Today, data is everywhere, and organizations can quickly access massive amounts of it. People and systems generate new data at a rate of 7.5 sextillion gigabytes per day, making it easier than ever before to collect and warehouse data. Organizations face significant challenges, however, when it comes to using it.
Research from storage solution enterprise Seagate and technology consulting firm IDC found that businesses and other institutions use just 32 percent of the data they collect, leaving 68 percent untouched. This is unsettling but unsurprising. Data science is still a new discipline and organizations that want to leverage the power of data struggle with not only data analysis but also data team building, data privacy, information bias, and technology adoption and optimization.
The future of data science is hazy in some ways but crystal clear in others. There’s no question that the use cases of data science are expanding, but what the discipline will look like in five or ten years is uncertain. Data scientists planning out their careers have to consider questions such as: What will data science jobs be like five or ten years from now? Is the amount of data at our fingertips exceeding our capacity to use it? How does the growing flood of information impact current data science roles? How will automation affect data science jobs?
Whether you’re already a data scientist or a data engineer—or your goal is to become one—thinking about the future of data science can help you refine your long-term aspirations and make intelligent career decisions. Tufts School of Engineering‘s online Master of Science in Data Science program supports the data science careers of the present and develops data scientists who can adapt to the future. You can complete the 10-course, 32-credit hour data science master’s in under two years while working full-time—and immediately apply the machine learning models, data analysis, data infrastructure, and data mining skills you learn in class in your organization. Keep reading to learn more about how you can meet the future of data science head-on.
What Is Data Science?
Data science is a complex subfield of computer science that integrates business intelligence, programming, mathematics, statistics, and several other disciplines. Dr. Ganapathi Pulipaka, Chief Data Scientist at Accenture, describes data science as an amalgamation of “software engineering, predictive analytics, machine learning, deep learning, HPC, supercomputing, mathematics, data mining, databases (SQL, NoSQL), Hadoop, streaming analytics platforms for live analysis (Apache Kafka, Apache Flink, Apache Spark, Apache Impala), IoT platforms, edge computing, fog computing, networks, statistics, web development, cloud computing, data engineering, and data visualization.”
People think of data science as a tech discipline, but the applications of data science transcend technology. Across industries as diverse as agriculture and finance, data scientists leverage powerful software, tools, and models to find valuable patterns hidden in raw information. Organizations use the insights hidden in data to set dynamic prices, predict future inventory needs, enhance workflows, manage systems in real-time, make processes more efficient, and answer abstract business questions.
Is Data Science Just Analytics Repackaged?
The short answer is no. While there’s some disagreement among technologists about how data analytics and data science differ, most agree that there are significant differences between the two. For example, the Institute of Apprenticeships & Technical Education asserts that data analysts “collect, organize, and study data to provide business insight.” In contrast, data scientists “find information in diverse data sets to address complex problems and improve organizational processes.”
The gap between data science and data analytics has indeed narrowed to some degree thanks to tools that simplify processes for non-specialists. Laypeople without a great deal of data science training can now visualize some forms of data and build simple statistical models with little help. However, there are still many tasks only skilled data scientists can take on. Big Data is still a domain of highly trained specialists, and only data scientists use data to develop testable predictions.
As CompTIA puts it, data analysts analyze data. Data scientists create “new processes for data modeling using algorithms, predictive analytics, and statistical analysis” and have the “technical skills to arrange unstructured data and build their own methodologies and frameworks.”
Someday, the gap between data science and data analytics may disappear. For now, education and technical expertise define that gap. It’s telling that 90 percent of all data scientists hold advanced degrees, such as Tufts’ online MSDS. Almost half of all data scientist job openings require applicants to have a master’s degree, whereas just 6 percent of data analyst job postings ask applicants to have advanced education.
Where Is Data Science Headed?
Every few years, headlines proclaim the imminent death of data science. Some articles list the cause of death as obsolescence—the theory being that APIs and pre-packaged algorithms will replace data scientists. Sometimes automation kills data science. There are futurists who predict we won’t need data scientists once computers powered by artificial intelligence can do the same work. And some sources predict market saturation will be the death of data scientists. As more analysts and engineers pick up data science skills, employers won’t have to pay top-dollar for career data scientists.
