The idea that people can glean a lot from data is nothing new. After all, we learn from experience and the missteps and successes of others. What is relatively new is our access to massive quantities of data — we now quantify, log, store and analyze those experiences, missteps and successes, as well as information related to just about everything people do. When is comes to building your data science resume, these new disciplines will often set you apart.
“Digital transformation is not about the evolution of devices (though they will evolve); it is about the integration of intelligent data into everything that we do,” write the authors of an IDC whitepaper on digital transformation and increases in global data volume. Nearly everything we do, from shopping to relaxing to working, is now tracked, monitored and analyzed for insights into human behavior. Data scientists are responsible for how that information is gathered, measured, and used. More data means more data scientists — the Bureau of Labor Statistics projects that employment of data scientists will grow 36% through 2031, much faster than the average for all occupations.
As humanity’s stockpile of data grows, responsible and ethical data scientists with advanced skills will shape the future of business analytics and human behavior. Aspiring data professionals can enter the field and build those advanced skills by earning a Master of Science in Data Science (MSDS) such as the one offered by the Charles V. Schaefer, Jr. School of Engineering & Science at Stevens Institute of Technology. The online data science masters is a data science resume must-have not only because of the immediate value it provides in the job market but also because it prepares students for long-term, successful careers in many industries.
Data Science Draws on Multiple Disciplines
Many data science professionals enter the field because they are drawn in by the potential to broaden their career potential. Take Stevens Institute of Technology master’s alum Kirill Prokrym, for example. He had a background in chemistry and was studying cancer in a biomarker lab but he was interested in programming and data analysis work, so he enrolled in the Stevens Online MSDS program.
“I would 100% enjoy working in the medical field again doing [something with] biological or genetic data,” Prokrym said. He added that data science requires multidisciplinary knowledge other types of master’s programs seldom provide. “The beauty of data science is that it takes three separate disciplines and pulls them together. You need statistics, you need computer science and you need business skills.”
Although data scientists lean on each of these three disciplines at every point in their work, it can be illuminating to break down data science’s multidisciplinary nature sequentially. First, statistics skills help data scientists understand what kind of data they need to track and measure. In an MSDS statistical methods course, students learn to do the work of a statistician, which includes data collection and organization. Statistics is also necessary for data optimization. Once it’s clear what type of data that needs to be collected, a data scientist uses computer science skills to work with the technology that can organize or “clean” massive datasets quickly. This is where AI and machine learning technologies are useful. Finally, data scientists need business skills and domain expertise to turn their findings into strategic recommendations.
Data scientist Ram Thilak offers another way to consider data science’s multidisciplinary nature: its history. Beginning in the 1950s, mathematicians, engineers, computer hobbyists and academics were researching the applications of computing technologies. At the same time, Thilak writes, industries were beginning to adopt management strategies that would make workflows more efficient — strategies that included data collection. These two areas of activity evolved separately through the decades as computer use spread and organizations leaned more heavily on statistics in decision making. With the creation of the internet, however, the disciplines merged. Business and “computer folks,” as Thilak calls them, came together with united interests, and modern data science was born.
The Rise of Unstructured Data
The internet age brought us into the age of unstructured data. Unstructured data is any data not automatically or easily arranged in a spreadsheet or formatted database, including email text, video, audio, surveillance data, data on social media platforms and data generated by smart technology. This type of data, which makes up the bulk of information most people work with today, is useful across disciplines and industries. For example, anonymized patient data can help healthcare organizations improve treatments, develop new drug regimens, predict health crises and increase the accuracy of diagnoses. The challenge is that unstructured data must be appropriately contextualized.
For a long time, few organizations had access to technology capable of capturing or analyzing large, evolving datasets. Analysts could only work with neatly categorizable data. Today’s data landscape is very different. The technology needed to work with Big Data is widely accessible and data science — as a technical skill and a management strategy — is institutionalized. Growth in the discipline has been rapid. In 2019, postings for data scientists on Indeed rose by 256% over the previous year.
