University of Wisconsin Data Science Degree

Data Science Webinar Highlights the Current Job Market and Future of the Field

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The University of Wisconsin Data Science program hosted a career webinar breaking down the future of the field. The discussion, which was facilitated by UW Data Science Academic Program Manager, Jennifer Cox, and Senior Success Coach, Michael Paul, examined the current job market and career outlook for the data science industry. 

The panel featured experts representing different areas of data science, all of whom were either UW Data Science graduates or Advisory Board members for the program:

The four panelists shared career knowledge on a wide range of topics, including what skills you need to succeed in the field and the importance of companies cultivating an inclusive workplace. 

What Employers Expect From Today’s Data Science Professionals

To work in the data science industry, possessing business knowledge is just as important as having a strong understanding of programming languages and visualization tools. With soft skills like strong communication and collaboration, data scientists can clearly and effectively explain projects to other departments in their organization.

“As a data scientist, being able to straddle that line between business and IT is expected,”  Ferrier said. “That’s why communication is so key, because you have to live in two worlds.”

Companies are searching for workers who can bring best practices to the current business through real-time analytics and personalization. In fields such as consulting and manufacturing, Ferrier said there has been a sudden rise in companies seeing the value in advanced analytics.

“There are tons of jobs out there for the jack of all trades, the generalist who can go end-to-end on a full project,” he said. 

The industry is also beginning to see a specialization of roles in larger companies. Wells predicts a job market where teams consist of “unicorn” employees, or workers who are hard to find but have unique and specialized skills that can greatly improve an organization.

“Instead of expecting the world out of one person, companies are starting to recognize that it does behoove them to specialize across their roles and build unicorn teams instead of that magical single unicorn,” she said. 

Creating An Inclusive Workplace for a Better Tomorrow

The panel also discussed the importance of diversity and equity in the workplace. Many companies have made an effort toward inclusivity to ensure everyone has a voice. 

Wittmann said American Family Insurance in particular has an Inclusive Excellence department, along with employee resource groups to support women in technology, different ethnicities, and veterans.

“The more diverse your teams are, the stronger the teams will be because you have different perspectives and ways of looking at things,” Wittmann said. 

Companies like Trane Technologies have begun to implement anonymous hiring by removing candidates’ names from their applications. This allows hiring managers to make decisions based on an applicant’s skills while avoiding gender bias and other forms of hiring discrimination. 

Remote positions have also created more job opportunities for graduates and working professionals across the country. In the case of Oshkosh Corporation, Ferrier said the company hired remote workers in Arizona who otherwise wouldn’t have been able to join the Wisconsin-based organization. The company has promoted tuition assistance as well for workers in other departments who want to acquire the core knowledge necessary for a data scientist role.

Group Of Businesspeople Examining Graph On Computer

Getting Your Foot in the Door

The job market for data science has become an employee’s market in recent years, which makes the career outlook for the field a promising one. 

For people looking to transition into the field or are searching for their first job after graduation, a solid résumé and proper networking can go a long way to set you apart from other applicants. The panel suggested using online job boards such as LinkedIn, which is how Challoner found her current job at Fox Valley Technical College.

“Because of my background, résumé, and data science degree, my inbox blew up,” she said.

The panel recommended broadening your search when looking for data science openings, since job titles can vary from company to company. 

“Organizations use many different terms,” Wittmann said. “You may not necessarily see the words ‘data science.’ They may be looking for some type of data engineer or whatnot.”

When searching for jobs, shadowing other employees within your organization or reaching out to a company’s hiring manager for an informational interview can help you find the right fit.

“How [hiring managers] describe what they want from their employee versus what they put in a job description are often very different things,” Wells said.

Creating an online portfolio is another option to demonstrate your data science knowledge and skill set. With the UW Data Science program, students can tailor their final capstone project to showcase a collection of work with potential employers. 

Furthering Your Career With a Data Science Degree

The emergence of online degree programs has given a wider range of students more access to an education. This includes Ferrier’s wife, who went back to school online while she was raising their children as a stay-at-home mother. 

“I find that a lot with our data science students,” Cox said. “They’re working a job or several jobs, have kids, and are fitting this in with their regular life.”

With the field of data science constantly evolving, the panel stressed the value of being a lifelong learner. 

“Getting that data literacy can help you in any career, not just one that is a traditional data science career path,” Challoner said.

