University of Wisconsin Data Science Degree

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

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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.

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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.

UW Data Science Student: ‘This Master’s Degree Propelled Me Forward in the Data Science Field’

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The path to a data science career often takes unexpected twists and turns. Data scientists have professional backgrounds as varied as journalist, systems developer, or—in the case of University of Wisconsin Master of Data Science student Halee Mason—lab scientist. We sat down with Halee with a few questions about her experience in the online program. Here, in her own words, the Westfield, N.Y. native shares how she went from lab scientist to data scientist, what drew her to this particular program, and what advice she would give to someone considering a data science degree.

How did you become interested in data science?

After earning a Bachelor of Science in Medical Technology, I worked as a clinical laboratory scientist at the University of Virginia Medical Center in a flow cytometry and immunology lab. As a clinical laboratory scientist, I practiced data science before I knew what it was. The more I worked in the lab, the more I realized how much I loved the data analysis part of my work. I looked for patterns in the data, pieced together information, and drew conclusions about a patient’s potential diagnosis.

After realizing my interest in data, I attended a two-day training course about discovering patterns in data. It provided me the opportunity to meet and network with data scientists. That’s when I decided to shift my career to data science and pursue a master’s degree to learn and practice the skills needed to become a data scientist.

Where do you currently work? 

I’m a data analyst at Elder Research, a recognized industry leader in the science, practice, and technology of advanced analytics. We have completed hundreds of diverse projects for commercial clients and government agencies. Our areas of expertise include data science, text mining, data visualization, scientific software engineering, and technical teaching.

Why did you choose this data science master’s program? 

The program’s diverse curriculum, the flexibility provided by the online format, and the opportunity to learn from a wide array of instructors was very attractive to me. Since I was working full time, the online format was a pivotal part of my decision.

How have you benefited from pursuing a data science master’s degree?

Pursuing this master’s degree complemented my background in data analysis, allowing me to move into the data science field and advance at a much quicker rate. Currently, I work with clients on projects, and on a daily basis, I apply what I learn from my courses to these projects.

What are your favorite aspects of the UW Data Science program so far? 

The best part of my experience at UW thus far has been interactions with the professors. During my first programming class, I was relieved to know that the professors were more than willing to meet on Skype, provide feedback, hold office hours, and even help troubleshoot problem areas within my code. This type of support has continued throughout my courses. I also appreciate the online flexibility, because for my job, I occasionally need to travel to clients for projects.

Which are your favorite UW Data Science classes so far?

DS 730: Big Data: High-Performance Computing has been the most technically challenging by far. I gained invaluable experience with cutting-edge technology that helped me secure a position at a top predictive analytics consultancy firm. Exposure to Hadoop, Hive, Pig, Python, Java, and HBase in one semester was challenging, but overcoming that challenge helped me hone my ability to tackle new problems and gave me a fearlessness for learning new technologies.

What data science skills or tools do you use in your daily work?

I use SQL, Python, and R on a daily basis. A lot of my time is spent wrangling data, and these are my go-to tools. In DS 710: Programming for Data Science, I learned how to use these tools in synchrony.

Do you feel well prepared for your position in data science? 

I do. For example, the skills I learned in Big Data: High-Performance Computing have helped me at Elder Research. I recently finished a project to deploy a log ingestion pipeline that fed the client’s predictive model on Amazon Web Services (AWS). Thanks to Dr. Erik Krohn and skills I learned in his class, I was well-suited for the project.

I’m looking forward to the capstone project to continue strengthening my skills as I finish the program. I’ve really enjoyed the support from advisers and faculty.

What has it been like as an adult in an online degree program?

At times, managing my time between work and my master’s degree has been a balancing act. But I still have time for exercising, hiking with my dog, yoga, and—when I can fit it in—scuba diving.

What advice would you give to someone who is interested in earning this master’s degree? 

The UW Data Science program is very comprehensive and brings together many disciplines to provide students with a well-rounded approach to solving data-driven problems. By the end of the program, you will feel ready to work for almost any business. The data science field is wide-open with opportunities right now.

It is important for data science students to be self-motivated—you need to stick with the online degree from start to finish. Self-motivation also helps you handle the highs and lows throughout a data science project. The perseverance you develop will help you solve even the most complex problems.

Find out more about being a data scientist by visiting the “What Do Data Scientists Do?” page. Or, if you have questions about the University of Wisconsin Master of Science in Data Science, an adviser would be happy to help. Call 608-800-6762 or email learn@uwex.wisconsin.edu.

Editor’s note: Halee has since graduated from the UW Data Science program and is now a lead data scientist at Cloud9 Esports. 

