The field of data science is growing at a rapid pace, with some roles growing by as much as 11.4 percent year over year. And there’s plenty of demand: by some metrics, there can be as many as 2.7 million job postings in data and analytics in a given year.
Everywhere you turn, you’ll see and hear “big data” and analytics described as the future-forward solution to nearly every business problem. Yet you’ll have a much harder time tracking down clear explanations of just why that’s the case.
The why and the how of data analytics are complex, and they are exactly what an M.S. in data science from Lewis University can help you understand and master. Graduates of our program have found many successful career paths, including as data scientists, data analysts, and data specialists, among others.
Data Scientist, Data Analyst and Data Specialist Job Titles Explained
There’s often a great deal of confusion about these job titles. Data remains an emerging field, and the associated roles in many businesses have been loosely defined (and sometimes creatively named).
Below, we highlight three of the most common ways of categorizing data science jobs. The names are similar, but the responsibilities and overall focuses are different. Armed with this information, you’ll understand the nature of each role. You’ll also be better positioned to decide which data science job you want to pursue, and what you should do now to prepare.
A data scientist is a true master of the data. To fill this role well, you need to be someone who can comprehend the nature of large sets of data, someone who knows how to create and use the models and calculations to work with that data.
A data scientist needs the insight required to ask the right questions of the data as well as the technical knowledge to build the model that will reveal the answer. This position requires novel thinking and creativity both in determining the problem and crafting a solution. Data scientists are often tasked with predictive and creative use of data. They must create new models that make predictions about what’s coming rather than simply analyze what has already happened.
Data scientists need exceptional math and technical skills and are often required to use Apache Hadoop or work in one of several programming languages.
To be effective in this role, you need to understand how to make the data work for your organization. You may receive (or generate) an open-ended question, and it’s your job to figure out how to get the data to tell you the answer.
Generally speaking, data scientists are the most valuable (and highest compensated) of all the data roles. They work independently or at the direction of C-level executives, and they often report to one or more C-level executives.
While becoming a data scientist is an impressive goal, it’s uncommon to land a senior role here without prior experience.
In most organizations, there’s a sufficient need for people who can use provided analytics tools to solve problems and discover trends in the data. Data scientists create the analytics tools needed. Skilled and well-trained professionals are often required to use those tools to gather those useful insights—in other words, the data analyst.
To succeed as a data analyst, you’ll need a solid grasp of statistics, and you’ll need sufficient programming chops, too. Most data analysts also spend significant time processing or working through data and turning the results into data visualizations.
As a data analyst, you might also be tasked with cleaning up data, improving your organization’s data intake regimen, or answering specific (rather than open-ended) questions that your business or its large customers bring to you.
Reporting structures vary significantly in all of these roles. That said, in most organizations of sufficient size, a data analyst would report to a higher-ranking data scientist. In some cases, data analysts report directly to a C-level executive, likely an information or data executive.
Compensation for data analysts tends to be impressive, but not quite so impressive as data scientists. This may be your ideal role if you enjoy working with data but prefer to have some constraints given to you—rather than working with open-ended, nebulous questions and parameters.
Of the three job titles discussed here, data specialist is the murkiest. The responsibilities of a data specialist are typically quite similar to those of a data analyst. There often isn’t much of a difference, and some organizations may not have both job roles. They may choose a single title to cover these responsibilities.
In some organizations, the specialist is the junior position—specialists report to analysts. In others, the roles are reversed, with the specialists being the senior and better compensated.
Three (or More) Terms, Unclear Boundaries
All these descriptions are being written with larger companies in mind. Of course, in the largest enterprise organizations with robust data departments, there may be further stratification or hierarchy. You might start as a junior data specialist and then progress through ranks and bands over the course of your career. There may be other related roles in the organization, as well (see the next section).
In a smaller but still data-driven organization, there may be no distinction between roles at all. One or two data roles might be expected to do everything that an organization needs related to data.
So, what is a student pursuing a career in data to do? Once you’ve determined what kind of focus you want to pursue, it’s time to develop a list of questions. Make sure to ask specific, detailed questions of any prospective employer. Ensure you have a clear understanding of what the organization means by a job title—what is and isn’t included in the role.
Bonus Round: Other Related Data Roles
We’ve covered three of the most common terms and roles within data, but there are plenty of others you might encounter. Here are a couple of other common roles you might be interested in pursuing:
- Data engineer: Similar to a data scientist role, the data engineer shapes the data and builds pipelines for it. This role takes a software engineering approach and applies it to data and analytics.
- Business analyst: This role requires a good understanding of data manipulation (perhaps not to the level of a data analyst) in combination with business and industry expertise.
Pursue Your Dream Data Role with an M.S. in Data Science from Lewis University
If you’re looking for a career in data, you need high-quality preparation no matter which job title you’re pursuing. Lewis University’s M.S. in data science will prepare you for a wide range of careers in data. With in-person and online offerings, Lewis accommodates the working professional as well as full-time students. We also offer two concentrations, depending on your desired specialization: computer science as well as computational biology and bioinformatics.