However, there’s no actual evidence that data science is on its deathbed and plenty to suggest that this discipline will continue growing. There is no data science bubble. What is happening is that data science is changing. It was once a niche discipline that even those working in it could not easily define. Now it is much more segmented and easier to see how data scientists deliver value. Some data scientists handle model development. Others do analysis. Still others adapt AI for technical implementation. Many data scientists who might once have been generalists now specialize in software engineering, deep learning, data mining, data visualization, or data architecture.
As more organizations invest in data science implementation, the discipline has become more value-driven. There was a time when simply having a data scientist on staff was enough. Executives and stakeholders who didn’t truly grasp the power of information hired data scientists to position their organizations as technologically progressive. Today, organizations expect data scientists to deliver insights that drive quantifiable enhancements. Far from being the end of this discipline, it is the beginning of a new era of data science.
Why Data Science is the Future of Everything
Big Data is playing an increasingly substantial role in all sectors. Very few sectors are untouched by data science’s impact and influence. Data powers forecasting and business decision-making in manufacturing, marketing, energy, business management, healthcare, and many other fields—and will play an even more significant role in the future as investment in data goes up. The practical applications of data science are broad. It even impacts our personal lives. Whether in recommendation engines or diagnostic software platforms, data science powers the world.
For example, data science informs treatments, diagnoses diseases, and makes medicine more accessible in healthcare. In retail sales, data science powers targeted rewards programs, enhances marketing, helps manage inventory pipelines, personalizes the customer experience, and boosts profits. Boston Consulting Group recently found that companies that implement data-driven marketing see a 20 percent increase in revenue. Data science helps organizations meet net-zero carbon emissions goals and gives scientists access to predictive models of humanity’s environmental impact. In energy production, data science lets responsive grids predict energy demand and adjust capacity in real time.
The impact data science has on our lives is already profound, but professionals in this space have only just begun to explore what data can do. In the coming decade, data scientists and researchers will likely discover applications of data science no one has dreamed of yet.
Why a Master’s in Data Science Is Worth It
The global data market has grown at a healthy pace year over year. It increased from $122 billion in 2015 to $187 billion in 2019, and the IDC predicts it will reach $274 billion in 2022. Organizations that don’t embrace data science are at a distinct disadvantage and risk losing customers, market share, and profits. Data science is mainstream, no longer the exclusive domain of tech companies such as Amazon, Facebook, and Google. Smaller organizations outside of tech now have to invest in data science to keep up with the competition. Unfortunately, this is easier said than done given a data science talent base that is smaller than it needs to be.
While 99 percent of firms report investing in data, many struggle to attract and retain the talent they need to implement data strategies. Right now, demand for data specialists still outstrips the supply of professionals with strong data science skills. That’s bad news for organizations that want to use data effectively but good news for new data scientists, as the talent gap continues to drive data science salaries up. According to the latest Robert Half Salary Guide, skilled data scientists earn about $135,000. Data scientists who specialize or land jobs at the big tech firms can earn a lot more.
If you meet the MSDS admission requirements, enrolling in a part-time, online data science master’s program is a straightforward way to prepare for a future driven by data. As a distance learner in the master’s in data science program at Tufts, you’ll acquire the technical skills necessary to meet the increasing need for qualified data scientists. You’ll also receive individualized career guidance from industry leading faculty, access to state-of-the-art technology and resources, and a foot in the door at local technology firms thanks to the university’s reputation for excellence in the Greater Boston region. The School of Engineering also maintains a Piazza dashboard where professors and faculty post internship opportunities, information about scholarships, hackathons, professional development opportunities, career fairs, and research opportunities and initiatives that can help you advance in your data science career more quickly.
Data science is a field with high demand and high salaries for those qualified to step into it. Now is the time to apply to earn the credentials you’ll need to compete now and in the future. As the influence of data science continues to grow across industries, employers will increasingly look for data scientists with the advanced credentials, high-level technical skills, and the soft skills necessary to move industries forward.