Extracting conclusions

As computers aggregate massive amounts
of information, Data Science master's student
Christie Lee Gan is sharing her love of finding
concise conclusions in large data sets.

Christie Lee Gan knows what it’s like to get overwhelmed when surrounded by a lot of new information. A spreadsheet packed with data can seem indecipherable at a first glance. Processing massive amounts of emails can be daunting, or in social settings, a room of crowded people can be a lot to take in (though after a year in quarantine, Gan jokes that she misses those crowded rooms).

That’s why Gan loves being able to take large amounts of data and find concise conclusions from it to share with others. These conclusions can range from analytical insights to predictions.

“With computers, there’s so much data being collected and aggregated every single second,” Gan said. “I think there is really valuable information and conclusions that we can bring out of those, and bring more understandable information to folks who might look at the massive amount of information and be overwhelmed.”

Gan is a graduate student in the Master of Science in Data Science program at UW. She attends school part time as she works full time at Boeing as a systems and data analyst. It’s been a challenge to balance school and work, but it’s improved her time management skills as well as given her new tools to use in her job.

Gan working for Boeing from her home office during the pandemic.

Gan decided to attend graduate school after wanting to delve deeper into her studies as an undergraduate Informatics major at UW. She had encountered machine learning through her undergraduate programming classes but she wanted to learn more. What were the math and the algorithms that allowed the code to work?

Now, she’s learning the theories behind it. Her graduate studies help her understand the foundations of machine learning. A computer can be trained on large sets of data to generate a model, which can be used to perform predictions on unseen data. For example, if Gan wanted to predict what her GPA might be in the future from many different factors like sleep hours, study hours, and hours of TV being watched, a machine learning algorithm could help her do that.

In industry, machine learning is commonly applied to image recognition or product recommendation systems. Some of this learning has already made parts of her job at Boeing easier, as she’s been able to automate some of her own work using this deeper knowledge of programming languages and databases.

Attending graduate school has allowed Gan to bring new knowledge not just to her own job, but to other employees at Boeing. Gan said Boeing is supportive of its employees attending higher education and Gan’s own graduate education is funded through a tuition initiative at Boeing. In the Seattle technology landscape, Boeing is one of the oldest companies, at over 100 years old, so higher education can bring fresh perspectives to the company, Gan said.

Gan with friends from the Data Science master’s program (photo taken pre-COVID-19).

During monthly “Tech Talks” at Boeing, Gan has been able to present some of her learnings to fellow employees, such as how to use Python scripts to clean up Excel spreadsheets packed with unstandardized data.

Part of the joy of data science for Gan is in sharing it. Her first few data science classes as an undergraduate at UW were so interesting that she signed up to be a teaching assistant, so she could support fellow undergraduates for the next three years in their data science courses.

“I wanted to bring other people into this field,” Gan said. “Because I do think that it’s a very valuable skill, but also it’s fascinating to be able to have the skills to extract conclusions that other people are not able to find.”

Even years after being a TA, Gan is in contact with students who tell her how those classes are still instrumental in their careers and helped them secure their first jobs.

Gan’s undergraduate graduation from the UW iSchool (photo taken pre-COVID-19).

Part of what makes Gan a great teacher comes from what she learned during her first experience teaching math during summer school in Seattle. Gan noticed stark inequalities between her students’ learning environment and the one she grew up in, an experience that inspired her to do everything she could to help others succeed. It also gave her a better understanding about how each student she teaches comes to education with a different background and perspective.

Gan enjoys working in industry right now, but said she could also see herself returning to teaching through lecturing and bringing her knowledge of industry back into the classroom.

After all, there’s an infinite amount of data out there and even more conclusions to extract from it.


By Kate Stringer, UW Graduate School

Published April 27, 2021