Degree Program Archives | University of Wisconsin Data Science Degree

Make Summer Count: Take These Data Science Summer Courses, Graduate Sooner

February 1, 2023 By UW Data Science Team Leave a Comment

If you’re a student in the online UW Master of Science in Data Science program, summer can save you time.

How much? Consider this example:

Say you take two courses each spring and fall semester, and you take summers off. You would earn your Data Science degree in three years.

Now, imagine you take two courses during summer semester, too. If you did this, you’d graduate with your master’s degree in just two years.

Summer matters a lot. Make it count.

You might be a current UW Data Science student hoping to speed up your time to graduation. Or maybe you haven’t applied to the program yet and want one semester to ease into online learning.

In either case, now is a perfect time to start planning because registration for summer courses opens March 13.

Here’s the list of courses being offered this summer:

DS 700: Foundations of Data Science

This course provides an introduction to data science and an overview of commonly used data science tools, such as Excel, PowerBI, R, and SQL Server. It also highlights the importance of data in business decision making.

DS 710: Programming for Data Science

This course introduces you to two of the most widely used programming languages for data science: Python and R. You will gain experience working with real-world data and leave the course with skills you can apply in other courses and on the job.

DS 715: Data Warehousing

This course introduces you to working with and managing subject-oriented, integrated, time-variant, and nonvolatile collections of data that support management’s decision making processes.

DS 735: Communicating About Data

With this course, learn how to master technical, informational, and persuasive communication to meet organizational goals.

DS 740: Data Mining & Machine Learning

Explore data mining methods and procedures for diagnostic and predictive analytics. You’ll learn about association rules, clustering algorithms, tools for classification, and ensemble methods. Prerequisites for this course: DS 700 and DS 710.

DS 760: Ethics of Data Science

This course will take a deep dive into the ethical issues that are involved with computer and data science through analyzation of ethical arguments and decisions, as well as the utilization of case studies. Prerequisite for this course: DS 740.

DS 780: Data Science and Strategic Decision Making

Explore the current and future applications of data science as a strategic decision-making tool to achieve a competitive advantage in business. This course will focus on making decisions based on an organization’s data assets to aid in real-life business challenges.

DS 785: Capstone

This course delineates what is involved with the capstone project, its purpose, and the outline of what is needed to fulfill the project’s requirements. Prerequisites for this course: DS 715, DS 730, DS 735, DS 745, and DS 775.

If you’re a prospective student: Apply now for a Master of Science in Data Science.

If you’re a current student: Register for summer semester after March 13.

Take a Look at a UW Data Science Course: Foundations of Data Science

December 3, 2021 By UW Data Science Team Leave a Comment

“What are the UW Data Science master’s program courses like?”

We often receive this question from prospective students. So, we created this post to give you an inside look at the first data science course in the curriculum, DS 700: Foundations of Data Science—and to answer some of the questions that may be on your mind.

What will I learn in the course?

DS 700: Foundations of Data Science introduces you to data science and its importance in business decision-making. The course provides an overview of commonly used data science tools, such as R and SQL Server, along with assignments on descriptive and predictive analytics. There is a focus on statistics, programming, ethical perspectives, and strategic deployment of analytics to lay the foundation for data science applications.

At the end of this course, you’ll be able to:

Comprehend fundamental concepts in data science and analytics
Understand fundamentals of SQL and apply them to relational databases
Use RStudio for programming, using packages, debugging, and knitting Rmd files
Effectively utilize packages from Tidyverse for R
Read/write data of various formats using R
Create effective visualizations using ggformula
Perform linear and logistic regression, interpret, and graph the model
Perform exploratory data analysis with dplyr package in R and perform data cleaning, management, and manipulation with R
Use conditional statements, loops, and control flow logic in R
Interpret p-values

This course features lessons that consist of lectures, readings and other media, assignments, discussion prompts, quizzes, and a final project:

Introduction to fundamental concepts in data science and analytics
Introduction to SQL and relational databases
SQL Join and conditional flow statements
Advanced SQL Server operations (Transact SQL)
Intro to RStudio for programming, using packages, and knitting Rmd files
Intro to debugging in RStudio, linear and logistic regression, and graphs
Implement graphs in R, perform exploratory data analysis with dplyr
Implement joins in R, use control flow logic, manipulate datasets, and implement imputation
Implement functions in R, advanced debugging, good coding practice
Write and debug code involving control flow logic
Interpret p-values, solve complex problems using R
Demonstrate ability to synthesize knowledge and skills

What are the lectures like?

