By Eric Haller
The demand for data scientists currently outweighs the supply – and that’s creating amazing opportunities for those willing to make the leap.
An estimated 2.5 quintillion bytes of data are created every single day, and it’s up to data scientists to extract meaningful insight from numbers and information points that will empower business leaders to make smart decisions. As the amount of information available has grown, so has the field of data science. The number of data science jobs is projected to rise at least 11% by 2024, reports the Bureau of Labor Statistics. These jobs pay a median salary of $91,600, according to PayScale.
There are some prerequisite skills for those interested in entering the profession. First, data scientist hopefuls must master basic algorithms in statistics and machine learning. I suggest learning through online courses such as Machine Learning (ML), Learning From Data, and Principles of Autonomy and Decision Making. Then try to master at least one programming language, such as Python or R. I advise prospective data scientists put theory into practice by working on a real data science project, as well as becoming an expert in the business problem they’re addressing.
Keep in mind, technologies and algorithms are just tools to solve real-world problems. Deepening one’s understanding of the business process and the problem is the utmost important aspect in designing and developing an effective data science solution.
Keeping up on trends is mandatory for the IT professional looking to transition into data science. The ones that have me stare off into space the most often are related to the ramifications of the ubiquitous application of ML tools. Whether accessible on all the major cloud platforms or an emerging component of productivity software, I’m focused on what does this mean in terms of our expectations for data scientists in the future and/or the “average” employee in any business. Today I can have a conversation about LDA, but the group of people that can have an educated dialogue about unsupervised machine learning is still relatively small.
What happens when someone doesn’t need to understand those concepts at all but can just press a button to be exposed to a powerful method to segment their customer base? I’m starting to buy in to the fact that we are on the precipice of radical change in data science. The field will dramatically expand to include a much broader population to apply ML techniques in a much broader set of “everyday” business tasks.
Leaders in the field will be driven to create new methods to solve the most challenging problems – like extracting more value from sparser datasets or managing computational limits on high dimensionality problems. And, they’ll be increasingly sensitive to what innovations the hardware side of technology will bring to the field – whether it’s things like leveraging GPU clusters or, eventually, quantum computing.
Budding data scientists should be aware of the way they offer competitive advantage. They bring distinction to an organization in several ways. First, they are very aware of the big data environment. Understanding what a NoSQL environment is or boosted trees is one thing; writing models in R or H2O that don’t blow up your hadoop cluster is another thing. So, knowledge is important as it allows the data scientist to execute well leveraging, state of the art tools. Second, they are pulling together multiple pieces to the puzzle simultaneously — a deep understanding of the data they are working with and a practical understanding of the business problem they are trying to solve. Data scientists can move much, much faster. It’s like having a mathematician, a software developer, and a person with a business background all rolled into one person.
So, you’re interested in big data and you think you have the skill set to become a data scientist, but you don’t have a STEM degree. Now what?
Here’s how to transition into a career as a data scientist, even if you don’t currently have the right background:
Start with courses to see if you enjoy the content. Focus on data hygiene, data management, data infrastructure, analytics, statistics and machine learning. If you enjoy the classes, consider a possible graduate degree in data science. Excellent programs include UC Berkeley, NYU, and Northwestern.
Download public datasets from sites such as Kaggle and test your skills against the open market. You can also see what kinds of projects you can get exposure to where you work, where you can get access to data and test your skills.
Talk with some actual data scientists. Listen to what their day-to-day life is like and ask yourself if that sounds exciting or worth the effort to change your career. Because demand is so high, just even networking with data scientists might open doors for employment opportunities as they are asked all the time about who they know that can possibly fill a role.
Location, Location, Location
Consider moving to a hot spot for data science. Top ranked cities for data jobs include Raleigh, Boston, San Diego, and Portland.
If you’ve got what it takes and you have a passion for crunching numbers – quintillions of them – get started on your path to a career in data science today.
About Eric Haller
Eric Haller is the Executive Vice President and Global Head of Experian DataLabs. Eric leads DataLabs in the US, UK and Brazil that support research and development initiatives across the Experian enterprise.