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Why Combining Psychology, Research Methods and Statistics Can Lead to a Powerful Data Science Career

September 19, 2016 By UW Data Science Team Leave a Comment

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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.

Research methods and statisticsData 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.
  1. 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.
  1. 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.
  1. 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.

More Data Science Stories

The Story of Moneyball Proves Importance of Both Big Data and Big Ideas

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The Story of Moneyball Proves Importance of Both Big Data and Big Ideas

August 24, 2016 By UW Data Science Team Leave a Comment

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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?

moneyball
Source: Wolf Gang, CC BY-SA 2.0

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.

moneyball
Source: Darryl Leewood, CC BY-SA 3.0

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.

Read More Data Science Stories

Can I be a Data Scientist? Healthcare Analytics in Health Information Management and Technology

Data Science vs. Data Analytics: The Differences Explained

Why Combining Psychology, Research Methods and Statistics Can Lead to a Powerful Data Science Career

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Can I be a Data Scientist? Healthcare Analytics in Health Information Management and Technology

July 19, 2016 By UW Data Science Team Leave a Comment

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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.
  1. 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.

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Data Management: An Interview with a Career Professional

November 10, 2015 By UW Data Science Team Leave a Comment

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Missy Wittmann, enterprise data strategist at American Family Insurance, shares her thoughts on big data, the challenges facing data managers, and what makes the new University of Wisconsin Data Science program unique.

The field of data science is exploding as fast as the world is creating data. But what is behind this growth, and what does it mean for business? To find out, we spoke with Missy Wittmann, a data management leader and evangelist for more than 20 years. In this exclusive interview, Ms. Wittmann talks about her experiences, her influences, and her advice for those considering a career in data science.

Missy Wittmann, data enterprise strategistPlease tell us about yourself and your background.

Certainly! My name is Missy Wittmann and I serve as enterprise data strategist at American Family Insurance. I am also president of the Wisconsin Data Management Association (DAMA) Chapter and vice president of Chapter Services for DAMA International.

I have worked in data management for 20 years. You could say it’s a passion of mine!

What do you do as enterprise data strategist?

I analyze how we define, model, source, and quantify data across the enterprise. My team and I look at data in the context of the business’s needs and its customers’ needs. Our goals are to help the business win new customers and retain existing ones. At AmFam, we have a data lab we use to find patterns, make predictions, and improve customer service and satisfaction.

I talk to the business side a lot. When the company runs TV commercials, for example, we collect and analyze data from social media sites to learn how well our ads are received. Are we speaking in the same voice as our customers? Has an ad resonated so well that it’s gone viral on Facebook or Twitter? The insights we find influence our strategies.

How did you get your start in data management?

I am very fortunate to have been in the right place at the right time. As I said, I’ve been in data management for 20 years. I have worked at American Family for 30. At some point, I got involved on a data project and became passionate about it. Data drives so much in our daily lives. How can you not be interested in that, or excited by it?

My employer recognized my interest in data management—as well as the importance of data to the organization. The company supported me with professional development and other educational opportunities to foster my knowledge and help me grow my career in this field.

Tell us about DAMA.

The Data Management Association is a users’ group. We provide training on data management. It’s a way for data practitioners to meet, network, and share ideas. DAMA meets quarterly and hosts internationally known speakers. We are growing. We draw people from all over Wisconsin. People of all ages and walks of life are coming into data management these days, particularly students.

The opportunity to hear from guest speakers is one of the best parts of DAMA membership. We recently hosted Bill Inmon, the grandfather of data warehousing. He spoke about the future of the field. To hear him speak anywhere else would cost a lot of money.

Why is big data getting so much attention right now?

big data collageBecause it’s huge! Today’s world is generating more data than ever. More and more people want to get hold of that data. They want to peel the onion and find ways they can use it to make better decisions—about business, about health care, about energy and finance and education . . . the list goes on and on.

Businesspeople in particular have discovered how data can help them uncover insights, opportunities, and solutions. These folks are clamoring for more data and more ways to use it.

Today’s reporting tools really help them get to the heart of things. For example, Google Analytics is making it easier for less-technical people to find patterns and actionable insights. With this kind of reporting, organizations can see just how they are doing, and how they can do better. It’s a fun place to be.