After you enroll in DS 700: Foundations of Data Science, you can log in to the learning management system to access all course content. The lectures are hosted in the Storybook+ media player and contain rich media content—slides, animations, videos, and instructor narration. You can listen and replay lectures as many times as you wish.

Some lessons have video content featuring interviews with real-life data scientists about their work and the current industry landscape. Other videos show the professor demonstrating vital skills that data scientists use on the job, including how to perform a K-means cluster analysis or work in SQL Server.

What type of assignments do I complete?

There is a mini project and a final project that encourage you to practice and demonstrate the concepts you learned in the course. You might be asked to create SQL queries for various scenarios using the sample database. You will use your R programming skills honed during the course to perform analytical operations and derive inferences.

Here’s an example of the mini project:

What else do I do in the course?

Learning material. The professor for this course picks selections from textbooks, articles, or videos for students to watch. Sometimes, you are asked to reflect on this material in an assignment or discussion prompt.

Discussion posts. A lesson may include a graded discussion prompt, which gives you an opportunity to learn from your peers and contribute your own ideas to the group. For these, you craft an initial post and reply to other students’ posts.

Quizzes. Most lessons include a quiz of 25 to 30 questions covering that week’s course content, readings, and videos.

What technology do I use in this data science course?

You use MS SQL Server, RStudio, and Microsoft Office (Access, Excel, Word, PowerPoint). All of these software applications are on the virtual desktop platform, which you have access to as a student in the UW Data Science program.

Who developed the course?

Dr. Rajeev Bukralia, a former instructor at UW-Green Bay, originally developed the content for DS 700: Foundations of Data Science. The course was revised by instructors Praneet Tiwari and Abra Brisbin. The course is facilitated by instructor Jae Hoon Choi. Faculty work with an instructional design team and industry advisory board to ensure that the course remains cutting-edge and aligned with employer needs.

Do students in the course interact?

Yes. Students interact and share ideas through graded discussions in the learning management system. Also, data science students can collaborate, ask questions, and have general, non-assessed discussions through Piazza, a web-based forum.

How much do I do in one week?

Generally, you need to complete one lesson per week. You have seven days to complete readings and other learning materials, lectures, assignments, and discussion posts. (If you take the course during the summer term, the timeline is accelerated, and you may complete multiple lessons per week.)

This data science master’s curriculum is as intensive as any University of Wisconsin program—on campus or otherwise. Some students put 20 hours of work into one course each week. But that number varies widely depending on how much experience they bring to the program. Although the program requires a serious time commitment, the flexible, online format allows you to study early in the morning, late at night, or whenever works best for your schedule, making it ideal for those who work full-time.

Have questions about DS 700: Foundations of Data Science, the rest of the curriculum, how to apply, home campuses, and more? Our enrollment advisers can help.

Call 608-800-6762 or email learn@uwex.wisconsin.edu.

What Prerequisite Skills Are Required For Admission to the UW Data Science Programs?

November 24, 2021 By UW Data Science Blog Team Leave a Comment

Are you interested in pursuing UW’s Data Science programs but not entirely sure whether your previous experience meets our requirements? Here are the prerequisite skills needed:

Women in Data Science: Meet Jennifer Cox, UW Data Science’s Program Manager

April 12, 2021 By Kaitlynn Martin Leave a Comment

As part of our Women in Data Science series highlighting the importance of women in the data science industry, we’re shining the spotlight on Jennifer Cox, a M.S. graduate in data science and the program manager for the 100 percent online University of Wisconsin Master of Science in Data Science program.