While online courses on LinkedIn Learning and Coursera are useful for supplemental learning, a master’s degree program will allow you to establish credibility and fully develop the technical skills needed in data science. The 100 percent online UW Master of Science in Data Science is a suitable option for those looking to advance their careers while having the flexibility to support any lifestyle. 

Want to learn more about the UW Data Science program? Talk with an enrollment adviser by emailing learn@uwex.wisconsin.edu or calling 608-800-6762.

The full webinar is available below:

About the Panel

Missy Wittmann | Data Solution Engineer at American Family Insurance

Data science webinar panelist Missy WittmannOver the past 36 years, Missy has worked several positions within the data space at American Family. Her passion for data led her to the Wisconsin Data Management Association (DAMA), where she was elected president and held board positions at the international level. Missy joined the UW Data Science Advisory Board to discuss the needs of the industry and ensure the program is aligned with the workforce. 

 

 

Brent Ferrier | Principal Data Scientist at Oshkosh Corporation

Data science webinar panelist Brent FerrierA graduate of the UW Data Science program as well as an Advisory Board member, Brent is responsible for the strategy and implementation of data science initiatives at the Oshkosh Defense business unit at Oshkosh Corporation. A lifelong learner, Brent also has an MS in Cybersecurity from Georgia Tech and an MBA from UW-Parkside. His educational background in supply chain management and data regulations has further complemented his data science career.

 

Rebecca Wells | Data and Analytics Catalyst at Trane Technologies

Data science webinar panelist Rebecca WellsRebecca develops the data and analytics vision at Trane Technologies, where she has worked for 20 years. This includes how the organization is structured as well as the process of turning analytics into data science. A graduate of the UW Data Science program, Rebecca was drawn to the field after developing an interest in visualization storytelling. She has since been able to combine her statistics and programming skills with her pre-existing business background.

 

Carolyn Challoner | Information Technology Instructor at Fox Valley Technical College

Data science webinar panelist Carolyn ChallonerA 2018 graduate of the Data Science program, Carolyn is working to train the next generation of data scientists in Fox Valley Tech’s newly formed Data Specialist degree program. She has previously worked at IT and global marketing jobs throughout the Fox Valley region of Wisconsin, including Plexus, Miller Electric, and Kimberly-Clark. 

 

 

Michael Paul | Senior Success Coach at UW Extended Campus

Michael PaulMichael has supported UW Data Science students since 2013, with a career in higher education spanning 15 years. As a success coach, Michael helps students improve their connectedness to the program, their home campus, and online resources.

 

 

 

 

Jennifer Cox | Academic Program Manager at UW Extended Campus

Jennifer CoxJennifer has worked in research, project management, and information science at the University of Wisconsin System since 2007. She became the Academic Program Manager for the UW Data Science program after completing the MS in Data Science degree program in 2018. Jennifer’s greatest data science interest relates to the use of data in business strategy and decision making.

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

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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

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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.

Women in Data Science: Why They’re Critical to the Data Science Workforce

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According to a 2020 National Center for Women and Information Technology (NCWIT) report, women make up 57 percent of the overall workforce, but only 26 percent of computing and math occupations. This is the first post in a new series from the University of Wisconsin Master of Science in Data Science to highlight the importance and need for women in the data science industry. The Women in Data Science series will feature the crucial roles women take on within the computing field. Additionally, the series will highlight 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. 

Though there is a lot of work yet to be done for women’s inclusivity in data science, there are a few statistics that are worth celebrating. In this first blog post, we take a look at hiring trends, strengths women bring to data science roles, and how women can stay competitive in the industry.

Positive job growth for women in data science

First, let’s break down the percentage of women in computing occupations. A 2022 Burtch Works study found that the number of women in data scientist roles has reached 24 percent, an increase from 2018 where just 15 percent of data scientists were women. The same study also found that the largest percentage of women in data science roles are in the entry-level individual contributor category.

These numbers indicate a possible increased presence of women in the field, along with the beginning of women-led leadership. However, there is still a great need for growth in the computing field for women and other minority groups, especially in the mid-to-senior level leadership roles historically held by men.

Diversity reduces bias and improves work quality in organizations

The 2020 NCWIT report also found that women in the computing workforce are more racially and ethnically diverse than men in the field. As shown in the graphic below, there is a higher percentage of women with African American, Black, and Asian descent who hold computing roles than men. 

