“I think if money were no object, I would just be in school getting degrees and learning about many things,” says Charis Loveland. “By nature, I’m a very curious person.”
One of her myriad interests is the ways enterprises use data to create optimal customer experiences. When she took a position as a program manager in machine learning with Microsoft in 2016, she had little background in data science, but her natural inquisitiveness soon brought her up to speed and sparked a fascination with the field. “I love machine learning because the opportunities for learning and exploring new things are quite literally infinite,” she explained.
Not that her learning curve was swift. It took Charis a year and a half and an immersion in 20 books including Prediction Machines, Weapons of Math Destruction, Algorithms to Live By, and Machine Learning for Dummies before she felt she had a working knowledge of the field.
Simply put, she says, data science is the art of answering compelling business questions using data and statistical techniques to discover insights. “When I explain it to people, they say, ‘Really? That’s all it is?’”
The Human Factor
Data science professionals deal with more than tedious statistics and coding, Charis found, and data science is certainly not an esoteric discipline that only computer geeks can fathom. The more she read and the more projects she worked on, the more she realized how useful and exciting data science tools can be. Her new understanding piqued her curiosity about its application to aspects of everyday life from financing the purchase of a house to choosing a college.“Credit Score uses an algorithm to determine its ratings,” Charis explains. “And U.S. News & World Report uses an algorithm to identify top schools. That’s all data science.”
Despite the importance and ubiquity of data science, barriers to entry to the field are still high.
“In my case at least,” Charis explains, “it took a long time to understand the field because there was a lack of introductory material. I was an English lit major in college, and I didn’t have a statistical background. I had to learn statistics and then linear algebra. Off-the-shelf learning didn’t make sense to me right away.”
But Charis persevered and laid to rest the misconception that data science is open only to programmers. “There are a lot of other roles where data science plays a part,” Charis says, “and there will be more in the future. Companies will need product managers to prioritize workflows. They’ll need programming managers who can help move projects along.
“Professionals in those roles don’t necessarily need in-depth technical, mathematical knowledge,” she continues. “Maybe they don’t need to read 20 books. Maybe they read one or two, and they can decide to jump in from there.”
The growing need for data-science-related professionals is good news for women and minorities, as well. “There is a big push in research institutions and corporations to make sure the past bias toward males in the field is not seeping into DS,” Charis says.
Events and workshops tailored specifically for women are held throughout the U.S., ensuring that data science is a safe place for women to explore opportunities. Early in her career, Charis attended the Grace Hopper Conference for Women and Computing in Atlanta and was pleased to discover among the 3,000 attendees several women who worked outside the realm of coding. “I looked around and thought, maybe there’s a place for me as an English major here. It was the first time I felt really included.”
That’s not to say the field is without its challenges. “There’s a lot of data cleansing and processing involved in creating an algorithm,” said Charis. “It’s quite tedious and prone to error. But if you’re curious, you can get a lot of insight about the data if you take the time to fully explore it.” Because much of the technology is new, data science professionals often diagnose which tools make the most sense for their uses, working on the cutting edge of novel applications.
While at Microsoft, Charis served as senior program manager on a crowd-sourced data science project focused on women’s health in the developing world. Survey data from the Bill and Melinda Gates Foundation was used to predict the health risk of women in nine regions of the developing world including the Asia Pacific and parts of Africa. The data was used to prioritize funding and to ensure women were screened for illnesses that they were most likely to have.
“We received survey data of 9,000 patients,” Charis explains. “The data included information such as whether the woman lived in a house or a hut, whether her floor was dirt or cement, whether she cooked over an open flame or on a stove. Did she have access to a computer? Did she have a cell phone?”
The survey included a health screening to help classify women with similar health concerns in order to create a predictive model to infer the screenings they should receive at local clinics. Testing for those illnesses would be unnecessary and a waste of funds, and eliminating the unneeded tests meant clinics could afford to treat more women.
“Data science played an important part in creating a scalable process that can be repeated in future projects,” Charis says. “We also established a model for collaborating with a nonprofit.” The project resulted in an accurate, trained model of the dataset, which Charis’ team shared with the Gates Foundation, enabling it to implement a targeted approach to screening women for health concerns at its clinics.
Students who know they’re interested in data science should immerse themselves as early as they can in math, statistics, and computer science, says Charis. But professionals entering the field mid-career as she did shouldn’t shy away from becoming involved. She advises enrolling in MOOCs (Massively Open Online Courses) to brush up on their skills and to become familiar with the sorts of questions data science can answer.
“I would like to see more courses for beginners,” she says, “more ways to make data science more accessible.” Her enthusiasm for the discipline extends to instilling that interest in others by sharing her expertise and passion for data science as an instructor at ABDS. ABDS courses and tools, she says, can help even non-math majors and non-programmers develop a data science expertise that will advance their careers and benefit their employers.
“Data science is an exciting and transformative field, and there is no reason to be intimidated by the subject,” Charis said. “You just need a little courage to learn something new.”
Interested in entering the data science field? Visit Charis’ blog for helpful tips and resources.
Charis Loveland is a business development manager on the cloud intelligence team at Amazon Web Services. She specializes in data science, artificial intelligence, and machine learning. Her decade and a half of experience in the field includes data analytics, new product introduction, file and hardware storage, and software development.
She has served as a customer-facing product manager at data management company EMC2,, launched crowdsourcing platforms at Microsoft, and founded Rue La La’s data science team where she created and executed a strategy to create personalized customer experiences. She has taught business courses at the coding organization General Assembly and online data science courses at MIT and Columbia University. She is also a coding instructor, mentor, and advocate for early STEM education and a volunteer at several nonprofits that promote diversity in the technology industry.