After graduating from UW-Platteville in 2005 with a bachelor’s degree in psychology and a minor in business, Jennifer worked as a program manager at a group home for adjudicated youth. However, she missed using the technical skills she gained in her psychology degree, specifically the statistical components.

program manager Jen Cox with her three daughters — *Jen and her three daughters*

Jennifer then transitioned to working in a research project management role within the School of Medicine and Public Health at UW-Madison, which is where she discovered the data science field. She was exposed to how databases, programming, and statistics merge together to investigate issues in healthcare. Jennifer also learned how big data, artificial intelligence, and machine learning are used to advance knowledge of disease, and offer opportunities for better prevention and treatment.

While in her role at the UW School of Medicine and Public Health, Jennifer decided to pursue a master’s degree in data science. She had been researching programs since graduating with her bachelor’s degree, but nothing seemed like the right fit. Then, she found the online UW Master of Science in Data Science program. She began her degree in 2016 while working full-time and raising her young children.

“It was not easy, but with support and encouragement from my family and the Data Science program staff, I was able to graduate in 2018,” Jennifer said. “I was thrilled to have completed another major goal in my life and in something I felt so passionate about.”

The UW Data Science program helped Jennifer gain confidence in her ability to do her work, and her employer noticed this. After she earned her degree, Jennifer joined a team of clinical informatics professionals to create a master’s degree program.

“I learned a lot about academic program development and found I enjoyed putting together the many different components required to effectively run a program,” Jennifer said.

Jennifer began her role as program manager for UW Master of Science in Data Science and UW Bachelor of Science in Applied Computing in August of 2020.

The following Q&A with Jennifer Cox spotlights her role as program manager and as a woman in the data science field.

*Jen and her family upon graduation with her master’s degree in data science in 2018*

What is your role as a UW Extended Campus program manager?

As a program manager of the MS in Data Science and BS in Applied Computing, I serve as a liaison to the programs’ partner campuses to establish systems, policies, procedures, and business practices that support effective operations. I see myself as a cheerleader for the programs, encouraging and supporting our partners to communicate and make decisions together as a team to best support our students. If we have shared goals, and an understanding of one another, we can best serve our students. I also collaborate with and provide support to internal UW Extended Campus team members to satisfy goals of these programs.

It is important to me that the programs are successful because I’m very passionate about both of these fields. I believe these programs are very strong and UW Extended Campus does a great job of setting students up for success. Graduates of these programs are the future of these fields, so I want to set them up to be the best they can be.

Why did you choose the UW Data Science program as a student? What unique perspectives do you bring to your role as Program Manager?

As a Wisconsin native, I knew I could trust the quality of UW programs. I chose the UW Data Science program because I liked that it was 100 percent online and that I could alter my school schedule based on my personal needs. I also felt the curriculum fully covered the skills needed to be successful in the field, with a good mix of technical and nontechnical coursework. The program also leverages instructors from several UW campuses. These instructors are experts in their domains and they work hard to ensure course content is continuously updated to keep us relevant in the field.

[perfectpullquote align=”full” bordertop=”false” cite=”” link=”” color=”” class=”” size=””]”Graduates of these programs are the future of these fields, so I want to set them up to be the best they can be.” – Jennifer Cox, UW Data Science Program Manager[/perfectpullquote]

I am utilizing the skills I obtained as a UW Data Science student in my program manager role by using data to understand organizational needs and to guide program decisions. I’m also able to effectively communicate data-related information to all audiences so both technical and nontechnical team members can understand. I also have the advantage of experiencing the program firsthand as a student, so I am able to share a student perspective with our campus partners.

Why are women crucial to the data science field?

Women bring unique perspectives and experiences to all areas of work. Leveraging women’s views allows a more complete understanding of an issue and how resolution might be reached. Gathering diverse perspectives, in general, will likely lead to an outcome that is more applicable to the masses.

Women should pursue a data science career if they have an interest, and they shouldn’t feel discouraged by the predominantly male field. There is an increasing amount of women working in technical fields and rightfully so. We need highly skilled women to use their experiences and perspectives to bring understanding to all areas of data science.

Women and men currently in the field can support women entering the data science field by encouraging involvement and providing mentorship. Women should believe in themselves and have confidence that they are a valued contributor to the field.

