Positive Long-Term Trends in Data Science Careers
Data Science has long been making headlines as a top career. Famously, Harvard Business Review, quoting economist Hal Varian, dubbed it “the sexiest job of the 21st century.” There’s good reason for the attention. The field continues to land high on the lists of best careers, and a master’s in data science salary can reach the mid or upper six figures. The U.S. Bureau of Labor Statistics reports that the field will experience a 27.9 percent rise in employment through 2026, and LinkedIn has found a shortage of about 150,000 data scientists across the United States.
But technology, business goals, and economic headwinds change rapidly. So, what does the future look like for data science careers? For someone considering data science, it’s a good time to get a better understanding of the field.
What is Data Science?
Data science is a multidisciplinary field that combines business strategy, information technology, modeling methods and algorithms. Data scientists rely heavily on programming languages such as Python, R, and Go to tame data flowing from the Internet and the increasing number of devices connected to the Internet. Data scientists use tools to collect, prepare, evaluate, and analyze data. They build and test data models to find patterns and trends to make predictions and solve business problems. Data scientists find meaning from raw data to help organizations make more effective decisions. Data scientists make data actionable.
“Competitive advantage comes from analytics and modeling, from models that are yours and yours alone, not from systems that others have developed and sell to the world. There will always be a need for people who can program and build new things.”
Thomas Miller, Data Science Faculty Director, Northwestern University
How Data Science Began
While analyzing information is hardly new — data analysis formally began with the first computers in the 1960s — the term “data science” was not coined until the early 2000s. The field grew as computers became more powerful and sources of data proliferated.
In 2020, online users created 1.7 megabytes of data per person per second. Data are generated every time someone searches on Google, remotely connects to their Nest cam or smart refrigerator, shares an image on Instagram, schedules an appointment online, or buys something from Amazon, to name just a few sources of data. Growing mobile data traffic and cloud computing also contribute to the increasing volume of data sets that are too large or complex for traditional data processing.
What is Data Science Used For?
Nearly every person has experienced data science applications. Data science helps advertisers find you long after you’ve left an online store. Data science models help banks flag suspicious activity on credit cards. Airlines track delays. Many companies run chatbots. Amazon serves up recommended products. Leveraging data on viewer activity, Netflix suggests similar TV shows and movies, saving more than a billion dollars on customer retention.
But it’s not just the big tech companies that benefit from data science. Companies across industries adopt data-driven decision-making, achieving 5 to 6 percent in increased productivity.
“Forget about the hype of data science: AI, machine learning, robotics. If you're going to invest in data science (and you should), invest for the right reasons. Hire and train data scientists and data engineers because they are good at learning from data.”
Thomas Miller, Data Science Faculty Director, Northwestern University
Why Data Science Matters
Does data science do more than drive business revenue and increase consumer choice and convenience?
Absolutely. Data science is critical to meeting a range of broad social needs. The combined power of advanced computing and data science is enabling more precise prediction of storms and extreme weather. Biomarker analysis and other healthcare data applications are already leading to better, more efficient treatment for cancer and rare diseases and predicting future pandemics. Healthcare organizations have saved $300 billion making decisions informed by data science, money that can be applied to research or patient care.
When coupled with artificial intelligence (AI) and machine learning, data science is also helping engineers develop self-driving cars that sense accidents and create virtual experiences for the disabled. Even traditional farming is changing, with data-driven smart tools that tailor decisions about seed and fertilizer to sustainably grow more food, using less water and land.
Trends in Data Science Careers
1: Continued Need for Diverse Skills
The continued demand for data science skills is not limited to a few programming languages. Nearly 50 percent of current job postings list Python, data science and machine learning as requirements or as part of skill clusters. The most frequently mentioned baseline skills include communications, research, and teamwork. Non-analytical skills such as writing, planning and creativity appear in 10 to 15 percent of pure-play data science job postings.
While some data scientists focus on analytics and modeling, others take on data engineering roles, implementing models in information systems. Still others become technology managers.
Northwestern's master's of data science program allows students to select among five specializations: Analytics and Modeling, Artificial Intelligence, Data Engineering, Analytics Management, and Technology Entrepreneurship.
2: Access to Smarter, Faster AI
Rapidly improving technology has opened the door for more businesses to develop strategies that rely on data. Regular companies — not just tech companies — can more easily insert artificial intelligence (AI) or machine learning into operations. According to Gartner Research’s latest report on data science, 75 percent of enterprises will shift from piloting to operationalizing AI by the end of 2024, driving a fivefold increase in streaming data and analytics infrastructures.
New standardized data formats and investments in new computer chip architectures are paying off, creating opportunities for more scalable, accessible AI solutions with high impact. That means new startup possibilities and more demand for data scientists with knowledge of machine learning and AI. It’s one reason that experts like Peter Bailis, CEO of Sisu and co-principal investigator of the Stanford DAWN artificial intelligence project, believes job prospects for aspiring data scientists are strong.
Bailis told Datanami that “while universities have up leveled up and expanded the reach of their data science curricula, the demand for data scientists has also risen. I see a huge demand for data scientists and analytics across the board—both at the tech giants and in the broader private sector.”
3: Evolving Business Intelligence
Gartner also found that by 2023, more than 33 percent of large organizations will have analysts practicing decision intelligence, as opposed to business intelligence. As decision-making has become more complex, decision intelligence has evolved from traditional business intelligence (BI) platforms to include artificial intelligence and machine learning. This new focus is on specific business needs and goes beyond BI’s standard reporting and benchmarking deliverables.
Decision intelligence requires a data scientist to design, compose, model, execute and monitor decision models and processes. This is not just about pulling data reports from dashboards, but testing, measuring and learning to arrive at more meaningful organizational decisions. According to Gartner, “engineering decision intelligence applies to not just individual decisions, but sequences of decisions, grouping them into business processes and even networks of emergent decision making.”
4: Expansion across regions, industries, employers
The highest number of data science job postings appears in California (currently more than 40,000), but open positions can be found from coast to coast. New York, for example, had nearly 15 thousand open positions posted in October 2021. States such as Texas, Virginia and Massachusetts had between 10 and 14 thousand job postings. Washington, Illinois, North Carolina and Pennsylvania each had more than 6 thousand during that same time period.
Like the broad geographic range, the top 15 employers of data scientists reflect diverse industries, from tech giants such as Facebook, Amazon and Apple, to large healthcare providers such as Humana and Cincinnati Children’s Hospital. Manufacturers such as Applied Materials, banking corporations such as Wells Fargo and Capital One, and nationally known consulting companies such as Deloitte and Booz Allen Hamilton are all posting hundreds (sometimes thousands) of data-science-related positions. They are looking for software developers and marketing specialists (the most common roles, at 34.8 percent and14 percent of postings, respectively), as well as IT managers, network and systems engineers, business intelligence experts and general researchers.
How to Get a Job in Data Science
Data science typically requires a bachelor’s degree in IT, computer science, math or a related field. Often this preparation is followed by a master’s degree in data science or a related field and some on-the-job experience. Despite the complex skill set of data science, it’s a field that’s open to people from all walks of life and with diverse educational and professional backgrounds.
At Northwestern University School of Professional Studies, many of the students in the Data Science Boot Camp and the online Master’s Degree in Data Science do not have a typical IT background or experience. Our faculty is sensitive to the needs of adult learners and those entering the field from non-technical professions. We offer a wide range of core classes, electives and specializations covering the latest career trends, and hands-on learning to build experience and portfolios prior to employment. Our students find that aside from the academic strengths, the well-known and respected Northwestern name helps open doors and build their professional networks.