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How to Make Your Data Science Resume Stand Out

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When it comes to landing the data science job of your dreams, you only get one opportunity to make a first impression. You need to make it count.

How can you do that? It’s simple: A succinct and targeted data science resume and cover letter. When written effectively, both documents set you apart from other candidates and convey exactly what you can offer and why.

Polish your data science resume and cover letter using these tips so you stand out from your competitors.

6 Sections to Include in Your Data Science Resume

Writing a resume can be a daunting task, especially if you don’t know where to begin. That’s why we compiled these six components that every data science resume should include:

1. Contact information

You know to include your full name, credentials, address, phone number, and email address. If you have a professional presence on LinkedIn, Twitter, GitHub, or another social media site, consider providing links to those as well. Make it as easy as possible for hiring managers to get in touch with you.

2. Professional summary

Your professional summary should be no more than three or four sentences stating the following:

  • Years of experience
  • Primary areas of data science expertise (e.g., data mining, data warehousing, or data visualization)
  • Type of position you seek (e.g., data analyst, data scientist, or data engineer)
  • Industry in which you hope to apply your data science knowledge (e.g., healthcare, retail, government, or finance)

The professional summary should tell your story—where you’ve been and where you’re headed next. Think of it as your elevator speech—that is, the way in which you’d describe yourself and your abilities in 30 seconds or less. You’ll likely have different versions of this speech depending on the type of data science position for which you’re applying as well as the industry. This is your chance to grab a hiring manager’s attention and encourage him or her to keep reading, so make it worthwhile.

3. Core competencies

This section should include a bullet-point list of your data science strengths (e.g., statistical analysis, data interpretation, and communication) and explain generals tasks you’ve completed to achieve each competency. For example, with data communication, perhaps you could include these tasks: Write, format, and present technical prose; facilitate data-informed discussions; and help non-technical professionals act on data findings.

Refer to data science course outlines to help build this portion of your resume. That’s because most data science course descriptions also include core competencies and learning objectives that you can use as a foundation.

4. Education

List each degree and institution as well as the date of graduation. A Master of Science in Data Science proves to employers that you have the wide range of knowledge and skills needed to do any data science job. Consider listing any courses that are particularly relevant to the job for which you’re applying. For example, if you’re hoping to land a data scientist job in biomedical research, consider listing any data science ethics courses in which you applied various analyses using an ethical framework. Tie your education experience as much as possible to the job you seek.

5. Technical expertise and certifications

This section should include a bullet-point list of specific data science skills you’ve honed as well as tools with which you’ve worked. If you earned a data science master’s degree, list that you have hands-on experience with data science tools such as SQL Server and Tableau and languages such as R and Python. Don’t list tools for which you only have a theoretical knowledge. Hiring managers want to know that you have experience working with these tools and will likely ask you for examples during an in-person interview.

6. Work experience

Don’t worry about keeping your resume to one page—that’s an outdated notion. If you’re a mid-level or above professional, you’ll likely need two pages to list all work experience. And never skip significant job experiences, even if they’re unrelated to the job to which you are applying. A gap of time between jobs is a red flag to hiring managers. For unrelated job experiences, don’t include as much detail on your resume as you would for other jobs.

Tailor each job description to the one you’re applying for. For example, if the position you hope to land requires an in-depth knowledge of SQL, tailor your previous job descriptions to highlight your use of SQL.

Also focus on key accomplishments in each previous position by listing a select group of three to five projects and briefly describing your role in each project’s success. Use action words (e.g., created, managed, coordinated, or led) to describe your participation.

Make Your Data Science Resume Keyword-Friendly for Hiring Managers

A strong data science resume also incorporates the keywords for which hiring managers are searching. This will vary from job to job, and a good rule of thumb is to incorporate as many of the same terms and phrases used in the job posting directly into your resume.

You’ll also want to incorporate as many general data science keywords as possible. For example, Towards Data Science lists these must-have resume skills and keywords for data science that you could reference if it makes sense to do so. Don’t force yourself to include these terms. Use this list as a point of reference, and be cognizant of it as you construct your skills, competencies, and previous job descriptions.

4 Things to Consider When Writing Your Data Science Cover Letter

Follow these four tips when constructing your cover letter:

  1. Personalize your love for data science and why you want to work in this field. Tell a brief story about why you love data and what you hope to accomplish in the data science role that you seek.
  2. Explain why you want to work for that specific company. Look at the company’s “about us” page, and respond directly to the values the company promotes and why these values are important to you as well.
  3. Expand on projects listed in your resume. What skills did you hone, and what did you learn that you can apply going forward in a future data science position?
  4. Remember the unrelated job experiences discussed above? The cover letter is your chance to highlight those experiences and explain how skills you gained in a different field will transfer to this job. This is what will make you truly stand out as a candidate.