*Jen and her family before her youngest was born*

What advice do you have for prospective data science students?

Getting a master’s degree is both a financial and time commitment. You should be honest with yourself about what your interests are and what you want to get out of completing the degree. It’s also important to make sure the outcomes of the program align with your goals and that the degree makes financial sense.

Having a support system is also really important. You should discuss your coursework schedule and how long it might take you to earn the degree with your support system. It’s likely you will have to give up some time with your family and friends to be successful, but understand it’s only temporary and it will pay off in the end.

How will data science impact the future?

In this digital age, data surrounds us and, when used and interpreted appropriately, we can improve the world around us. Organizations can use data science to improve business practices, such as making operations more efficient or improving products and services. For example, healthcare organizations can use data to improve knowledge of diseases and how to best prevent and treat them. Information gained from harnessing data can allow us to learn things that might not have been possible before.

UW Data Science strives to be inclusive

In an effort to highlight the importance of women in the data science industry, this is the second post of a blog post series from UW Data Science. The Women in Data Science series features the crucial roles women take on within the computing field. Additionally, the series highlights students, faculty, and program leaders of the UW Master of Science in Data Science program who share their experiences as women in the data science industry, and offer advice for breaking into the male-dominated profession.

The UW Master of Science in Data Science program aims to create an inclusive environment for all. If you’d like to learn about graduates of the program, read about Venmathi Shanmugam, a modeling and simulations engineer working at the Veterans Affairs office, and Halee Mason, a lead data scientist at Cloud9 Esports, Inc. You can also learn about our diverse faculty members.

Have questions about the courses, tuition, or how to apply? Talk with an enrollment adviser by emailing learn@uwex.wisconsin.edu or calling 608-800-6762.

UW Data Science Professor Motivates Students to Look at Data Through Personal Lens

February 12, 2020 By Kaitlynn Martin Leave a Comment

When Abra Brisbin was earning her undergraduate mathematics degree from Carleton College, she wanted to apply math and data to areas that made an impact in the real world. To do that, she earned her Ph.D. in applied mathematics from Cornell University, where she focused on statistical genetics—understanding how genetic variations influence people’s health.

In the next two years, Abra completed a postdoc at Mayo Clinic in Rochester, Minnesota, and then moved into her current role as an associate professor in the mathematics department at the University of Wisconsin-Eau Claire. Additionally, she co-develops and teaches courses in the 100 percent online University of Wisconsin Master of Science in Data Science, including DS 710: Programming for Data Science and DS 740: Data Mining.

In the following Q&A, Abra shares what drew her into the world of data science, what motivates her to be a teacher in the UW MSDS program, and how she helps her students gain the skills they need to be successful in the field.

What drew you into the world of data?

I think one of the great things about statistics and data science is that you can analyze data from a wide variety of different fields.

When you find an interesting data set, you can figure out what there is to learn from it. For example, I’ve worked with students on research projects involving crime rates in Eau Claire or students who have analyzed factors that influence housing prices in the city—which both have a personal importance beyond the numbers.

Data set from DS 740: Data Mining

Why did you become a faculty member of UW MSDS?

It was an opportunity that came up through my connection with Alex Smith—the chair of the mathematics department at UW-Eau Claire. He was getting involved in developing the UW MSDS curriculum and it was really interesting to me. It sounded like a way where I could have some influence over designing these new courses and to really focus on the things that I think are the most important about programming for data science and machine learning.

What are those important aspects of data science, machine learning, and programming?

I think the problem solving aspect is really important because it’s not just simply following a series of steps. It’s thinking where you use both logic and creativity to come to a solution.

The willingness to learn is also huge. As a professor, I can’t possibly teach all of the algorithms that students might need to know when they go out to do data science in their careers. I make it a point to demonstrate how I figure things out and learn more about how various data science methods work, so that students will be prepared to teach themselves in the future when they are out of a structured learning environment.

What is your favorite part of teaching your UW MSDS courses?

The final project in DS 740: Data Mining is really fun because the students can choose whatever topic they want to analyze. They start by choosing a data set from Kaggle—an online source for data science data sets, programming challenges, and data analysis challenges.

