If you’re looking to break into tech, you’ve seen the term “data science” thrown around. It’s no surprise HBR named Data Scientist the “sexiest job of the 21st century”; data is more valuable and more available than ever. Making sense of it though? Still a challenge.
Careers in data are mysterious, but luckily for job seekers, patterns are emerging. If you’re analytical-yet-adventurous and you have a flair for storytelling, this could be the field for you.
Data analysts sift through data looking for patterns and solving business problems. They’re pros at data manipulation and have a knack for separating insight from noise.
Data scientists interpret data as analysts do, but their focus is strategic rather than tactical. They use tools such as predictive modeling, machine learning and statistics to find answers. Their work can seem abstract to non-technical decision makers, so they’re often responsible for communicating the story of the data.
Let’s take a deep dive into each career path and discuss how to prepare for that first interview.
A data analyst turns data into insight. Analytical skills are key, so a STEM degree is often required. Beyond that, you should have:
The bulk of your day will be spent interpreting data. With less emphasis on predictive analytics or statistics, data analysts work with various databases to answer business questions and create reports.
The below posting for a data analyst at Edmodo lists the following responsibilities:
All of this experience will carry over into data science, but scientist’s backgrounds are both broader and deeper.
Data scientists are super-powered analysts with an advanced skill set. As a data scientist you’ll spend lots of time asking future-oriented questions. To find answers, you’ll run experiments and test cutting-edge tech like machine learning.
Per Glassdoor, common job requirements are:
*Although a high percentage of data scientists hold advanced degrees (88% per KDnuggets), fields of study vary. Your PhD in zoology isn’t a dealbreaker!
A typical day-in-the-life is hard to pin down. You’ll wear many hats and each industry has its own set of expectations. Heavy into statistics and programming, data scientists are systems builders. One day you may build a predictive model based on an interesting trend. Another day you’ll improve data architecture for the analytics team.
A job posting for a data scientist at Evernote lists the following:
If a life of data wrangling appeals to you, you’ll want to put your best foot forward. These jobs are in high demand!
If data analyst sounds like the right fit:
If data scientist is more your style:
Flash forward. You’re a Python expert and you can explain the basics of predictive modeling to your mom. You’re ready to apply for your dream job. But you know lots of other candidates have the same qualifications. What can you do to stand out?
First, assemble your materials. Tailor your portfolio according to the company and job description. Make sure your GitHub profile/blog features a few relevant projects. Code should be visible and well documented. Bonus points for including a README file that explains setup and summarizes the project! Here are a few tips for putting together a standout portfolio:
Once you’ve got a killer portfolio, the best way to prepare for a technical interview is to practice. We recommend Exponent’s Data Science Interview Course which includes questions from Google, Facebook, and Amazon as well as 1-on-1 coaching. If possible, begin far in advance of any interviews so you have time to get comfortable. And don’t forget to practice explaining your answers - communication is as important as technical competence in data careers. Here are a few extra tips:
This goes for any interview but bears repeating. Prepare your outfit and paperwork the night before, get a good night’s sleep, and leave early to account for traffic. If you’ve followed the steps above, you’re well-prepared to rock that interview.
Both data analysts and data scientists make data actionable and "elegant” but a data scientist is a true scientist in the sense that they ask their own questions, figure out how to find answers, and explain how those answers affect the bottom line. The analyst is a super effective problem-solver, but he/she doesn't need 20 slides to explain themselves to upper management.
Keep in mind that data careers are changing constantly. You probably noticed the overlap between the two. It’s true that data analysis is a great entry into data science. The best way to prepare for a career in either is through practice; with the right practical skills, you can own a career in data. Good luck!
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