Annie Lane

Tell us a bit about yourself.

I grew up in New England and currently live in Milwaukee, Wisconsin. I try to get outside every day, whether that’s biking, running, golfing, or cross-country skiing.

At what school and in what concentration did you receive your undergraduate degree?

I graduated from Boston University with a major in computer engineering and a minor in systems engineering. I took some courses in data systems, statistics, and machine learning, which helped to solidify my interest in data science.

Why did you choose the UC Berkeley School of Information?

The short version is that I like the flexibility of being remote while having weekly class with peers and faculty — so I can belong to and learn from the community while continuing to live and work away from a physical campus.

As part of my engineering leadership development program at work, we’re encouraged to pursue a technical master’s degree. Since my work program involved rotating to different locations, I wanted a master’s program that didn’t require “in-person” classes and had flexibility. However, I also value collaborating with peers and the “classroom feel.” The I School program satisfies both criteria and allows me to work globally with fellow students in many different industries.

What is the I School's advantage?

Big takeaway from this one ... At BU, we called ourselves societal engineers, and I loved this because we learned technical skills while also considering how what we want to build would impact people. Similarly, the I School takes a holistic approach where it's not just about being able to apply skills in a bubble, but develop systems within our complicated and always changing world.

The I School focuses on how to really apply data science to problems. The research and theory is fascinating and exciting. However, making an impact requires more than just understanding the technical details. It requires a strong foundational understanding of the domain’s problem space and the decision-making processes required to make trade-offs and successfully integrate an applied solution.

What has been your favorite class at the I School and why?

This is a hard one — I’ve been loving all of my classes. I think W241 Experiments and Causality was a fantastic experience. My team conducted an experiment with over 75 volunteers to measure the effect of participation in “Step Competitions” on the amount of steps people take. While it sounded like a straight-forward question to answer, we faced a lot of semantic and logistical questions in the design and execution of the experiment. A lot of times, we focus on analysis techniques once you have a cleaned-up data set in hand, so it was great to develop an appreciation for the data collection process. Plus, our class had discussions and practiced critical thinking about data quality, which is fundamental to developing models that not just have good “accuracy” or other metrics in a Jupyter Notebook, but depend on robust data to “work” in the real world.

What are your future plans?

I plan to keep applying data science and encourage the spread of it. I love teaching (it was really cool to TA W266 for a semester). At work every day, I get to teach my colleagues with various backgrounds and perspectives new things about data science.

I currently work in a non-data-native company that has a lot of untapped opportunities to learn from data and improve our customer experience with new data science–inspired features. I’m building the foundations and proving the value of these kinds of efforts while sharing the data science language with my colleagues. I plan to continue spreading data science throughout our organization while applying it to create new efficiencies and capabilities within the health care industry.

Do you have any advice for aspiring information professionals/data scientists?

When you get curious about something, go explore it. See if you can apply something you’ve learned or seen elsewhere to the domain you’re curious about. There isn’t a prescribed set of steps to get to an impactful output — you’ll probably “fail” along the way, but that’s just learning what will not work so you can focus energy on what may work.

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