“The volume of structured data is growing faster, driven by more organizations using metadata to contextualize unstructured data and by consumers and organizations continuing to adopt and deploy connected IoT devices,” said John Rydning, research vice president of IDC’s Global DataSphere.
The use of metadata and leading-edge data science tools are among the technical skills taught in MSDS programs. At Stevens, aspiring data scientists work with IBM’s Blue Mix and Google’s TensorFlow to develop applications that use AI and machine learning — “deep learning” — in entirely new business application areas.
The Data Science Landscape Is Changing
In the past decade, the number of professionals who could accurately and efficiently use advanced data analytics to drive real-world change has been too low to meet the demand. On the one hand, businesses didn’t have a complete understanding of the roles and responsibilities data-oriented teams would need to cover. On the other, many first-wave data professionals couldn’t keep pace with automation and AI technologies or manage increasingly large and complex datasets. A 2021 NewVantage Partners survey found that 99% of firms surveyed wanted to leverage the power of data but could not find the needed talent. Everyone knew data was useful, but no one knew what it would take to harness its utility.
While skills gaps still exist, data science is no longer as mysterious. It’s an established business concept both employees and companies are beginning to understand and utilize on a practical level. Professionals are hustling to upskill, and employers are looking for data professionals with advanced degrees who can work alongside automated analytics and data cleansing tools. When hiring, they seek out applicants who bring foundational and advanced knowledge to the table.
According to a report by QuantCrunch, 39% of data scientists and advanced analysts have a master’s or PhD. This suggests master’s degrees are not required in every data science job but help candidates stand out for some of the most competitive or well-paid positions. Financial quantitative analysts, for example, earn an average of about $103,600 a year, according to that same report, and 47% of job postings for this position require a master’s degree or higher.
MSDS Programs Offer Career Support
The best MSDS programs offer more than just classroom instruction. They provide opportunities for students to participate in real-world projects that attract recruiter interest, furnish networking opportunities through strong faculty and alumni connections and provide plenty of student support so that MSDS candidates do not feel alone when navigating their career paths.
Stevens Institute of Technology has won the 21st Century Award for Best Practices in Distance Learning seven times because our distance learning platform melds online learning and career building. The faculty is multidisciplinary, with experience in fields such as planning, public policy, finance and healthcare. Distance learners pursuing the MSDS can connect with a global network of more than 50,000 alumni. Students studying remotely at Stevens also work with Success Coaches who help them achieve their academic and professional goals while enrolled in the program.
Data Science Resumes Made Stronger with an MSDS
There’s nothing inherently wrong with going to a data science or data analytics bootcamp, especially to pick up a specific new skill or refresh an existing understanding of the fundamentals. But for those who want to add something to their data science resumes that will stand out in an increasingly competitive field, the MSDS is the best and most holistic option.
Data science master’s students usually participate in hands-on projects during their time in graduate school, earning valuable practical experience. They also add new skills in applied machine learning, Hadoop, programming, database management and more to their data science resumes. They may be able to draw upon impressive references from faculty members, alumni or fellow students already working in the field.
Additionally, a master’s in data science can give professionals interested in working remotely a leg up. The most desirable remote data science jobs offer flexibility or even full-time work on distributed teams. These positions tend to go to highly skilled, highly experienced and highly credentialed applicants with a robust data science resume. The more skills, experience and education an applicant has, the more latitude they have to negotiate for the working conditions they want.
If You Want to Become a Data Scientist, Earn an MSDS
Data science and machine learning professional Jack Raifer Baruch summed up the core issue with data science bootcamps in an assessment of his “subpar” experiences in one. “You can learn some of the basics and techniques,” he wrote, “but the nuances of working with data, comprehending the mathematics (at least statistics and probability) and feeling comfortable working with data and building models, will take quite a long time and a lot of actual working with data.” That’s why, if you are serious about becoming a data scientist, you should earn a master’s in data science.
The evolution of Big Data collection, computer processing and AI coupled with the business demand for data-driven insights has created an employment rife with opportunity for those qualified to take advantage of it. Now may be the best time for aspiring data professionals — whether already working in the discipline or interested in moving to a new industry — to build your data science resume and pursue career development in a graduate program.