Then, they analyze the data using two of the methods that we cover throughout the course and they come up with their assessment of how the data addresses a question that would be of interest to their target audience and which of the data analysis methods they’d recommend.

This project is when students have a chance to branch out into the data set that’s interesting to them, which can show off their personality and learning style. It’s fun to grade, because I get to read about all of these different data sets that I may not be familiar with.

Data set example from DS 740: Data Mining

How would you describe your teaching style when it comes to online learning?

I’ve structured the courses I teach so that each lesson fits into one week of the semester (although the pace is faster in the summer). Most lessons have some reading and one or more short video lectures, where you can hear my voice or the voices of my co-instructors as we talk through slides or demonstrate an analysis on our computers. Additionally, lessons have some low-stakes practice problems where you can get hints and try them as many times as you want. Then, you have weekly homework, which gives you hands-on experience with implementing the methods discussed in that lesson.

The interactive part of the courses comes from our discussion board and online office hours. Our course discussion board is very active with students asking questions, helping to answer each other’s questions, and even discussing related topics, such as sharing interesting news articles they read about data science. I check the discussion board twice per day, so students get feedback quickly.

I typically have online office hours two or three times a week, once or twice during business hours, and once that’s either in the evening on a weekday or at some time on a Saturday. The goal is that as many students as possible will be able to attend at least one of those times. It can be a phone call or you have the option of sharing your webcam so we can see each other. There’s also a chat window where students can enter questions.

When I’m answering questions in office hours, I can share my screen and demonstrate examples of code or pull up slides from the lecture to show how a question connects with the material that was covered in the class that week. Students can chime in with follow-up questions or help answer each other’s questions. We record office hours and post links to them within the discussion board, so students who can’t attend office hours can still view what was discussed.

What advice do you have for current and prospective UW MSDS students?

Be excited about problem solving and learning about all of the aspects of data science!

Also, be aware that master’s-level courses are challenging, even if they’re only three credits. Students who are working full-time jobs might be better served by taking just one course a semester. Some students who are very motivated can do more than that, but taking three courses in a semester while also working full-time is a very, very heavy load. It’s important to find that balance that works best for your life.

Why should prospective students consider UW MSDS?

Our program offers a strong balance of the quantitative rigor that students will get from programming and data mining courses along with the business acumen that they get from courses such as DS 735: Communicating About Data and DS 760: Ethics of Data Science.

I think the fact that this program is online makes it really good for students who are currently working in a job that uses some data science and those who want to advance their careers by getting a degree. This program can help them get to the next career step they are aiming for.

Want to learn more about how expert faculty members, like Abra Brisbin, teach, connect, and prepare UW Master of Science in Data Science students for exciting careers in the field? Check out the program’s curriculum or contact an enrollment adviser at 608-800-6762 or learn@uwex.wisconsin.edu.

6 Questions to Ask Before Pursuing a Master’s Degree in Data Science

October 11, 2019 By Kaitlynn Martin Leave a Comment

Face it, you’ve been on the fence about going back to school for a while now. After Googling master’s degree programs in data science here and there, you still feel overwhelmed about deciding if one is better than another. When there’s countless sources of information tugging you in every direction, it’s best to gather your bearings and figure out the most important details you need in order to guide you in the right direction.

Luckily, we’ve done the legwork. Below are the six most important questions to ask before you begin your application to a data science master’s degree program. Think of this as a mental checklist to help you cross off crucial considerations before taking on your educational future:

1. Do I have an interest in or want to strengthen my problem solving, data management, and research skills?

When you have an interest in data science practices and want to grow your hard and soft skills, enrolling in a data science master’s degree program is a promising endeavor. In fact, 2022 Lightcast research shows that data analysis and programming languages are frequently listed in data science job postings, while management, problem solving, and communications round out the preferred soft skills:

Employers want data science master’s degree graduates to be well-rounded professionals, capable of tapping into the technical side of a role while effectively understanding and communicating data to drive actionable insights. With this in mind, it’s to your advantage to find a master’s degree program that not only prioritizes the hard skills of data science, but also the soft skills. When researching program curricula, look for courses such as DS 735: Communicating About Data, DS 760: Ethics of Data Science, and DS 780: Data Science and Strategic Decision Making—all of which expand on skills that are not only needed, but also demanded in today’s data science roles.

