If you’re a data scientist, you probably recognize the names DJ Patil and Jeff Hammerbacher. Not only are both often credited with popularizing the term “data science,” but they also exemplify the modern data scientist—that is, one who applies his or her data-savvy expertise in any setting that demands it, including healthcare, e-commerce, social media, and journalism—just to name a few.

Patil, the chief data scientist at the United States Office of Science and Technology Policy, boasts an extensive resume that includes stints at LinkedIn, Greylock Partners, Skype, PayPal, and eBay. [Read more…]

In psychology, data is everything. Without data, it’s difficult to quantify findings that lead to more effective care and positive outcomes. Making data actionable requires the insights of a savvy psychologist with a background in data science who can help others understand what data is truly saying about human behavior—and what it could mean going forward.

Data science—particularly research methods and statistics—enables psychologists to dig deeply into important challenges we face in today’s society. To drive change effectively, psychologists must know how to conduct laboratory or field experiments. Doing so creates data to support evidence-based strategies that help people tackle barriers and live more comfortable and satisfying lives.

However, data’s reach extends far beyond traditional psychological applications. Consider the psychologist who analyzes data to design more appropriate teaching methods for students with learning disabilities. Or one who conducts research to understand the best marketing strategy for a business. Or perhaps one who examines human responses to natural and technological hazards to better understand why people do or do not conserve resources or why and how diseases spread. Without data, developing solutions to address these challenges would be nearly impossible.

Advancing Your Psychology Career Using Data Science

Data science skills in psychology are not only in-demand, but they also yield lucrative salaries. Consider the following high-paying psychology jobs that benefit from a degree in data science:

1. Industrial/organizational (I/O) psychologist. These individuals use data analyses to help companies make more informed decisions. They provide insight not only into trends and patterns in the data but also human behavior related to that data, helping to address workplace issues that affect individuals, teams, and organizations. The national average salary for an I/O psychologist is $105,000, according to AllPsychologySchools.com. In 2021, the Bureau of Labor Statistics reported a mean salary of $113,000 for I/O psychologists. Those who are self-employed consultants earn the most, followed by those working in pharmaceuticals and private sector healthcare.

2. Research psychologist. These individuals collect and manipulate data to investigate psychological issues related to perception, memory, learning, personality, and cognitive processes. They also analyze test results using statistical techniques and collaborate with other scientists to formulate theories. According to Glassdoor.com, the national average salary for a research psychologist is $103,000; ZipRecruiter.com reports it at $86,000.

3. Neuropsychologist. These individuals study behavior and brain function using imaging techniques and behavioral assessments. According to Indeed.com, the national average salary range for a neuropsychologist is $87,000 to $237,000.

4. Forensic psychologist. These individuals conduct research related to the criminal justice system, helping with jury selection or mental state examinations of criminal defendants. Often times, forensic psychologists are focused on determining why a certain type of person commits crimes and how to prevent those crimes from happening in the future. According to Indeed.com, the national average salary for a forensic psychologist is $104,000.

Intersection of Data Science and Psychology

Big data has permeated many academic disciplines, and psychology is no exception. As data continues to proliferate due to technological advances, psychologists with a background in data science, research methods, and statistics can help articulate why certain patterns and trends may occur. They may also be able to explain how technology continues to shape human behavior.

For instance, take the study of Computers as Persuasive Technology, better known as “Captology.” Coined by Dr. B.J. Fogg, this term describes the manner in which we “…use computers to interact with people and attempt to influence them to change their attitudes or behaviors” (Fogg, 2002). With technology now occupying every corner of the world, we are all susceptible to its overreach.

In an article from Trust Technical Services, Issam Wadi writes that technology such as this permeates our politics, education, organizations, behavioral sciences, and more. We also see this on a micro-level in our day-to-day lives: advertisements persuading you to purchase that soap you never liked but will now buy because your favorite celebrity endorses it, or your favorite video game making you think differently about how you see the world through their lens. Through the intersection of technology and persuasion, there is an indefinite change in human behavior and attitudes toward the world around us. 

The Need for Data Science Professionals in Psychology

Data science professionals in psychology are essential. These professionals can help ensure data governance, proper data mapping, and suitable data application. They will increasingly be in demand as employers seek to capitalize on the inherent insights of big data. What are the trends, and why? How can various business sectors respond accordingly? Psychologists with data science skills can help find the answers.

