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WiDSTORY | November 15, 2021

First Time Kaggle Participant Team Earns WiDS Datathon Excellence in Research Honorary Mention

The WiDS Datathon Excellence in Research Award is an opportunity for WiDS Datathon teams to write papers about their research. The papers are judged on their potential for real-world impact, rigor in scientific methodology, and clarity of communication. Team Parameters Patrol from the United States and the United Kingdom with teammates Natalie Pirkola, Elena Barbulescu, Stacy Forsyth and Kate Tereshchenko won an Honorary Mention for their research paper, Feature Engineering to Improve Performance.

They say the WiDS Datathon was the perfect opportunity for collaboration. “We decided the WiDS Datathon 2021 project was interesting and right for us given our health care background and desire to learn more in data science/machine learning… All four team members were very excited to participate on this project and we started right away. We got together and proceeded to meet multiple times per week to explore the data and work in Kaggle. It was a first time Kaggle competition for all of us… We all feel we improved data science skills as a result, and it was a rewarding experience.”

Team members:
*Stacy Forsyth, Health Care Benefits at Ford Motor Company (formerly Industry Consultant for IBM)
*Natalie Pirkola, VP Ambulatory Clinical Pharmacy Services, Henry Ford Health System and Health Alliance Plan
*Elena Barbulescu, Data Analyst, naviHealth (a subsidiary of Optum)
*Kate Tereshchenko, Customer Success Manager-Architect, IBM London

We asked some of the team members to tell us more about themselves and the experience of working together during the datathon competition.

Tell us about your background.

Stacy Forsyth: I grew up in a Detroit suburb. I decided to study math because it always seemed so hard for everyone, and I like to challenge myself. I also love puzzles and solving problems. I majored in math in college with a minor in computer science. After college I started in a data related field, programming relational databases for an insurance company. I dabbled in actuarial science then moved to health care data management at what is now IBM. For the last 15+ years I have been working with health care data in various roles. I love being able to apply my math/data skills to the complex and challenging world of health care. I specifically like working in health care because I want everyone to be as healthy as possible and I know our health care system in the US needs improvement.

Natalie Pirkola: I am from a Detroit suburb. While I was always good at math and science, I was often told by classmates growing up that girls couldn’t do coding. I fell in love with chemistry first which took me into pharmacy for the start of my career. As my interests turned to population health and value-based health care, I became more and more interested in learning coding to improve my ability to understand populations and allow myself to get right into the data.

Elena Barbulescu: I grew up in Romania and my mother worked in a laboratory, and I would often visit with her there. Using a microscope and learning about the laboratory really made an impression on me at a young age. In college, I studied biology and anthropology. Like my mother, I did molecular biology research in different laboratories, biomedical engineering, neurology, and pharmacology. After college, I decided to enter the healthcare field. And because of my father’s influence as an engineer who loves mathematics, I realized I took much more after him and continued my path into machine learning and AI.

How did you get interested in data science?

Stacy: In working as an Industry Consultant (Sr. Analytic Consultant) for IBM I frequently used data, however we didn’t call it ‘data science’. I started seeing classes about data science pop up at IBM and I wondered how it was different from my main job of telling health care stories with data. I learned a main difference was the use of machine learning/Python. I became curious and wanted to learn more to see if I could apply it to my job. I started taking courses (many of the same courses that Elena took) and started talking to Elena/Natalie about it as we were all learning. When the WiDS Datathon competition came up at IBM, it seemed like a perfect fit to try out what I was learning because it was focused on health care (diabetes in the ICU) and machine learning.

Natalie: I began to work on data visualization in graduate school and I found that I really loved working with data. I continued to find myself gravitating towards working with and leading data teams. The biggest barrier was my fear of coding. As part of developing a growth mindset, I challenged myself to face the fear of nay-sayers head on and go back to school for a Master’s in Information Systems.

Elena: As my healthcare career evolved, I wanted to leverage my six and a half years of laboratory research experience and find a career path where I could work with data which is how I got interested in data science. While at IBM, I took my first online data science courses through the IBM/Coursera Professional Certificates and the IBM AI Academy Machine Learning Explorer Badge. And I have not looked back.

What are you currently working on?

Stacy: I will continue to work in data/analytics as part of my new job, so I plan to keep up on topics like data visualization and health care data analytics. I currently also volunteer as a generalist on a project called ‘Call for Code for Racial Justice’ which involves machine learning, Python, and data science algorithms.

Natalie: I am looking to bring my Python experience to improve dashboards and automation within my own department. I am also exploring engagement prediction and pharmacogenetics.