2. What are my career goals?

Venmathi Shanmugam, UW Data Science graduate

If you’re looking to be part of a dynamic team in charge of solving real-world problems, or if you want to efficiently draw conclusions from data to better inform actionable business strategies, a master’s degree in data science can set those career goals in motion.

Whether you strive to be a data analyst, database administrator, data architect, strategic business and technology intelligence consultant, or another data-focused professional, the impact of your work can be substantial. For example, Venmathi Shanmugam, a recent graduate of the University of Wisconsin Master of Science in Data Science online program, is the Modeling and Simulations Engineer at the Veterans Affairs office in Austin, Texas. In her role, she manages large amounts of government data pertaining to veterans healthcare and finance. She also holds responsibilities in the supply chain division of her department—using data to positively impact patient needs.

3. Who teaches the data science curriculum?

When enrolling in a graduate school program, you want to feel confident that you are learning from qualified experts in your desired field. A strong data science master’s program is rooted in an interdisciplinary approach, not siloed off to one academic school or department. Diversity in faculty expertise and perspectives is a critical component to consider before applying to a program. Don’t hesitate to research potential faculty members’ background, education, and recently published research studies.

The UW Data Science online program is a prime example of an interdisciplinary approach in action, where students learn from faculty members across six UW System campuses. With advanced degrees in mathematics, marketing, computer science, philosophy, management, statistics, and rhetoric and computer composition, graduate students receive the direct benefits of working and learning alongside UW faculty who are driven to grow and advance from every corner of the data science profession.

4. Does the degree program provide a networking community fueled by collaboration?

It’s important to consider how a data science master’s degree program sets you up to connect with industry leaders during your time as a student and after you graduate. Look for programs that feature an advisory board of data science professionals across business sectors. This collaboration of individuals shows that the program is supported by outside experts who can help you connect with future employers and/or mentors.

The UW Data Science online program boasts an impressive advisory board, where recognized data science leaders have the opportunity to shape the program. UW Data Science Advisory Board members can also sponsor capstone projects, plugging students into real-world data science settings where they draw upon their skills and grow from hands-on experiences. Currently, the UW Data Science Advisory Board includes data science professionals in the retail, banking, software programming, manufacturing, state government, insurance, transportation, staffing, and medical fields.

5. Do I have the time to earn a graduate degree?

For some working adults, it is too time consuming to earn a master’s degree through an on-campus program. Traveling to and from a campus throughout the week to sit in on hour-long lectures can complicate the balancing act of work and family responsibilities.

If this is your reality, then pursuing an online graduate degree might be your best option. But, not all online degrees are the same. Make sure to thoroughly research an online degree’s requirements and consider how much flexibility you will need when it comes to lectures, readings, and coursework.

UW Data Science is a smart choice for busy adults who want to advance their careers, or make a career change. Offered 100 percent online, you can study and complete coursework whenever and wherever you have an internet connection. Courses have no set meeting times, and you never need to come to campus.

6. Is the program accredited and respected?

When deciding to pursue a master’s in data science, you want to be sure that future employers will value the degree-granting institution you’ll graduate from. There’s no question that you can earn a graduate-level education from almost anywhere in the country and world, so how do you know which programs are worth your time and money? Look for degree programs that are accredited by regional and national accreditors, such as the Higher Learning Commission (HLC).

According to the HLC website, in order to be accredited an educational institution must 1.) have a clear and publicly articulated mission, 2.) conduct actions responsibly, ethically, and with integrity, 3.) provide high-quality education, no matter where and how offerings are delivered, 4.) evaluate its student learning effectiveness and promote continuous improvement, and 5.) have resources, structures, and procedures that support the institution’s mission.