Graphic from the 2020 National Center for Women and Information Technology (NCWIT) report showing the racial composition between women and men with computer and information sciences (CIS) bachelor degrees in 2019. At all degree levels, the women earning CIS degrees are more racially and ethnically diverse when compared to men.

Workplace diversity plays a large role in reducing bias, which is especially critical in the computing field. According to a Boston Consulting Group (BCG) study, “Interpreting causal relationships and correlations in large data sets requires subtlety, and both humans and machine learning algorithms can occasionally ‘see’ patterns that lead to spurious, biased, or even downright dangerous conclusions.” A diverse team—whether by gender, race, ethnicity, sexuality, age, disability, and more—allows for various opinions and experiences to be considered when tackling projects.

Workplace diversity also improves the quality of work. A study done by Columbia University researchers Bo Cowgill and Fabrizio Dell’Acqua found that prediction errors were correlated within demographic groups, especially by gender and ethnicity. More diverse teams will reduce the chance for compounding biases, resulting in fewer errors.

While diversity reduces bias, it also leads to higher innovation revenue. Because people with different backgrounds and experiences often work through problems in different ways to come up with a variety of solutions, the odds that one of the solutions will be a financial success increases.

A chance to make a difference in the world

There are a number of ways that data science impacts the world, and according to the BCG study, this is important to women. The study found that 73 percent of women entering the data science and machine learning field prioritize tangible impact in their career choice, compared to 50 percent of men.

Graphic from the BCG study comparing the percentage of women and men STEM students who have a preference for working on applied problems with tangible impact. On average, the results show that women, including data science majors, place a higher emphasis on applied, impact-driven work than men do.

However, many women do not see data science roles as fulfilling their career goals. According to the same study, half of women instead see the data science field as “theoretical and abstract, focused on manipulating code and data with low impact and, by implication, low purpose.” Although there are probably many factors that contribute to this negative perception, the study cites the largest is the companies themselves. It is argued that companies are not communicating the important and meaningful role that data scientists play within their business, deterring applicants.

Data science is utilized in all industries, ranging from renewable energy, to healthcare, to public safety. Within these industries, the work of data scientists often has a tangible impact on the technology that will shape our future. In order to recruit women to data science roles, companies should showcase how data science is at the heart of effective decision making by highlighting the specific problems that data scientists solve in their organization. Bottom line, all industries stand to benefit from the experiences, perspectives, and skills that women in the field have to offer, and they must work toward better communicating with women in data science.

How to stand out in a competitive industry

If you have an interest in data science and want to stay competitive, enrolling in a data science master’s degree program may be the right next move for your career. You can start by asking yourself a few important questions before taking the leap, including which program is the right fit. Diversity in faculty expertise and perspectives is a critical component to consider before applying to a program. It’s important that you can relate to and feel supported by program faculty and staff.

UW Data Science strives to be inclusive

The gender gap within data science roles and leadership positions continues to be a pressing issue. Every organization can benefit from prioritizing diversity, equity, and inclusion initiatives within their internal culture and hiring processes. This requires conversations at all employment levels, from institutions granting degrees to CEOs discussing promotions to leadership positions. 

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, who is 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.

Data Science vs. Data Analytics: The Differences Explained

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If you’ve ever scanned job websites for roles in data, you’ve probably seen listings for data scientists and data analysts accompanied by job descriptions that sound quite similar. While the two fields are interconnected, data science and data analytics vary in scope, responsibilities, and goals.

One overarching similarity is that professionals in both roles use big data to solve problems and create improvements in an organization. The biggest difference, however, is how they interact with data. 

Data scientists often work with vast stores of raw data, working as investigators to create ways to analyze and model that data using statistical analysis and heavy coding. The goal of their work is to uncover the questions the data can answer. Data science often lays the foundation for further investigation. 

Data analysts leverage the modeling of the data scientist to create actionable and practical insights using a variety of tools. The work of data analytics involves using organized data to apply findings immediately.

Skills and Tools You’ll Need in Data Science and Data Analytics

There are some distinct differences between skills needed for data science and data analytics careers. However, there is also some overlap.

data science vs data analytics skills comparison in venn diagram
A Venn diagram highlighting the similarities and differences between the skills needed for data science and data analytics careers.

A Deep Dive into Jobs and Responsibilities

When looking at job opportunities, it’s important to not only look at the job title, but also the responsibilities, as the titles can overlap between data science and data analytics.