Elena: I am a continuing my data science studies via the DataCamp career track certification for Data Scientist while also continuing to build my data science portfolio website. Recently, I took part in an Omdena Machine Learning Challenge building a 12-hour Rainfall Forecasting AI Model using Radiosonde data. And I’m continuing to learn from the Kaggle platform, by continuing to compete in the upcoming competitions. I hope to also partake in the next WiDS Datathon in January 2022.

How did you first discover WiDS?

Stacy: I heard about the WiDS Datathon competition from an IBM Slack message board. I was interested because it involved health care and machine learning (the timing was just perfect for what I was studying) and I read all the information available on the WiDS site. I reached out to Natalie first who was really excited and convinced me to make time. Then I contacted Elena who I knew from previous conversations was looking to get into more data science projects. Finally, we all went to an IBM-led overview of the project and Kate was there, who I knew from Call for Code for Racial Justice. We invited her to join our group. I’m so happy we all came together.

Natalie: I was in a machine learning course and sharing my excitement with my colleague Stacy Forsyth. We both shared a goal to join a datathon to get experience with large datasets and to learn from other data scientists. Stacy found the competition and pulled me in.
“I was inspired by the fact that this datathon was for women. I felt safer trying new skills with a really supportive team.” ​
Kate Tereshchenko brought incredible machine learning experience. Elena was fantastic with visualization and data exploration. Stacy was never afraid to jump in and was always pushing us for new ways to apply our learnings. She also kept us organized and on track. Stacy and Elena also monitored the postings of other competitors and shared learnings in our meetings. I cannot understate how much I learned from my teammates.

Elena: I had been thinking about doing some work on Kaggle, and when IBM announced the WiDS event, I jumped right on it. I knew Stacy Forsyth through conversations we had about Call-for-Code, and she asked if I’d like to be part of the team. This was a fantastic first experience with WiDS. I loved being part of an event that inspired and motivated women to put themselves out there and learn new skills. I learned so much from each team member. We have different personalities and different levels of data science experience, and yet we were able to collaborate so well and complement each other so effortlessly. It was probably because we were all eager and curious.

And I second everything Natalie said in her story. She was a huge part of our success because she also had the domain knowledge and expertise. When doing data science projects, a critical aspect of working through the data is having a subject matter expert for the data that you’re working with.

Have you been involved with WiDS since that first experience?

Stacy: I went to the WiDS annual conference. I read all the newsletters and hope to work on more items in the future.

Natalie: This has been my first experience with WiDS.

Elena: Since the first WiDS Datathon, I have tried to stay updated on the WiDS Workshops as I find them very valuable. And I am eager for the next WiDS Datathon to arrive.

How has WiDS made an impact on your life and/or work?

Stacy: I gained a ton of confidence in my new machine learning skills. Also, thanks to Natalie my knowledge of how to identify diabetes skyrocketed. I really enjoyed working with my amazing team.

Natalie: I learned so much from the team and from other data scientists in the datathon. I learned that you should not fear poor prediction results. Every iteration is a chance to learn. Each time we iterated, whether the result was better or worse, we were able to learn something to apply to the next iteration. Our team was fantastic about teaching each other as we went along so that we all benefited from our wide experiences.

Elena: The WiDS Datathon 2021 made a huge impact on me. It absolutely boosted my confidence. It allowed me to see how I could translate my studies into hands-on practical solutions. It taught me not only where my strengths were but also where I had gaps. I will always be able to have this shared experience with my fellow team members. Learning with them gave me the opportunity to fail and appreciate the chance to learn, as a part of the process of experimentation and discovery.

What comes next for you? And what are your hopes for women in the data science in the future?

Stacy: I hope more women enter math and data related fields. I’ve been to many math and data classes where I am the only woman. Natalie, Elena and Kate and I worked as a team to help each other succeed, it was amazing. More positive experiences in math and data and more support will help women step up to the complex field. What’s great about data science is you can apply it to a field you have great interest in (health care, the environment, politics) to solve problems and I would love to see more women approaching problems from this angle. When I started studying math, I had no idea where my career would end up. I love how it has morphed over time to such an interesting role. As for what comes next for me, I will keep learning and growing.

Natalie: I plan to keep my skills fresh and compete again in the future. Bringing equitable representation in data science starts with groups like Women in Data Science.

Elena: I plan to continue to grow and develop into my data science career path, through learning and application. My hopes for women in data science is that we continue to support each other, help each other grow, and be a source of coaching, mentoring and sponsorship.