For more information about data science, explore the University of Wisconsin Master of Science in Data Science website or speak with an enrollment adviser at 608-800-6762 or learn@uwex.wisconsin.edu.

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Picture this: The bases are loaded at the bottom of the ninth inning. A batter steps up to the plate, scuffs his sneakers against the mud-caked marker that signals home, and glares at the pitcher’s mound. The crowd roars as the pitcher nods to the catcher and winds up for the throw. In the blink of an eye, the fastball slices through the air at nearly 90 miles per hour. Will the batter get a hit and enable at least one run? Or will he miss and disappoint his team?

To answer this question, you might choose to focus on the usual statistics—batting average, RBIs, hits, and stolen bases. The player’s overall past performance is usually an indicator of his future success, right?

What if these statistics aren’t the best predictors of whether a player can perform under pressure? This is the premise behind moneyball theory, which emphasizes only two important data points as predictors of a player’s abilities:

1. Slugging percentage: Total bases divided by at-bats.
2. On-base percentage: The rate at which a batter gets on base for any reason excluding fielding errors, fielder’s choice, fielder’s obstruction, or catcher’s interference.

Essentially, moneyball theory seeks to answer these two very basic questions: Can the player hit? Can the player create runs? If the answer to one or both of these questions is “yes,” chances are you’ve got a good pick on your hands—and one who can perform when duty calls.

At least that’s what Oakland A’s general manager Billy Beane believed in 2002 when he used moneyball theory to pick a team of undervalued players that would eventually go on to achieve a 20-game winning streak and clinch the American League West. These moneyball-inspired picks came in the wake of Beane losing three highly valuable players—Jason Giambi, Johnny Damon, and Jason Isringhausen—to free agency.

Moneyball Theory in Action

Using data analytics and moneyball theory, Beane hired the best players he could with an extremely limited budget for payroll. With approximately $41 million in salary, the Oakland A’s ultimately competed with larger market teams such as the Yankees, who spent over $125 million in payroll during the 2002 baseball season.

Exactly how did he do it?

Beane performed data mining on hundreds of individual players, ultimately identifying statistics that were highly predictive of how many runs a player would score. These statistics weren’t necessarily numbers that baseball scouts traditionally valued. Instead of competing for high-priced home-run hitters with high batting averages, he sought lower-cost players with high on-base percentages. His theory was that players with a higher on-base percentage would be more valuable than those with lower on-base percentage even when those with the lower percentage ultimately hit more home runs and were faster and even stronger. He also encouraged players to focus on walks, thereby forcing pitchers to throw strikes to ensure an out.

Effects of Moneyball Theory on Baseball

Between 2000 to 2006, the Oakland A’s went on to average 95 wins, capture four American League West titles, and make five playoff appearances. Although baseball scouts and general managers initially scoffed at moneyball principals, they slowly began to realize the validity of the theory and sought to take advantage of it. The Red Sox tried to hire Billy Beane but were unsuccessful. Instead, they hired Bill James—the creator of sabermetrics on which moneyball theory is based—in an advisory capacity.

Over the years, Beane’s moneyball theory has had a lasting legacy in baseball, allowing teams with significantly lower budgets to choose players that would allow them to successfully compete with big-market teams such as the Red Sox and Yankees.

According to a 2013 article on MLB.com, “Moneyball has played a role in 15 of 30 teams getting into at least one postseason series—not a Wild Card Game, but a postseason series—the last three years. Moneyball may also be why nine franchises have won the World Series the last 13 seasons.”

What Data Scientists Can Learn from Moneyball

Today, the story of Billy Beane and moneyball theory is famous. It was the subject of Michael Lewis’ book Moneyball and the film of the same name. But according to Forbes, “What’s interesting is that as widely-familiar as the story is, it is almost as widely misunderstood.”

Analyzing data was nothing new to baseball in 2002. Data on baseball players has been available since the 1800s and data analytics used since the ’70s.

The reason Beane’s strategy was ground-breaking is because he “had the courage to use the insight gleaned from data analytics to drive the way he ran his business… ‘Moneyball’ succeeded for the Oakland A’s not because of data analytics but because of Beane, the leader who understood the analytics’ potential and changed the organization so it could deliver on that potential.”

His story should resonate with data scientists. It speaks to the advantage of making data science part of an organization’s DNA, but just as importantly, it highlights how a big idea about big data can translate to serious business gains.

Interested in the online 36-credit University of Wisconsin Master of Science in Data Science? Start exploring the degree program here.

