Look, I love being a writer. I went to school for this stuff, three times. But that was before I knew that data science was a thing, how cool it is, and the kinds of job (and salary!) prospects that are out there for people who study it.
Chatham Assistant Professor of Applied Data Analytics Stephanie Rosenthal filled me in. “Companies are using data science analytics today all over the place,” she tells me, and gives a bunch of examples, which I’m not even going to pare down for you, because that’s how excited I am:
Amazon.com and other websites use data analytics to determine what products to recommend to you and even what to charge for them.
- “Walmart is famous for knowing exactly what to ship to every store at every time, because they track everything—what comes in, what goes out, what the weather was like—whether people tend to buy hot dog buns when there’s a hurricane approaching in addition to toilet paper and bottled water. They know all of these things about collective behavior based on our purchases and demographics.”
- Credit card fraud is identified using data science analytics “That’s why you’ll get a call as soon as one purchase is made that is out of character for you,” says Rosenthal. “They’ve developed models to see what your normal behavior is, so they can see what’s out of the ordinary—either because a lot of different people are suddenly buying something, or because you’re buying something that seems out of character. You get a phone call because someone did that math.”
- “Your Google search results look different from mine because they’re based on what we’ve searched for in the past,” Rosenthal says.
- If you see a rectangle drawn around your face in a photograph that you’re viewing on your phone or computer screen, that’s data analytics, too. “Someone has gone through and labeled faces and worked out how to detect them—in general, what they’re looking for is tone gradients, where the forehead, cheeks and chin are lighter than the eyes, nose, and mouth regions—and that’s just built into cameras today.”
- “The traffic information you get from your GPS or your phone is possible because it collects data from other phones in cars—whether they’re moving or not. Some of the cool new research I’ve seen coming out of CMU figures out how to change the timing of traffic lights based on the number of cars that are waiting there, so when there is a lot of traffic coming, it can be pushed through faster.”
- Voice recognition programs like Siri and Alexa are built using data analytics around natural language.
In general, says Rosenthal, data science and data analytics try to get information from data—analyzing patterns to come up with insights. What’s the difference between the two? “Very roughly,” she says, “I would say that data analytics is about running statistics on data, and data science is about collecting it, getting it in the right format, and visualizing it in ways that are productive. We’ll be doing both, which is why the major is called Applied Data Science Analytics.”
Data science and data analytics are some of the highest paying jobs in the job market today. People all want to make better use of their data. It’s not just Microsoft and Facebook and Google who are hiring those people; it’s also UPMC and Highmark, and marketing, travel companies, school systems, consulting firms. Our goal is to prepare students to be successful in any of those places.”
This fall, Rosenthal is teaching a research methods course and an introduction to programming course. “I learned to program a long time ago, from my gym teacher,” she says. “I wasn’t really taught why things work, just how to code. So my goal for the Intro to Programming course is to try to really give students insight into why they’re doing what they’re doing.”
Rosenthal will also be co-teaching the Capstone Seminar for some business courses with Professor and Director of Business Programs Rachel Chung. For example, students in the management information systems major will be helping the Master of Arts in Food Studies students open their new coffee lab.
It’s a business that’s starting up; there’s no reason our students shouldn’t be able to help analyze what their business plan should look like,” she says.
Rosenthal plans to provide students with more hands-on experience by involving them in her own research, too. “I’m interested in how we can collect data more intelligently and also to teach data collection and research methods for effectively,” she says. She is developing a data collection platform to deploy on campus. Students in Rosenthal’s current classes are researching where it should be located, what it should do, and how it could be marketed. Once deployed, students in the Applied Data Science Analytics major will be able to use the data collected by the platform in their classes and also display their work for the campus to see.
Rosenthal is also interested in “producing English explanations of what data analytics say.” In computer security, for example, experts often monitor networks by hand, because of lack of trust that artificial intelligence would make the right decision. “We can help people trust systems better if we do a good job of explaining why they should,” she says.
Chatham’s Applied Data Science Analytics program teaches students to critically identify, communicate, and analyze challenging analytical problems, effectively organize and manage datasets, and develop robust solutions. They are also equipped to evaluate ethical, privacy, and security challenges in their fields of practice.