There’s no doubt that data scientists are in demand, and have been for some time. As far back as 2012, the Harvard Business Review described data scientist as the sexiest job in the 21st century. And while, for some at least, it may conjure up images of geeks in glasses, staring at endless streams of data, the reality is (sometimes) far from the stereotype.
What is a data scientist anyway?
The term ‘data scientist’ means many things to many people. Depending who you ask, it may be someone with a PhD in Statistical Analysis or an MBA in Computer Science. The reality is, a data scientist is somewhat of a hybrid.
According to the Oxford Living Dictionary, a data scientist is “A person employed to analyze and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision-making”. Which, as dictionary definitions tend to be, is a concise description.
Similar to a business analyst or data analyst, a data scientist uses their knowledge of computer science and applications, modelling, statistics, analytics and math to gain insights into data. Unlike a traditional business or data analyst, a data scientist then uses the insights they have gained to change the way an organization approaches challenges.
This unique approach combining analysis with strong business acumen and effective communication leads to an equally unique role. But apart from being the sexiest job of the 21st century, why would you consider becoming a data scientist?
Why become a data scientist?
Data scientists are in high-demand
According to recruitment site Glassdoor, data scientist ranks as the best job in America. In reaching that conclusion, Glassdoor looked at the number of job openings, salary, and overall job satisfaction rating. And as far as the number of job openings are concerned, it certainly seems that there are more than enough to go around.
Glassdoor lists almost 36,000 openings for data scientists, and other job boards show similarly high numbers of openings. LinkedIn has almost 10,000 listings, while Indeed has a little over 17,000.
Big data equals big money
Depending on who you ask – and who’s hiring – salaries for data scientists can range anywhere from $60k right up to $250k per year. The national average seems to be around $130k and in a time when the overall average salary is around $65k, that’s pretty good.
Long-term job stability
Machine learning and AI may one day do away with the need for data scientists, but for the foreseeable future, the prospects of long-term employment look rosy. With more organizations collecting and leveraging more data, data scientist may well be one of the few careers that can last you a lifetime.
So how do you jump on this lucrative bandwagon?
How do I get started on a data science career?
As Thomas Davenport and D.J. Patil say in their Harvard Business Review article, a data scientist is “a hybrid of data hacker, analyst, communicator and trusted adviser.” It stands to reason that having a strong background in at least one of these areas is a key requirement.
Looking through the job openings, many of them require a degree in an engineering, computing, statistics, or business discipline. All of which makes sense. Any one of these areas will provide a solid background upon which to build the technical skills needed.
And you will need technical skills. Far and away the majority of data scientist jobs look for skills in R, Python, SQL and similar technologies. The good news is that most of these can be learned (although not mastered) in a relatively short time, and there are plenty of places to learn the basic skills for little or no cost.
In the free category, IBM (yes, I said “IBM” and “free” in the same sentence) have the Big Data University, a community initiative offering opportunities to learn the basic concepts required to become a data scientist. In the true spirit of gamification, you earn badges as you progress. Although there’s no formal certification to be earned, prospective employers can see that you have at least gained an understanding of the principles.
If you want a more ‘formal’ certification, Microsoft, in association with edX, jumps to the rescue with the Microsoft Professional Program in Data Science.
Described by Microsoft as “helping to close the skills gap by teaching conceptual skills alongside technical ones on a variety of technologies”, the MPP in Data Science lets you learn eight skills at your own pace.
Through ten courses hosted on edX, you will learn data science skills such as Python, R, T-SQL, HDInsight, and PowerBI. You can ‘audit’ the courses free of charge, but if you want to be awarded a digitally-sharable credential, you’ll need to upgrade on edX to Verified Certificates. The cost for each certificate varies from $49 to $99, and with ten courses making up the whole curriculum, you’re looking at less than $1,000 for the whole thing.
The advantage of this approach is that with Microsoft being a well-known name, the credential is likely to carry a little more weight with your future boss.
In addition to general data science qualifications, many vendors offer their own certifications. While being specific to a particular vendor, these are valuable in themselves as they show your proficiency in a particular solution. If an organization is looking for someone to work with a particular solution, they may well give preference to someone certified by the solution’s vendor.
In keeping with many of the ‘big name’ vendors, Data Ladder offers various levels of certification in DataMatch Enterprise, from Associate through Expert. With many organizations now choosing Data Ladder for their data cleansing, matching, linking, and address verification, a Data Ladder certification is something worth aiming for.