Businesses of all types and entire industries have a big problem on their hands: there simply aren’t enough qualified data scientists out there.
With big data becoming so popular and companies seeking to get their hands on as much of it as possible, the demand for data scientists has skyrocketed in recent years. This has resulted in a shortage of people with the desired data science skills.
As could have been predicted, many educational institutions have responded by organizing new data science programs with the goal of churning out a new generation of data experts to fill those positions. However, we’re still years away from seeing that talent gap reduced. In fact, we could still be seeing a major shortage of data scientists through the end of the decade. This has lead many people to looking into becoming data scientists themselves, even if they don’t have the specific degree for it. It’s not exactly the easiest path to take, but it can quickly prove rewarding.
Make no mistake: getting a degree in data science is well worth the effort, but if you’ve already graduated, the prospect of going back to school might not be all that enticing. Luckily the work of a data scientist isn’t always easy to define. A data scientist in one industry could end up doing vastly different work compared to a data scientist in a different field. In essence, performing data science requires a large set of skills that can be developed independently. Many people take the skills and talents they learn from getting other degrees (like physics, chemistry, biology, etc.) and apply them to the topic of data science and analytics.
Even a degree in some seemingly unrelated field, like the Humanities, can contribute vital skills. After all, data science often requires creative thinking and even some artistic flair in visualizing data and telling a story with it, all skills cultivated through the Humanities.
Of course, identifying what additional skills you’ll need is no easy task either. Computer skills are definitely necessary, as is some knowledge of statistics and the industry you hope to get a job in. Coding can’t be ignored, and it’s helping to start out in an open source coding language like Python. You’ll also want to gain a thorough understanding of databases and distributed storage, all important components of becoming a skilled data scientists. Beyond that, familiarity with data cleaning, where bad data is eliminated so the good data can be analyzed, is something every prospective data scientists should know about. All of these skills will likely need to be developed in your spare time, as without an organized set of courses, you’ll need to educate yourself in some way.
Beyond the skill set, part of becoming a data scientist is mastering the tools of the trade. There are numerous platforms that data scientists can use, so learning about Spark, Hadoop, and more will prove extremely beneficial. Some tools are available for free online, which gives you a great excuse to get familiar with them. You’ll also need to read up about all the latest on data science, learning about news updates, new technologies, different strategies, and more. In this way, you’ll be able to follow the data science community and prove your knowledge about the subject.
While you may be working on your skills, what many employers want to see if experience. Can you put those skills to use and deliver a unique and informative project? This requires you to pursue your own projects on your own free time. There are plenty of competitions that you can enter, giving you the chance to demonstrate your skills in finding new ways to use data to solve problems. Beyond that, finding a knowledgeable mentor within the community is another way to hone your talents while knowing you’re on the right path.
Companies are eager to find new big data solutions to their dilemmas. They need data scientists to make it all happen. Though you may not have taken a college program specifically in data science, you don’t always need a degree to be a data scientists. By growing your skills, following the community, and discovering your own solutions, you’ll prove your worth as a data scientist in short order. It may be a difficult road to travel at times, but the rewarding career that follows is a valuable tradeoff.