How to Prepare for a Data Science Interview
As data science roles continue to explode nationwide without enough qualified candidates to keep pace, you may want to consider a career in this lucrative and growing field. Data science roles grew by 34 percent annually between 2013 and 2019, compared to just a 15 percent increase in job seekers in the field. And data scientist consistently ranks among the top three jobs in the country, with an average salary of $123,340 in the U.S.
But before you can attain this exciting new job title, you must clear a fairly conventional professional hurdle: the job interview. Data science interviews can be a particularly challenging experience, designed to test your technical skills and business acumen in equal measure. Read the following five key tips to acing your interview, including ways to research and prepare for typical data science interview questions.
Start With the Right Training
Regardless of where you are in your career or your level of fluency with data analytics, it can’t hurt to add some formal education and training in data science to your resume. You should make an honest assessment of your current level of expertise and seek out a course or program that can help make sure you have the knowledge and skills you need to ace your data science interview.
Online courses are a great option for working professionals who want to sharpen their data science skills on the side while maintaining their full-time jobs. EmergingEd, for instance, offers online data science courses at a variety of levels. They have options for professionals with little to no technical experience who are just starting out in the field, and more advanced courses for seasoned veterans built around real-world data analysis case studies.
Perfect Your Portfolio
Data science interviewers are interested, first and foremost, in seeing evidence that you’re able to carry out complex analytical tasks. You will likely be asked to submit a portfolio of your work to demonstrate your ability to consider a data-focused business task; understand, sort and analyze the data set at hand; interpret the results; and communicate your findings.
While your portfolio will likely be viewed by the hiring manager before they decide whether to invite you in for an interview, you should be prepared to respond to any questions about it in detail. Make sure that the portfolio is clear, complete and impeccably proofread for typos and grammatical errors. And even though it is your own work, you should set aside some time before the interview to study up on it. You don’t want to be surprised by a question and give off the appearance that you either are not fully invested in the work or do not understand it at a fundamental level.
Brush up on Technical Skills
Data science is a complex, quantitative field, and the professionals who have what it takes to cut it in data science roles must be quite fluent in some extremely technical analytical tools and processes., Whether you’re planning to apply for a data science position or already have an interview lined up, it’s important to remember that you will need to be ready to discuss these topics with your interviewers when the time comes.
It’s possible you will be given a sample coding problem to complete during the interview process, so be sure you’ve practiced your programming skills. You also may be given sample analysis problems and asked to describe how you’d go about solving them. While you won’t have time to execute your entire solution in the interview room, you should be confident enough in your knowledge of the tools at your disposal to describe which ones you would use, how you would use them and what kind of hypothetical results you may expect them to yield.
Research Potential Questions
Because of the large number of data science positions that have been created over the last several years, a wealth of information is available online regarding the types of questions you may be asked in an interview setting. With a bit of research, you should find some helpful online forums where interviewers and interviewees alike share their experiences with this process.
Lists like the one compiled on Glassdoor by previous data science job candidates, for instance, go beyond simply cataloging data science interview questions. You can see how candidates answered the questions, learn whether candidates were successful with the approach they took and read ongoing discussions among data science professionals about the correct approach. And if you can find a forum where interviewers participate, even better—any insight you can find into the mindset and priorities of a data science hiring manager can only help.
Practice Makes Perfect
While data science is highly quantitative, it is also a business discipline that demands top-notch communication and persuasion skills to thrive in it. Your data science interviewers will be gauging not only your technical competence, but your ability to clearly articulate your ideas as well.
To be sure you speak confidently, purposefully and convincingly in a high-pressure setting, practice answering sample questions at least several times before your interview day. You can do so alone, but the most effective way to train yourself for success is to practice with a friend, colleague or professional interview coach. This will help you develop pre-planned answers to likely lines of questioning and will make you more comfortable thinking on your feet when you encounter a question you weren’t expecting.
EmergingEd’s online data science courses are fantastic building blocks for a career pivot into this dynamic, lucrative field. Explore our industry-approved, expert-led courses today, and check out our guide to data science books that can help propel your career growth.