The digitization of so many aspects of our life has opened a window into our world in ways we could not have imagined a few years ago. From the statistics generated by wearable medical- and fitness-tracking devices to the digitizing of printed books and handwritten manuscripts to the analytics data that all digital platforms collect, more information is available to more people more easily than ever before.
Advances in artificial intelligence (AI) and other developments mean that we also have increasingly powerful and sophisticated tools for exploring and analyzing data of all kinds. AI allows us to “make machines that can do things we thought only humans could do,” says Jim Hetrick, director of Pacific’s new undergraduate data science program as well as its longer-standing master’s program. “It’s an explosive time for data science.”
Data scientists analyze data to answer—and raise—a plethora of questions in nearly every setting we can imagine. Data scientists are found in a wide variety of settings, from businesses to governmental agencies to think tanks to universities. They do work that includes gauging the success of social programs, predicting the geographic spread of diseases, and identifying ways to increase the performance of athletic teams.
Data science programs prepare students to do this work by teaching them statistical methods, programming languages and the use of analytics tools so that they can manipulate, interpret, explain and act on data wherever they are employed.
Data science vs. business analytics
While there are some similarities between data science and business analytics programs, the most basic difference between them is that business analytics programs typically offer a business degree.
The business analytics curriculum teaches data analysis in the context of business operations; data science is more open-ended. Business analytics majors interested in double-majoring are encouraged to find another business school major, but a data science student could benefit from finding a second major in nearly any subject.
Business analysts are focused on business objectives: increasing efficiencies, growing the business and increasing its bottom line. They tend to be process-driven, and business analytics programs tend to focus on using tools including Excel and Power BI.
Data scientists are more outcome-driven. Consequently, data science programs tend to require more math, statistics and programing classes.
Nine data science career paths
The field of data science is expected to grow by 32% by 2032—much faster than the 3% expected growth rate for all fields combined, according to the U.S. Bureau of Labor Statistics. Here are some of the careers open to data scientists:
- Crime analytics: Crime analysts support police departments in a wide variety of ways, including creating threat assessments based on historical data and intelligence, creating data visualizations for presentation in court, and suggesting locations that could benefit from increased police presence or methods for deterring crime.
- Data engineers: Data engineers are responsible for collecting and curating data in preparation for analytical uses. They also are often responsible for providing data to stakeholders, so they must know how to extract data using whatever methods are necessary. If you’re interested in becoming a data engineer, you should consider double-majoring in computer science and data science.
- Geospatial analysis: Geospatial analysts use geographic information systems technology and other tools to analyze location-related data. They work in fields as diverse as urban planning and military intelligence. Project could include predicting which neighborhoods’ streetlights are most likely to need maintenance soon or determining which areas might be at the highest risk of a catastrophic wildfire.
- Government and public policy: Data analysts working for government agencies or nonprofit organizations study the effectiveness of existing and potential programs to understand how they could be improved upon; examine inequities in society to determine how to best address them; and investigate ways to make services available more efficiently.
- Health care analytics: Health care analysts use data to find ways to improve patient care and reduce inefficiencies in the use of hospital resources, time and billing; to create data visualizations to model public health data; and use public data on social determinants of health to predict disease prevalence and spread.
- Image analytics: Data analysts process images using programs, including AI tools, to extract information from images. This can include recognizing letters and numbers and converting them to text, using facial recognition for sentiment analysis, and testing visual regressions in web development as well as photo identification and organization.
- Marketing analytics: Marketing analysts study all aspects of the sales funnel to optimize tactics and increase sales. This can include analyzing email and social media marketing metrics, studying subscriber or customer churn, ticket pricing and more.
- Sports analytics: Sports analysts find ways for players and teams to improve by studying game film and sports data. At Pacific, math professor John Mayberry and water polo coach James Graham have collaborated with students to improve the water polo team’s performance.
- Thought leadership: There is a need for thoughtful, well-informed writers and speakers on ethical and social uses of AI. “You want to give them the values that we aspire to, but they’re looking at the data on how we actually act,” says Hetrick. There are ample opportunities to explore the possibilities and perils of AI in contexts ranging from its economic impacts to the challenges it poses to intellectual property rights to its role in the practices creative and academic life.
The possibilities are nearly limitless. “There is no organization that doesn’t have data that they have to wrestle with,” says Hetrick. “Nobody I’ve talked to has ever said, ‘Thanks, but we understand all of our data.’”
Data science at Pacific
Pacific’s Bachelor of Science in Data Science is designed to encourage participation by students who might not be immediately drawn to majoring in a STEM discipline, while allowing those who want a more technical education to dive deep into it. The program is an interdisciplinary collaboration between the College of the Pacific and the School of Engineering and Computer Science.
Data science degree requirements can vary. The technical depth of Pacific’s program depends on the track chosen: Data Engineering, Decision-Making, Storytelling or Predictive Modeling and Machine Learning.
“A strong math background is incredibly helpful, but it’s not necessarily a detriment if you don’t have one. Motivation and a growth mindset are more important,” says Mayberry, who was instrumental in creating the undergraduate data science program.
All data science students learn the needed fundamentals of math, statistics and programming.
Students are encouraged to double-major to either expand the breadth of their knowledge and skills or to double-down on the technical side of the degree. Recommended second majors include economics, political science, biology, math, English, art, psychology and engineering.
Regardless of the track chosen, Pacific’s data science education includes a strong ethical and philosophical component. We need to learn “how to use these systems to augment our humanity rather than replace it,” Hetrick says. He instructs his students to consider “not just ‘What are the tools?’ but ‘How do we responsibly use them?’”
Experiential learning is part of that: a student’s capstone requirement can be fulfilled by completing an internship or completing a project under the direction of a faculty member.
Data science internships
Internships involving data science are becoming easier to find as the field grows. Here are a sampling of the internships Pacific students have done recently:
Internships involving data science are becoming easier to find as the field grows. Here are a sampling of the internships Pacific students have done recently:
- For Gallo Winery, analyzing tank fermentation to optimize the process; examining customer churn; and analyzing email marketing efforts to understand who was unsubscribing and why.
- For the St. Joseph’s hospital system, analyzing patient care data to improve care.
- For the Sacramento Kings, ticket pricing.
- For Sparta Healthcare, analyzing jump assessment data to determine which athletes were at greatest risk of rotator cuff injuries.
- For the San Francisco Municipal Transportation Authority, analyzing the locations where parking meters were frequently being damaged or destroyed.
- For the Oakland Roots Sports Club, a professional soccer team based in Oakland, analyzing game statistics to discover gaps in their offense and defense, and which available players fit those gaps, to guide recruiting efforts.
Master’s degree in data science
For students who want a career that goes deep into data science, graduate education can help. Accelerated 4+1 pathways to Pacific’s Master of Science in Data Science are in place for students in the BS in applied mathematics and BS in economics programs, and an accelerated pathway to the master’s degree for data science undergraduate students will be made available.
Learn more about Pacific’s data science program.