How to Prepare to Get Your First Data Science Job - A Guide

Oct 24, 2018 | 3264 Views

With the demand for data scientists exceeding supply by 50 percent to 60 percent, there is a pressing need for more talent in organizations. And you can use that your advantage to establish a wonderful career. So, yes, snagging a data science job is certainly an excellent idea.

Not only will you be spoiled for choice when it comes to data science job opportunities, but you'll also benefit from high job security and the capacity to directly impact the direction and decisions of an organization.

What's more, the current salary offered for data scientist positions is nothing to scoff at. No wonder this profession is currently ranked as one of the best in America.

So what's the secret to landing your first data science role? See some tips below:

Want a data science job? Start thinking long-term
OK, now comes the bummer: In reality, only a handful of data science jobs are available for entry-level aspirants. So, a fresher might have to start somewhere else and hone their skills in multiple areas before they can consider a full-time data science job.

Although numerous career opportunities exist in the field of data science, two of the popular ones include business analytics or academia. If you hold advanced degrees in computer science, statistics, the sciences, or applied economics, and are capable of handling large sets of data, then you might consider switching from a career in academia to one in the tech sector.

Also, business analysts possessing long-term experience in dashboards, database querying, and reporting are qualified for data scientist roles due to their handling of large data sets and translating them into actionable advice for their associated firms.

Hone your coding skills
Irrespective of their industry, data scientists are successful only if they acquire a combination of analytical, presentation, and technical skills. Moreover, they must be passionate about answering tricky questions and solving problems creatively.

Even though the requisite level of technical talents varies from company to company, most require data scientists to know four coding languages viz. Python, R, Bash/Command, and SQL. Depending on the company that hires you, you might use one or all of them. Thankfully, once you learn one language, picking up the others will seem easier.

Make use of available resources
You have two resources available at your fingertips for learning data science in a simple and cost-efficient manner. The first is books, and the other is online video courses and webinars.

Contrary to popular belief, you don't have to spend thousands of dollars on workshops or conferences. Instead, pick up a good book on the subject. You will get detailed and focused knowledge regarding online data statistics, analysis, and data coding.

Plus, there are always data science courses and sessions available online, which although not free, still cost a whole lot less than expected. And they cover a wide range of subjects, from business intelligence to data coding. If you search hard enough, you can find free learning materials and courses.

Use these ready resources to soak up as much information as possible prior to searching for a data science job.

Networking and meet-up events are also a great way to familiarize yourself with the who's who of the data science field. Ask them for career advice and identify gaps in your resume or skill set. They might even suggest unknown resources that boost your expertise.

Choose a specialty
Data science is THE job of the tech industry right now, which means there will be plenty of people fighting over a coveted position. Plus, the responsibilities of a data scientist are vast and cover a lot of ground.

You can maximize your chances of getting a job by choosing a specialty like data visualization, NLP, or web scraping. This enables you to stand out among the potential hires and provides a better focus and grasp over your talents.

Engage in independent work
The show, don't tell - that's the mantra of the data science industry. Your prospective employers will better appreciate your talents if they can see results instead of listening about them. Of course, given that you're a fledgling data scientist candidate, you're unlikely to have a large amount of work-related projects to showcase during the interview.

However, you can still find a way to work around this dilemma. First, check out techniques to incorporate data into your existing position.

Find out whether you can leverage the existing data of your company to make better and more prudent choices. Although it's a tiny step forward, it can connect your current experience to your targeted data science position.

Create a solid portfolio
Once you build up a sizeable data science portfolio, you can use it to demonstrate how capable you are of performing tasks assigned by your potential employers. It will also serve as a substitute for any job experience you might lack.

Look at the situation from the perspective of the employer - they want to hire someone skilled, savvy, and knowledgeable while avoiding weak candidates. A portfolio is a perfect way to demonstrate that you have the qualities and skills needed for that particular position.

Expose yourself to outside projects
You might have a full-time tech career already, but that should not discourage you from pursuing a passion project outside the office. You should feel free to work on projects that utilize your skills to the fullest extent - playing video games is fun but 10 hours a week could be a bit much.

When you devise something that excites you and motivates you to code in your spare time, you are likely to end up with tangible work examples that you can present later on to a potential employer.

Also, when you've developed a baseline of experience, check for volunteer or freelance work for small startups or companies that cannot afford to employ full-time data scientists. This is one of the best ways to build up your credibility in the industry and present yourself as a serious data scientist candidate to prospective employers.

Spend your time constructively
The field of data science is growing rapidly without any signs of slowing down. If you want a piece of the pie, you will have to conduct thorough research and work hard to convince potential employers that you can make a difference in their company. Only then will you have a rewarding and fulfilling career path - and a data science job.

Source: HOB