3 Bonus Tips to Boost Your Application

Following are three other tips to consider:

  1. Keep the layout simple. Use a straightforward layout and font for your resume and cover letter. Let the content do the talking. And never go below an 11.5 font size.
  2. Proofread for grammar and punctuation. Data science is all about the details, and you don’t want to ruin your chances of landing an interview by misspelling a word or forgetting a crucial punctuation mark.
  3. Invite feedback from a friend or colleague. Are there areas for improvement? Is the resume clear and succinct? Don’t hesitate to look at other data science professionals’ resumes as well. This includes LinkedIn profiles—especially those profiles of individuals who already work for the company to which you’re applying. What language do they use, and what skills do they promote? Use this as a guide for developing your own stellar and unique resume and cover letter.

Considering a Master of Science in Data Science? University of Wisconsin offers this degree online through six campuses. Learn more about the program and which UW campuses offer the degree here.

 

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Editor’s note: Nicholas completed the UW Data Science program in 2018. He is now the director of information technology at Steel King Industries, Inc. 

Though only halfway finished with his University of Wisconsin Master of Science in Data Science, Nicholas Sondelski already knows his future lies in data-driven manufacturing.

Nick is the IT manager at Pointe Precision, a Plover, Wis. manufacturing company, where he’s witnessed the dawn of Industry 4.0—or, the evolution of “smart factories.” Today, these facilities produce a tremendous amount of data that can be used to automate and improve manufacturing technologies.

“Data is exploding right now,” he says, “but manufacturing needs more professionals who can study and interpret this data.” And Nick sees opportunities here—for manufacturing and for his career.

A Start in IT

Nick earned bachelor’s degrees in computer information systems and business administration from UW-Stevens Point, the same university he’s attending online to earn his master’s degree in data science.

For the last 15 years, he’s been at Pointe Precision, moving up from intern to IT manager. Nick loves his work. The company manufactures mission-critical components for surgical instruments, fuel delivery systems on jet engines, and firearms—all of which need to withstand high heat and are vital to the end-use application.

Pointe Precision’s leaders recognized the potential for data to provide insight into processes and, ultimately, better business decisions. Thus, they supported Nick’s decision to pursue a master’s degree in data science.

“Every day, I see opportunities for improvements in a variety of business processes from the shop floor to the front office. Using data analysis techniques gives us additional insight that we may not have seen in the past. That’s what drove me to the UW Data Science master’s program. I hope to become a data analyst so I can help the business make even more strategic decisions.”

A Data Pioneer

Nick is carving a path for data science in manufacturing. And he was one of the first students to sign up for the 12-course, 36-credit UW Data Science program, which is offered online through six University of Wisconsin campuses.

UW Data Science students are a diverse group—they come from backgrounds in business, science, and many other disciplines. (“This makes class discussions very interesting,” Nick adds.) That’s because data science applies to just about every industry.

“I had been leaning toward an MBA program until the data science opportunity came along. Once I started taking classes, everything clicked. I knew this is exactly what I wanted to do.”

Plus, the career outlook alone was a major draw. “Pointe Precision is a growing, successful company, and I love working there. But it’s also comforting to know that analytics professionals are in demand everywhere.”

A Challenging, Useful Education

“Before I started the UW Data Science program, I was unsure of the online format. But the courses are more challenging, fun, and useful than I expected.”

In his current position, Nick has used his education to look at data, cleanse and manipulate it, and extract insights. And he now has hands-on experience with programming languages Python, R, AWS on Amazon’s cloud, along with Hive and Pig on the Hadoop file system used for big data.

The UW Data Science curriculum includes a balance of technical subjects—such as databases and statistics—and courses focused on building management and communications skills. At a recent conference, Nick gave a presentation and used some of the persuasive presentation skills he learned in his data science communication course.

In class, he created a data visualization demonstrating how fast new parts make it through Pointe Precision’s engineering department. “I was able to share a unique perspective with the executive team at Pointe Precision, and they wanted to see more. The VP of engineering was impressed to see data presented in such a different way.”

Industry 4.0 and Beyond

Nick expects to graduate with his Master of Science in Data Science from UW-Stevens Point in 2018. He wants to be more involved in a data science role, leading his own team of Industry 4.0 data professionals. Nick is committed and passionate about Pointe Precision’s mission—but he’s also interested in changing the business and the manufacturing industry as a whole.

“As a data scientist, I have the opportunity to make an impact every day. So far, I’ve only had a taste of what it would be like to apply my data science knowledge. Whether I get to help modify a process, save costs, or make better business decisions, I know it will be a rewarding career.”

