Computer science might forever remain the go-to field for broad tech knowledge, but specializations are becoming more and more valued by employers around the globe. For example, cybersecurity professionals are becoming increasingly necessary for businesses to keep private information digitally and physically safe. Information systems managers are focused on keeping networks live so businesses’ employees can cooperate on profit-earning projects. However, for big and small businesses alike, one of the most fascinating and useful specializations is that of the data scientist, who is adept at extracting meaning from terabytes of seemingly useless numbers.
Data science isn’t a new discipline within the ever-expanding tech industry, but it has recently gained some popularity alongside the increasing prominence of Big Data. As the name suggests, data science is the study of data ― but more specifically, it is the effort to understand, store, and manipulate data efficiently. As companies and individuals generate more and more data ― largely because companies have begun to recognize the social and economic value of that data ― the task of organizing and using data effectively has become more challenging. As a result, qualified data scientists have become hot commodities.
Typically, earning an advanced degree, such as a data science master’s online, will qualify one for work in the field. Data scientists can find employment almost anywhere that makes use of Big Data, such as universities, health care facilities, scientific research facilities, government agencies, and more. The key is to look for jobs in any of the following three career paths.
1. Data Scientist
This one should be a no-brainer, but rarely do recruiters post positions for data scientists that are so clear-cut. More often, jobs in this career path have titles such as “data manager” or “statistician”.
Nevertheless, whatever they are called, data scientists on this track use their mathematics and programming skills to handle data directly. Usually, these positions allow data scientists to track data from one end of a project to the other ― an exciting opportunity for control ― by building data storage spaces, composing predictive modeling processes, and reporting on their findings. To do this, data scientists typically need a firm grasp of coding languages, particularly Python and SQL.
The average salary for experienced data scientists on this track is $115,000, but entry-level workers should expect to make closer to $80,000. By 2024, the need for data scientists should grow roughly 30 percent, meaning there are plenty of openings for new workers.
2. Data Engineers
Also called data architects or database administrators, data engineers have slightly different responsibilities from true data scientists. In fact, some data scientists argue that it is possible to excel in this career with a general computer science degree ― but that doesn’t mean a background in data science isn’t better.
Like other types of engineers, data engineers are particularly interested in working with raw materials to create solutions. Using math, programming, and a familiarity with Big Data, data engineers are almost always elbow-deep in data sets, working to process the information contained therein and to clean up unnecessary or confusing information.
Similarly, data engineers should have extensive experience with Python and SQL, but skill with Java-based frameworks like Hadoop is also a plus.
Entry-level salaries for this occupation average about $81,700, while those at the top of their field can make over $100,000. Growth in data engineering is a bit slower than other data science fields, at 11 percent by 2024, but still faster than the national average for all jobs.
3. Data Analysts
Though the distinctions between the terms “analyst” and “scientist” may not be clear, this career distinguishes itself by being much more closely linked with business practices than the previous paths. Typically, data analysts can comfortably fill any position with “analyst:” project analyst, market research analyst, information security analyst, business analyst, and the list goes on.
The job of data analysts is to make data comprehensible to those not trained in data science. By creating attractive, intelligible graphs and charts or using plain language, these data scientists are able to make the information come alive to everyone else. Having statistics skills is of course mandatory, but just as important are the abilities to translate the data into business terminology and strategy. Using SQL is vital but equally so is familiarity with Excel.
Perhaps because data analysts rely less on technical knowledge than other career branches in data science, the average starting salary for this career is about $65,000. However, because it is more closely related to business, analysts have more opportunities to reach executive positions, netting them salaries well into the six figures. Plus, growth is substantial in this field, with 30 percent more job openings by 2024.