Some common job titles in the data science field are:

  • Data scientist
  • Data analyst
  • Data architect
  • Data mining engineer
  • Machine learning engineer
  • Hadoop engineer
  • Predictive modeler

Data science responsibilities often include: identifying opportunities for investigation, collecting data, predicting trends, cleaning and validating data, and communicating. To learn more about data science careers, read here.

Some common job titles in the data analytics field are:

  • Data analyst
  • Business analyst
  • Database analyst
  • Market research analyst
  • Operations analyst
  • Business intelligence analyst
  • Tableau developer

Here are some common data analytics responsibilities: exploratory data analysis, data cleansing, statistical analysis, and developing visualizations.

Salary Expectations

According to the U.S. Bureau of Labor Statistics, the median annual wage of a data scientist is $100,910. The 2022 Burtch Works Salary Report found that data scientists make on average between $90,000 and $275,000 annually, depending on experience level and managerial responsibilities. According to the same report, analytics professionals (referred to as artificial intelligence professionals in the report) can make between $105,000 and $275,000 on average, also depending on experience level and managerial responsibilities. For a more in-depth look at salary information, visit here.

Educational Advantage

One way to increase your salary is to earn an advanced degree, which is common in both fields. The Burtch Works study found that 93 percent of the data scientists and analytics professionals surveyed held an advanced degree.

A graph from the Burtch Works study showing the educational levels—including bachelor's, master's, and Ph.D.— of predictive analytics professionals and data scientists.
A graph from the Burtch Works study showing the educational levels—including bachelor’s, master’s, and PhD—of data scientists and analytics professionals (referred to as AI professionals in the report).

The 100 percent online UW Masters in Data Science prepares students for both data science and data analytics roles. Students not only learn technical skills they need to succeed, but they also gain knowledge in effective project management, leadership, and communication. 

Want to focus on data analytics? UW Extended Campus offers a virtual Data Analytics Bootcamp that can be completed in just 24 weeks. You will graduate from the program ready to apply your knowledge in the professional world.

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The University of Wisconsin offers an online, 36-credit Master of Science in Data Science degree program. This data science master’s program will teach you how to harness the power of big data using the latest tools and analytical methods. Start your journey today.

Explore

Curious about what you’d learn in UW Data Science courses? See the curriculum.

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Have questions about the UW Data Science program? Contact an adviser at 608-800-6762 or learn@uwex.wisconsin.edu.

From Dentistry to Data Science: A UW Grad’s Career Journey

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In May, Venmathi Shanmugam and her five-year-old daughter shared a special milestone.
They both received diplomas—one graduated from kindergarten while the other from the University of Wisconsin Master of Science in Data Science online degree program.

A couple days before crossing the stage at UW-Eau Claire, her home campus for the program, Venmathi reflected on the major accomplishment:

“When it comes to putting your heart and soul into something, you need to go for it,” she said. “People can say going back to school is impossible. I have a family and a busy, full-time job. If I can do it, anybody can do it.”

Venmathi is currently the Modeling and Simulations Engineer working at the Veterans Affairs office based in Austin, Texas. This dual role includes data engineering and data scientist responsibilities where she works with large amounts of government data pertaining to veterans healthcare, finance, as well as the supply chain division.

Being in the data analytic and data science profession for about seven years now, Venmathi has taken on previous positions in business and IT consulting, statistical data analytics, and clinical programming. But before diving into the data science world, she was on a different path.

“Ten years ago, I was practicing dentistry in India,” Venmathi said. “But the idea of expanding my scope was always at the back of my mind. As a healthcare professional, I needed to work with and understand data. When I reviewed my patients’ medical history before any treatment, I saw patterns in how oral health affected their overall personal health and started getting curious. It was during that time that I realized data science was something that would help me connect all the dots and was an area I didn’t want to miss out on exploring.”

 A Supported Online Degree Experience

After she moved to the U.S., Venmathi completed online certifications in statistics, healthcare informatics, programming, bioinformatics, and more to help bolster her knowledge as she gained experience in various data science roles.

Growing from one job to the next, Venmathi was always motivated to take on new challenges. Already comfortable with online education through her previous trainings and certifications, Venmathi decided that her next step was to get a formal data science degree.

“I took about six months doing course research on every single university that offered data science,” Venmathi said. “The UW Data Science curriculum had a little bit of everything, starting from the very basics and establishing a foundation to progressively dive into more deeper challenges like machine learning, robotics, advanced programming (Hadoop, Spark, Amazon Web Services), and advanced statistics. When I learned about how the degree was flexible and online, I felt like it was meant to be!”