The UW Data Science program is HLC accredited, signifying that its students receive a high-quality education led by faculty who are committed to staying up-to-date with the expanding data science field. Plus, UW is known and respected worldwide, helping graduates of the UW Data Science program stand out to employers. As a trusted and valued institution, a UW degree can help graduates accomplish their career and personal goals.

So, you’ve gone through all six questions, what have you found?

With an expert-led curriculum, a 100 percent online format, and a recognized and respected UW degree, now is the time to see where the UW Master of Science in Data Science online program can take you.

Have questions about courses, tuition, or how to apply? Talk with an enrollment adviser by emailing learn@uwex.wisconsin.edu or calling 608-800-6762.

6 Surprising Degrees That Set You Up for Data Science Success

May 21, 2019 By Kaitlynn Martin Leave a Comment

Right brain v. left brain—which one do you prefer? It’s a common assumption that more left-brain professionals are taking the lead in science and math-dominated fields. Comfortable with flexing their analytic muscles, pursuing a data science career—one of the top five jobs in America according to Glassdoor—seems like well, a no brainer.

But what does this mean for the traditionally right-brain folks? Those who find strength in creativity, imagination, and intuition shouldn’t write off a data science career so quickly. Having a non-technical undergraduate degree can actually complement a master’s degree in data science. Although data scientists do rely on math, programming, and more technical skill sets, a data science career isn’t all about numbers. Often, data is gathered from human behavior. As a result, coming into the data science field with little-to-no prior STEM education can give you a more holistic view of how data can evolve and expand into the future.

The need for data scientists reaches into virtually every industry, providing numerous career opportunities. The following undergrad degrees might seem as right-brain areas of studies. However, they could set you up for data science success:

Sociology

What is it?
Defined as the scientific study of human groups, a sociology degree focuses on understanding how a society functions through the lens of social constructs that affect individuals and entire populations. Sociology has many branches including education, crime, family life, organizations, race, social class, and more. The common goal of sociology is to see the world differently and understand that not everything is what it seems.

How does it relate to data science?
Data is the result of asking questions, and sociology majors are no strangers to examining why something occurs. If you have a goal of advancing contemporary humanity and have an itch to use data as a tool in the process, sociology and data science can be an advantageous coupling of what may otherwise appear as dissimilar interests. By collecting survey data, sociology-driven data scientists seek to study social phenomena, allowing space for both numbers and creative questions to lead their research. The expansion of Big Data is promising for new areas of social research, such as digital behaviors, longitudinal analyses, and network analysis beyond household connections.

Psychology

What is it?
Simply put, psychology is the study of the mind and behavior. A common assumption of a psychology career focuses on professional therapists who work with a rotation of patients with different needs. However, psychology is a diverse field that touches human development, sports, health, social behavior, cognitive processes, and more. Would you be surprised to learn that data plays an integral part in psychology? Similar to sociology, psychology research aims to answer complex questions regarding human behavior.

How does it relate to data science?
Data science skills, such as research methods and statistics, can help professional psychologists understand data findings and turn them around to drive positive change in patients’ lives. Additionally, by developing evidence-based strategies, those with a psychology background can advance best practices for individuals with learning disabilities. The possibilities of a psychology and data science pairing are expansive and have the potential of truly changing lives.

Public Policy

What is it?
Highlighting social science fields such as economics, public management, and sociology, an undergraduate degree in public policy examines issues within governmental administrations and operations. Adapting theories, research, and models to practice, public policy focuses on how cultural norms, circumstance, and other human differentials affect policy making.

How does it relate to data science?
Research data heavily influences public policy models, which can impact hundreds of thousands of constituent lives. In some states, city government has expanded to include data teams that use civic data to serve communities more effectively. Major questions that a public policy and data science approach can begin to answer include: Can we target outreach and intervention to those at risk of poor health outcomes? Where are there unreported incidents of food poisoning? How can we predict major bridge problems before they happen? How can social media data help identify public safety issues?

Philosophy & Ethics

What is it?
With a degree in philosophy and ethics, graduates become well-versed in considering the fundamental questions about who we are. By examining historical and present-day philosophical thought, this area of study strengthens critical thinking, persuasive writing, and effective arguing. Subjects such as political philosophy, metaphysics, logic, philosophy of mind, and more are common in coursework, preparing graduates for careers that value strong communication and problem solving.

