Every day, organizations are creating more and more data, yet struggling to tap into and ultimately benefit from this vast amount of information due to the lack of talent available in the field. A study from McKinsey found that the U.S. alone could face a shortage of 150,000+ data analysts and an additional 1.5 million data-savvy managers by 2018. And, according to LinkedIn, statistical analysis and data mining were the hottest skills that got recruiters’ attention in 2014. Glassdoor ranked data scientist as the number one job to pursue in 2016. Harvard Business Review even called it the sexiest career of the 21st century.
Got your attention yet? For those interested in tapping into this in-demand and potentially lucrative career, there are some best practices to ensure you are set up for success before applying for jobs in the field.
Preparing for a Data Science Career
Click through for tips on how to ensure you are ready for success before applying for a data science job, as identified by Springboard.
Build a Data Science Portfolio and Resume
You need to make a great first impression to break into data science. That starts with your portfolio and your resume. It’s important to have work to share that you’ve completed in a real-world setting, be it at a current job or through a course that provides that opportunity. Many data scientists have some type of online presence to showcase this work, such as their own website or blog. This vehicle allows them to demonstrate their experience and the value they create in the data science community. If you go this route, your portfolio should highlight your best projects presented in a well-designed, captivating way.
Network and Build a Personal Brand in Data Science
Once you have learned the skills and developed a strong portfolio, the next step is to connect with people who can help you leverage those strengths into a data science job. Building your network among data scientists will substantially increase your odds of breaking into the field. Many of the best opportunities aren’t posted on job boards, but are spread privately through certain networks. Networking with the data science community and helping to solve challenging real-world problems will enable you to build a portfolio and a personal brand, which can help you land a job.
Find a Mentor
One of the highest-value networking activities you can pursue is finding a mentor who can guide you as you seek and pursue a data science career. Somebody who has been in a hiring position can tell you exactly what companies are looking for and how to prepare for interviews. She can also introduce you to other people in the data science community, or in the best of cases, even end up hiring you!
Get Involved in the Community
With a bit of searching, chances are you can find great data science events in your area. These are valuable places to meet fellow aspiring data scientists and pick up the jargon. You will get to hear from, and build connections with, established data scientists, and even unearth hidden job opportunities. In addition, some of the best data scientists are on Twitter, and you’ll often find data science podcasts to follow. You’ll also find blogs, newsletters and communities that can help you connect with data scientists online.
Prep for the Interview
If you get an interview, what do you do next? Data science interviews involve some targeted preparation. There are several kinds of questions that are always asked, such as those about your background and those related to coding and applied machine learning. You should always anticipate a mixture of technical and non-technical questions in any data science interview. Make sure you brush up on your programming and data science — and try to interweave it with your personal story.
So whether you are simply considering a move, or you’re ready to send those resumes out, keep these five key best practices in mind to increase your chance of landing the job of your dreams. Getting your first data science job might be challenging, but it’s possible to achieve with a diligent approach.