Venmathi has not forgotten how she felt at the beginning of the program. A feeling that she knows many prospective students might be dealing with.

“I was really worried about what I should expect and if I would even be prepared to go back to school,” she said. “I was doubting myself and wondered how structured the courses would be. I wasn’t sure if I had the principle and drive to see a full degree through.”

Venmathi’s fears were quickly proven unfounded once she jumped in. She soon got into a routine, with readings, coursework, and other tasks due on the weekends. She loved having deadlines to work against. They were tough challenges with her busy life, but they also brought structure and fun into her learning experience. By having the flexibility to take lessons when her schedule allowed, Venmathi felt like she was accomplishing tasks and learning something new every week.

“In other online courses I didn’t connect with faculty unless it was for quarterly reviews,” Venmathi said. “When I started the UW data science program, I had a lot of questions, and I found that all my professors, my student success coach, Michael Paul, and my student advisor, Dr. Alex Smith, were always there for me. I should really thank all of them for making my time in the program so positive.”

Taking on a Challenging Capstone

Venmathi wanted her capstone to be the culmination of everything she learned. So, she decided to push past all of her comfort zones and pursue a challenging topic involving a combination of AI automatic speech recognition, voice analytics, facial recognition, Natural Language Processing (NLP), advanced Python, and deep learning techniques.

The result was a comparative case study and demo between an Automatic Speech Recognition System (ASR) technology—currently implemented in smart devices—and an Advanced Multimodal Automatic Emotion Recognition System that could potentially combine voice, face, and emotion.

Inspired by her successful projects in
DS 740: Data Mining that focused on the use of NLP, emotion recognition, and sentiment analysis from live social media posts, Venmathi began to research and experiment more on the futuristic topic.

“When a user talks to an AI product like Siri and says ‘I’ve had a long day,’ the AI either usually apologizes for not understanding or gives you search results on what that phrase means,” Venmathi said. “You get a little annoyed because the supposedly ‘smart’ device is not smart enough to capture your feelings or tone of voice, unless you spell it out. You keep repeating things, and there is always going to be problems with understanding different accents.”

With the right combination of voice, text, and facial recognition, Venmathi believes the AI could pick up on the stress in the user’s voice, see the frown on his or her face, and launch into a more helpful response.

Venmathi faced a lot of road bumps with the computing and deep learning aspects of her capstone. As she wrapped up her project, she knew there was a lot more research and testing to be done—especially with model tuning and improving fusion accuracy. However, she was happy with how she was able to successfully prove her target goal: that a multimodal emotion recognition system would add measurable improvement to the accuracy rate of the current ASR system.

“Professor Ethan Christensen, who reviewed the capstone project, really appreciated me,” she said. “He was a huge encouragement and recommended that I publish my capstone as a research paper. I am planning on making that happen.”

From One Accomplishment to the Next

Even before graduation, Venmathi could put her data science coursework into action.

“I have a brand new portfolio, including knowledge and skillset on all cutting-edge technologies and tools like NLP, deep learning, AI, Hadoop, Python, R, SQL, Spark, Pig, Scala, and Java, and I find myself becoming useful to my team more and more from everything I learned from the UW courses,” Venmathi said. “It’s a really good feeling when you actually apply your learned skills at work and know that your project can get you a lot more than good grades.”

Now that she has obtained a master’s degree in data science—graduating with a 4.0 GPA—Venmathi feels that she can understand, communicate, and execute on all levels in her data science and data engineering roles— effectively coordinating business, tech, and engineering needs.

Next up, Venmathi has her eyes set on an executive doctoral program in health informatics and leadership and is excited to move from one successful educational experience to another. But for people who are still wondering, like she did, if the
UW online master’s in data science is worth the leap, Venmathi hopes readers take the following words to heart:

“Encouragement or discouragement comes from within ourselves,” she said. “Life and big decisions, like going back to school, are always going to be a challenge—whether it’s today or tomorrow. You will be able to do this, whatever the case may be.”

Start exploring how the UW Master of Science in Data Science online degree program can push you to new career heights. 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 Master of Science in Data Science is a collaboration of several UW System campuses. Venmathi earned her degree from UW-Eau Claire.

6 Surprising Degrees That Set You Up for Data Science Success

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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.

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