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You’re in the health information field, but you want to expand on your expertise or explore other fields… Are you the type to geek out over numbers, equations, and statistics? Do you frequently question existing assumptions and processes? If the answer to either or both of these questions is ‘yes,’ then data science—an interdisciplinary field that focuses on the use of data to inform decisions and make new discoveries—may be a good fit for you.

What exactly is a data scientist?

“A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization,” says Anjul Bhambhri, vice president of big data products at IBM.

Data scientist is one of the five best technology jobs, according to the 2022 US News Best Jobs report. Not only is the job in demand, but it’s also highly lucrative.

According to the U.S. Bureau of Labor Statistics, it is projected that the employment of data scientists will grow 36 percent from 2021 to 2031, approximately creating 13,500 job openings. And increasingly, these companies are in the healthcare sector. As big data continues to grow commensurate with electronic health records, data scientists are the ones who utilize healthcare analytics to manage and merge data sources, create visualizations, build mathematical models, and present insights—all with the goal of producing answers, predictions, and calculations as quickly and accurately as possible.

A simple search for “healthcare data scientist” on LinkedIn generates more than 300,000 jobs nationwide, many of which require previous healthcare experience and knowledge of patient data.

According to Glassdoor, data scientists earn an average annual salary of $114,067. The need for big data skills continues to lead to pay increases— approximately 20 percent as of 2021, according to Burtch Works.  It’s probably fair to assume that those working in healthcare—and who possess a formal health information management education—may earn even more due to their specialized knowledge.

Advancing your career

According to AHIMA’s Career Map, many employers actually require—or strongly prefer—a master’s degree for certain types of data-related positions such as informatics researcher, data analyst, research and development scientist, project manager, director of clinical informatics, or chief clinical informatics officer. A master’s degree in data science certainly fulfills these requirements.

Even if you don’t intend to advance your career in the immediate future, a degree in data science can make you a more effective health information management professional. By building a solid foundation in healthcare analytics, computer science and applications, communication, modeling, statistics, analytics and math, those with a working knowledge of data science are able to:

• Collect and report on complex data
• Communicate findings to both business and IT leaders
• Apply data to business problems

Relevant healthcare topics in data science

The following data science coursework is also particularly helpful for individuals currently working in health information management:

1. Data warehousing. Health information management professionals can use data warehousing skills to collect, clean, and prepare data stored in the electronic health record and various other electronic systems. Being able to evaluate data in terms of its source, volume, frequency, and flow is an important part of ensuring effective health information exchange.

2. Big data computing. Health information management professionals can use big data computing skills to answer important questions, such as: How can the organization reduce readmissions or prevent hospitalizations? What is the root cause of certain denials? Can the organization justify expanding a particular service line? Should it join an Accountable Care Organization or purchase a physician practice?

A 2011 study by MGI and the McKinsey Global Institute found that there will be four to five million jobs in the United States requiring data analysis skills by 2018. The report also found that the healthcare sector could create more than $300 billion in value annually if it were to use big data and healthcare analytics to creatively and effectively drive efficiency and quality. The ability to perform big data computing puts health information management professionals at the forefront of the big data revolution.

1. Data communication. Health information management professionals can use data communication skills to educate decision makers about the nature, structure, and interpretation of coded data—including ICD-9 vs. ICD-10 as well as a whole host of other clinical and non-clinical data.
2. Data mining. Knowledge of data mining can benefit health information management professionals frequently who frequently generate reports necessary for clinical and operational improvement. Accessing and analyzing unstructured data helps health information management professionals create a more well-rounded and insightful clinical picture.
3. Ethics of data science. Being able to discuss the privacy, intellectual property, security, and integrity of data is important aspect data management, release, and exchange in an increasingly electronic health information management environment. Those who are well-trained in the ethics of data science can contribute intelligently to these types of conversations and drive change within their organizations.

Finding the time to benefit your field

Obtaining a master’s degree in data science will invariably help health information management professionals tackle these ongoing challenges within the industry:

• Raise awareness of the intersection between health information management and big data
• Justify a seat at the decision-making table
• Capitalize on the power of healthcare analytics in the electronic health record

As a working adult, though, it can be hard to find the time to pursue a graduate degree. Fortunately, the University of Wisconsin Master of Science in Data Science was designed with adult learners in mind. It’s 100 percent online, making it easier to fit into an already-busy schedule. Visit the UW MS in Data Science homepage to learn more about this degree and find out if it’s right for you.