able to explain p-values to the interviewer; don’t worry about the complex stuff, worry about knowing the basics really, really well.

2) Create a Portfolio (as a learning strategy) 📝

When I graduated with a degree in economics, I had never been taught the importance of having a portfolio in the job search. After accepting a spot at UVA’s MS in Data Science program and talking with potential future employers from the Federal Reserve, Secret Service, and Department of Justice (I’m personally interested in the public sector), I heard one piece of advice over and over again: bring a portfolio to show us.

Interviews are bad proxies for sniffing out all-star employees. Even technical interviews don’t help a lot; you are very rarely, if ever, going to be whiteboarding a SQL query instead of just typing one. Don’t remember how to solve FizzBuzz? Googling it takes .2 seconds — what does it matter if you can’t recall it by memory during an interview?

By having a portfolio, you accomplish three things: 1) your personal projects have a clear start, middle, and end, 2) you give people a sense of your skills, and 3) you broadcast to people that you are one of us. Data scientists are constantly publishing tricks of the trade they’ve learned, so by creating a portfolio it shows you are taking part in the community long before you hold the title of data scientist.

3) Advertise Yourself 📰

When I was a sophomore in high school, we did a whole segment on Jane Eyre, finishing with the movie (don’t you miss when school used to include movies?). A plain-looking, quiet orphan girl, Jane secures a position as governess on a nearby estate to the great surprise of her friends.

“How did you do that?” They asked, to which Jane replies, “I advertised.”

Advertising yourself is a thing. To many of us, it’s also scary and inconceivable. You see that it works for about one in a million and figure that that the odds are too stacked against you to try. You don’t have the connections, or the internet savvy, or started off on Medium before it became popular.

I didn’t know how to advertise myself, so I took the book’s advice to the T and focused on high-reward areas only. First, I spent three days creating a personal website. Then I created a Twitter and started sharing data science blogs I liked. Naturally, this constant blog-absorption led me to Medium, and after awhile I gave in and wrote my very first blog post about SQL (which I had just learned three days prior).

Within 15 days, I had written 5 more posts, had over 2,500 views on my articles and had cracked 100+ followers. Once I linked my LinkedIn to my Medium, my LinkedIn page views started skyrocketing, too. I was getting bajillions of requests from hot data scientists in my area all over the place.

I’m not an edge case, and advertising yourself doesn’t have to be scary. Start small and you’ll have a strong base of loyal followers before you know it.

4) Keep the Pipeline Full 🚰

Let’s face it; only a small percentage of dates you go on lead to finding “the one”, and it’s the same exact way with jobs. Sometimes they don’t like you, sometimes you don’t like them. And sometimes you do like them, but they’re still hung up on another person, or have predetermined biases you weren’t aware of (like hiring someone internally because the paperwork was easier).

Never do a “real” interview. By treating every interview as practice for your next interview, you take off the pressure on that individual job being “the one” and can have more fun. These people are going out of their way to give you fun data science puzzles, they might buy your lunch, what’s not to like?

Interviewers are not just looking for the right human plug-in, they are also looking for someone who is going to be a fun coworker, too. Keep your pipeline full by applying to jobs continuously until you have an offer in hand, which will also help keep the pressure off from feeling like you have to ace any particular interview in the line-up.

Photo by Van Tay Media on Unsplash

5) Apply to Jobs You Are ≥ 60% Qualified For 💼

The logic is simple: for many companies, the listed job description is more wishlist than requirement. I’m sure we’d all love to be fluent in every programming language, a master communicator with data visualization and a machine-learning and NLP prodigy. In gist, we all want to be data science unicorns (or at least be perceived as one).

Companies will often list more skills than they need for a role, hoping to attract that unicorn. For this reason, Emily Robinson and Jacqueline Nolis suggest applying for jobs you are 60% or more qualified for. If you fulfill 60% or more of the requirements, you are prepared for that job.

I was personally very excited for this advice because it takes the guesswork out of things; I’ve taken to highlighting all of the skills I “come with” for a job description, then look at my qualifications as a fraction of the bullet points listed. If that number is ≥60%, I go for it! If not, it probably wouldn’t be a good fit for either of us.

Lastly, it’s also important to not sell skills that you don’t have because you think it makes your more desirable; if you’re crossing your fingers that they don’t ask about x, y, and z lines on your resume, you should seriously consider if you should have those lines at all. Be your genuine self and focus on the awesome skills you do have: no more, no less.

Final Thoughts 🤔

What did you think of this advice? Was it obvious or novel? Will it change anything about your job search tactics? I’d love to hear your thoughts in the comments below!

Thanks,

— Amanda