How does it relate to data science?
Although philosophy is a humanities degree, its approach and skills can easily transfer into a data science role. One data science manager who first earned an undergrad degree in philosophy, notes that his philosophy studies prepared him for the hypothesis-driven, logical practice of data science. A philosophy-data science path also spotlights the timely issue of data use and security. As more companies come under fire for data breaches and misuse, data scientists are tasked with an ethical burden of how to handle data. Having a philosophy background can better position data science professionals to be more involved in ethically sourcing, securing, and verifying complete and accurate data.

Journalism

What is it?
The world of journalism is no stranger to change. With the decline of traditional print newspapers and magazines and the swift pivot to digital and social media as main sources of breaking news, reporters now need to be at least 10 steps ahead. Dwindling attention spans mean audiences are distracted, requiring journalists to think outside the box to bolster engagement. While the core principles of journalism stay true: serve the public with fact-checked, credible, objective information—the delivery has changed. And, that is where data journalism steps in.

How does it relate to data science?
Journalism, digital media, and data science are all connected. As Big Data evolves in languages and processes, data scientists must understand digital media technologies that provide a platform for their work. Next, it’s critical for journalists to stay abreast of data security, collection, and usage issues that affect the public—knowledgeable reporting comes from plugging in and understanding the ins and outs of the data science world. As a result, journalists with their drive to learn, understand, and inform, can bring all of those skills into a data science role. In effect, having a journalist’s mindset in a data-fueled world can help piece together the bigger picture of how data touches nearly every part of our lives.

Marketing & Business

What is it?
Advertising, promotion, marketing communications, research, targeting, consumer behavior, and other courses prepare a marketing graduate to land a variety of roles. Marketing and business degrees often cross paths, because having a successful business means also maintaining a strong marketing presence. However, the two disciplines cannot be nearly as impactful without understanding data, often in the form of ROI or digital analytics, such as tracking visits, clicks, and conversions on websites and social pages.

How does it relate to data science?
Human behavior, which can be recorded and interpreted through data, is a critical driving factor behind why a marketing or business endeavor flies or flops. In turn, data companies rely on marketers and business professionals to invest in their tools and services, creating a symbiotic relationship flowing with information. Earning a data science graduate degree benefits marketing and business analysts who are looking to take a deeper dive into how data can better inform and predict their brand strategy and campaigns. Having the ability to review and understand data and then the creativity to turn those insights into an optimized branding push is an invaluable skill.

What’s Next?

Do you have a degree in sociology, psychology, public policy, philosophy, journalism, marketing, or business and are intrigued in how data science can expand your professional future? The UW Masters of Science in Data Science online degree can be the bridge between your non-technical education and your next career move.

Earning a master’s degree in data science opens the door to diverse and exciting work with Big Data, and a promising career outlook.

Contact an enrollment adviser to learn about transferable work experience, recent prerequisite coursework, and other admission requirements. Call 608-800-6762 or email learn@uwex.wisconsin.edu.

New Video Showcases UW Data Science Online Learning Experience

April 12, 2016 By UW Data Science Team Leave a Comment

Wake up. Let the dog out. Drop the kids off at school. Work from nine to six. Pick the kids up from practice. Go to the gym. Get dinner on the table. Clean up. Their schoolwork.[Your schoolwork.] Orchestrate bedtime. Sleep. Repeat.

Is earning a degree or certificate actually realistic for a fast-paced life like yours?

Yes, absolutely. No matter what your daily schedule involves.

Online learning through the University of Wisconsin Master of Science in Data Science program is convenient and flexible enough to fit even the busiest schedule. A simple Web interface makes it easy to access courses, connect with instructors and classmates, check your grades, and get support from student services when you need it. Clear course expectations help you stay on track and plan your studies around work and family life.

Watch this video to discover six reasons why you will love learning about data science online through University of Wisconsin.

If you’d like to find out more about online learning, visit the About Online Learning page of our website.

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 discuss your options with an enrollment adviser today.