Find out more about being a data scientist by visiting the “What Do Data Scientists Do?” page. Or, if you have questions about the University of Wisconsin Data Science master’s degree, an adviser would be happy to help. Call 608-800-6762 or email learn@uwex.wisconsin.edu. 

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Data Scientist Jobs: What a Search on LinkedIn Reveals

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A quick search on LinkedIn reveals more than 312,000 data scientist jobs nationwide. More than 220,000 of these jobs were posted in the last month. More than 19,000 were posted in the last 24 hours alone, suggesting that those who pursue a data science degree can literally (and figuratively) bank on job security.

Data scientists in demand

Data scientists work in virtually every industry, including healthcare, computer science, information technology, retail, marketing, manufacturing, transportation, communication, education, insurance, finance, science, security, government, nonprofit, and law enforcement just to name a few. Companies such as Facebook, Amazon, IBM, Kayak, Capital One, The New York Times, and others continue to clamor for those with data science training who can drive business intelligence using voluminous and complex data.

What exactly are these and other companies looking for? Following are just a few snippets taken verbatim from data science job openings posted recently on LinkedIn:

  • Use big data tools (e.g., Hadoop, Spark, H2O, and AWS) to conduct the analysis of billions of customer transaction records.
  • Establish scalable, efficient, and automated processes for large-scale data analyses that will tie into production systems.
  • Use predictive analytics and machine learning to create new products or drive business decisions.
  • Apply expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how our users interact with both our consumer and business products.
  • Interpret, document, and present/communicate analytical results to multiple business disciplines, providing conclusions and recommendations based on customer-centric data.

Finding the right data scientist training

Employers want to hire data scientists who don’t just know the technology but who know what to do with it; agile communicators who are well-rounded can remain flexible as new challenges and business needs arise. If you’ve thought about pursuing a career in data science, look for a program that provides not only a solid foundation rooted in statistics and computer programming but also one with instruction in economics, business management and communication.

Aligning your education with the top 12 most in-demand data science skills

The interdisciplinary nature of the University of Wisconsin Data Science program caters directly to the types of skills that employers seek. Many employers require a master’s degree in data science or a comparable discipline (e.g., statistics, computer science, or mathematics) as well as the ability to perform these 12 most-common tasks:

  • Create and manage simple databases in Access and SQL Server.
  • Write and execute SQL statements to retrieve and manage data.
  • Analyze data to solve basic analytics problems using Excel and R.

You’ll learn all of this and more, including how to use other well-known data science tools (e.g., PowerBI and Access) in the course, Foundations of Data Science.

  • Use Python and R to analyze real-world data.
  • Use an application programming interface (API) to collect real-world data from social media.
  • Clean and format data for analysis.

You’ll learn all of this and more in the course, Programming for Data Science.

  • Use tools and software such as Hadoop, Pig, Hive, and Python to compare large data-processing tasks using cloud-computing services.

You’ll learn this and more about big data analysis in the course, Big Data: High-Performance Computing.

  • Help non-technical professionals visualize, explore, and act on data science findings.

You’ll master technical, informational, and persuasive communication in the course, Communicating about Data.

  • Create effective visuals to maximize readability, comprehension, and understanding of complex datasets.

You’ll learn this and more in the course, Visualization and Unstructured Data Analysis.

  • Use data and predictive analytics to inform the decision-making process.

You’ll learn this and more, including optimization, decision analysis, game theory, and simulation in the course, Prescriptive Analytics.

  • Transform findings from data resources into actionable business strategies.
  • Explain how data assets can be used to develop competitive advantage.

You’ll learn this and more about how to obtain decision-making value from an organization’s data assets in the course, Data Science and Strategic Decision Making.

In addition, the University of Wisconsin partners with an industry advisory board dedicated to helping students bridge the gap between classroom learning and real-life data science challenges. Their continual input helps ensure course content meets today’s employer needs in this rapidly evolving field. This makes for a smooth transition into the workforce.

Giving employers what they need and want

Upon completion of the University of Wisconsin master’s in data science program, individuals are prepared to:

  1. Identify organizational questions for which data science can provide answers.
  2. Collect and manage data to devise solutions.
  3. Select, apply, and evaluate models to solve data science tasks.
  4. Interpret data science analysis outcomes.
  5. Effectively communicate data science-related information in various formats and for various audiences.
  6. Value and safeguard the ethical use of data.
  7. Transform findings from data resources into actionable business strategies.

Increase your value to employers seeking savvy, well-rounded data scientists by earning a University of Wisconsin master’s degree in data science. To learn more, download a program guide at https://datasciencedegree.wisconsin.edu/.

New Video Showcases UW Data Science Online Learning Experience

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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.

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