What are your major challenges as a data management professional?

The big challenge is in collecting the right data. It has to be useful. You can be in an environment like Hadoop—you can collect all the data you want, but is it useful and valuable? Does it contain the information we need? Oftentimes, it takes creative thinking to figure out how seemingly useless data can in fact be very valuable.

After that, we need to be able to present the data and our findings in a way that makes a difference. That means creating visualizations to help us communicate clearly and persuasively to leadership and nontechnical audiences in language they can easily understand.

What kinds of organizations need data scientists?

Everybody needs data scientists! I really don’t think there’s an area that doesn’t.

Take Wisconsin, for example. We do a lot of farming here. Data analytics allows growers to track crop production from year to year and make decisions on what to plant the next season based on weather and moisture.

Wisconsin breweries use analytics to identify and predict sales patterns, inventory levels, and customer preferences.

Insurance companies like AmFam need data scientists to help them assess risk, improve pricing, create custom plans and services, and built customer relationships.

Again, everyone needs data scientists. Manufacturing, retail, energy, finance, medicine—everyone.

Who or what has influenced you most in your career?

Karen Lopez is a great educator and influence. She is a renowned data management expert who speaks and blogs about data quality, data governance, data modeling, compliance, and more.

Steve Hoberman is another influence. He is a prominent data modeler, author, and trainer who was awarded DAMA’s International Professional Achievement Award in 2012.

Tell us about your role in developing the UW Master of Science in Data Science program.

I was approached by George Kroeninger, assistant dean at UW-Extension’s Division of Continuing Education, Outreach and E-Learning. He and the other founding program members were interested in talking with professionals in the industry, and as DAMA’s president, I was one of those.

So I joined the team as an adviser. One of the best things we did was run focus groups made up of business leaders. We asked them, “What are you not getting from your new data recruits? What skill gaps do you need to fill?” We were surprised by their answers.

One of the biggest things they told us was their employees lacked soft skills. These folks knew how to hunker down and analyze data, but they didn’t talk to one another. And communication in data science is key.

Data modeling is another area they mentioned. This isn’t really taught in-depth at universities—it might be one chapter in a book. But it really is the foundation for everything. It is the blueprint that determines how all of an organization’s systems integrate and talk with each other.

I enjoyed helping with public relations. I have often been the program’s spokesperson for interviews. I will soon be a co-chair on the advisory board. It’s exciting.

What do you think makes the UW Master of Science in Data Science program unique?

The program is unique in the way it was designed. We didn’t just sit down and make up a curriculum. We talked with employers and business leaders to find out what they want and need in data professionals. Then we built a program from the ground up to address those needs.

What words of wisdom do you have for students entering this program and the field?

This is a great time to get into the world of big data. Employers have a critical need for skilled data professionals, and that need will only grow as organizations generate and collect more and more data.

Companies are figuring out that they can find competitive advantages in data; ideas and insights they can use to get ahead their rivals. There aren’t enough data professionals right now to fill the available positions and accomplish the things employers want and need to. So the demand is huge.

Employers need skilled people so badly that some are even changing organizational policies in an attempt to attract top talent. For example, some places are offering telecommuting options for new hires who don’t want to move across the country.

I think a lot of this is due to the fact that the field of data science is still immature. If you are good, you can find a job in Silicon Valley making $250,000 a year.

What changes or growth do you see in the field over the next five years?

Businesspeople using dataI see more businesspeople continuing to get involved. Data collection, analysis, and visualization tools will mature, allowing more “regular” people to spot trends and uncover the insights they need to make better decisions.

The needs of the business will drive IT, and data scientists will be at the heart of it all.

Is there anything else you would like to share?

Data science is an exciting place to be. It’s a strong field and only getting stronger. Now is a great time to jump in!

One last question. What do you do for fun?

My kids keep me busy. I have three children (two boys and a girl) with my daughter still at home. She is a three-sport athlete. I follow her around to watch her play.

Many thanks for taking the time to share your experience and insights!

Are you looking to start or advance your career in data science? Find out why the online UW Master of Science in Data Science is a great program for aspiring and established data professionals. Call 608-800-6762 or email learn@uwex.wisconsin.edu to talk with a friendly enrollment adviser today.

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