Dr. Alex Smith is an academic director for the UW Master of Science in Data Science program. Currently a professor of mathematics at the University of Wisconsin-Eau Claire, Dr. Smith joined the faculty in 1990 and has served as the UW-Eau Claire Math Department Chair since 2007. His research interests are in computational science, differential geometry, and symbolic computation.
We spoke with Dr. Smith to ask his thoughts on this new master’s degree program, what makes it unique among other data science programs, his suggestions for prospective students, and his predictions about the impact of data science over the next 20 years.
Why is big data getting so much attention right now?
Cloud storage and computing are becoming ubiquitous. Today, you buy a computer and its operating system comes pre-installed with Dropbox. We are fast realizing the value of cloud computing, and one of those benefits is access to unprecedented amounts of data. This data presents businesses and organizations in every industry with incredible opportunities to solve problems, make superior decisions, and answer questions no one has even thought to ask.
Security is another reason big data is getting so much attention. Every time another business or government agency has its computers hacked, we all hear about it. So the general public is becoming more and more aware of the benefits and potential risks of big data.
What aspects of data science interest you most right now?
“The prospect of working with professionals from different disciplines to solve big modeling problems is exciting to me.”
For me, it is the promise of interdisciplinary research—the potential to answer questions and solve problems in areas far outside my area of expertise. For example, there is a physicist at UW-Eau Claire who was interested in answering the question of how thick is the ice on Jupiter’s moon, Europa. As a mathematician who has no expertise in planetary science, I collaborated with him on the problem. Later at a NASA conference I learned of other interdisciplinary approaches by other teams with people with different types of expertise. The prospect of working with professionals from different disciplines to solve big modeling problems is exciting to me.
How did the University of Wisconsin master’s in data science come about?
“We created this program to address the growing need for quantitative professionals—not just in business, but everywhere.”
We created this program to address the growing need for quantitative professionals—not just in business, but everywhere. Big data is found as financial records, medical records, consumer patterns, internet searches, genetic databases, and in a plethora of other real-world situations. Successful organizations today need professionals with the skills to make sense of the data and put it to good use.
Initially, the idea was to offer a new business analytics program. But we recognized the need for these skills across a broader range of sectors and industries, and we changed the model to data science.
What was the guiding vision behind the development of this program?
“Our online learning management system includes a ‘virtual lab’ that lets students work in different software packages and tools without having to purchase and install them locally.”
Data science is an interdisciplinary field. It’s a combination of computer science, statistics, applied math, machine learning, business management, communication, and more. And our multi-campus format is an ideal match for that.
The UW Master’s in Data Science brings together the talent and expertise of top instructors from six University of Wisconsin campuses. These experts, who specialize in a variety of disciplines, got together to discuss what a leading-edge data science program should look like. Then, we designed a curriculum to teach the skills that tomorrow’s data scientists will need to succeed in the organizations that hire them.
We also wanted to build a program especially suited for busy adults. The UW Master’s in Data Science is for people who have been out in the working world for a few years, who have some experience in one or more key disciplines of data science. Our online learning management system includes a “virtual lab” that lets students work in different software packages and tools without having to purchase and install them locally, and the flexibility of the online format gives these hard-working professionals plenty of time to earn their master’s degrees while balancing work and family commitments.
In what ways does the UW Master of Science in Data Science program prepare graduates for success?
The program prepares successful data scientists in many ways.
It teaches programming skills in languages and packages both current and emerging. These skills are the key to mining and analyzing large data sets. Beyond specific packages, students learn the essential, underlying skills they need to solve problems regardless of programming language.
Students also learn statistical skills—how to spot meaningful patterns and trends. We recognize that some students may not come to our program with a background in statistics. UW Data Science offers a stats course (DS 705: Statistical Methods) early in the curriculum to teach those students how to separate significant and meaningful patterns from all the noise.
The program teaches data mining and warehousing skills using current packages and applications. This enables students to analyze unstructured data such as emails and texts.
Data scientists must also understand privacy and security policies. So part of the curriculum focuses on this. We teach strategic decision-making so students learn how to use their findings to make better decisions, and communication skills so our students can present their findings effectively and persuade top leadership to embrace their action plans.
Finally, the top programming languages of the day continue to change. Our instructors keep adapting, and our students will learn that skill, too. We will show them how to look ahead and spot the “next big thing” that’s coming in data science, whether that is a new programming language or practice.
What about the curriculum makes this program unique?
“The multi-campus nature of this program allowed us to build a truly interdisciplinary curriculum.”
The multi-campus nature of this program allowed us to build a truly interdisciplinary curriculum. In traditional single-campus programs, the data science faculty might be made up mostly of computer science instructors. But because our program draws interested faculty from across the University of Wisconsin System, our instructors bring expertise in computer science, statistics, business management, communication, and more. This broad base of knowledge and experience is a really big benefit of our multi-campus model. No one campus would have been able to offer a program like this on its own.
What one suggestion would you give to prospective students who are interested in majoring or working in data science?
“What the data science field really needs are professionals who possess a strong mix of skills.”
Assess your interests. As I say, data science is an interdisciplinary field. To be successful in data science, you need to be interested in expanding your knowledge and skills beyond your specific area of training. For example, if your forte is computer science, are you willing to gain skills in statistics and communication? If your background is in business, are you willing to learn computer science? So many people today are focused on one specific discipline. What the data science field really needs are professionals who possess a strong mix of skills.
It helps if you’ve spent a few years in the workplace. That will help you gain exposure and experience in different areas. If you are a math major and you go straight into data science, you probably don’t have a broad base of experience. An interdisciplinary foundation can also prepare you to assume management positions in which you may oversee teams of people with different areas of specialization.
What are one or two of your predictions about the impact of data science over the next 10 or 20 years?
That’s a tough one! If you think back 20 years or so, to the early nineties, computers were really just starting to become ubiquitous. The Internet was there, but it was crude. We still had dial-up connections. At that time, nobody could have predicted there would even be a data science field.
In the next ten years, the world will continue to embrace cloud computing. As more and more people gain access to big data sets, they will want to fulfill the potential of that data to make their lives better and their organizations more successful.
Twenty years out, data science may help us to finally create true artificial intelligence. Back in the 80s, people were really interested in the idea of robots with A.I. that could think and act on their own. That hasn’t played out. But machine learning and the R programming language have enabled us to create domain-specific A.I. For example, we have machines that can vacuum floors and control climate inside buildings. But we aren’t yet at the point of having a “thinking” computer we can converse with. Maybe data science will help us get there in the next 20 years.
Dr. Smith would be happy to answer your questions about the program. To get in touch, send him an email at firstname.lastname@example.org.
Looking to start or advance your career in data science? Find out why the online UW Master of Science in Data Science program is a great choice for working adults. Call 1-877-895-3276 or email email@example.com to talk with a friendly enrollment adviser today.