The Women in Big Data (WiBD) Spring Hackathon 2024, organized by WiDS and led by WiBD’s Global Hackathon Director Rupa Gangatirkar, sponsored by Gilead Sciences, offered an exciting opportunity to sharpen data science skills while addressing critical social impact challenges. This year’s event focused on healthcare inequity, using a rich oncology dataset enriched with socio-economic and climate data. Participants had to predict the time it takes for patients to receive a metastatic cancer diagnosis, gaining valuable insights into how climate patterns affect healthcare access. WiDS Worldwide aimed to increase women’s participation in data science, fostering a supportive and collaborative environment for learning and growth.

 

From Bay Area Chapter By: Yoga Ramachandran

Thrilled to announce that Ananya Paul, Gurdeep Kalra, Sowmya Keshavaiah, Yoga Ramachandran, and our exceptional technical team leader, Sanja Damjanovic, Ph.D, have successfully completed the Women in Big Data Spring 2024 Hackathon in collaboration with Women in Data Science (WiDS) Worldwide. I want to express how proud I am of our team’s accomplishments. Their journey of growth and learning has been truly inspiring to watch – Anat Zohar, Bay Area chapter Hackathon Lead. 

The Inspiration Behind Joining the Hackathon

As a data science enthusiast, I am always on the lookout for opportunities to apply my skills and learn new ones. When I heard about the WiBD Spring Hackathon 2024, I knew it was the perfect event for me. The unique oncology dataset from Gilead Sciences added an extra layer of intrigue and complexity to the task. Additionally, the emphasis on diversity and inclusion, encouraging the participation of women in data science, resonated with my personal and professional values.

The Experience of the Hackathon

I was part of the Bay Area chapter, led by Anat Zohar, with our team mentored and guided by Sanja Damjanovic, Ph.D.

From April 9th to June 1st, our team of four—three women and one man, a diverse mix of university students, professionals, and data enthusiasts—dove into the challenge with enthusiasm and determination. Working with a medical dataset was a new domain for me, and it was both exciting and daunting. The initial phase involved understanding the data, which required extensive research and exploration of articles and write-ups related to oncology.

The hackathon was hosted on Kaggle, a platform known for its collaborative environment and robust tools for data science competitions. We leveraged Kaggle’s features to share our work seamlessly and made multiple submissions from each team member. This iterative process was crucial for refining our models and improving our predictions.

One of the highlights of our hackathon experience was the guidance we received from our mentor, Sanja. She provided us with incredible resources and learning materials that were instrumental in shaping our approach to the machine learning problem. Sanja’s expertise helped us understand the nuances of data analysis and prediction in the context of medical data. Her support, coupled with weekly interactions and updates, ensured that we stayed on track and continually learned from each other.

Reflections on the Journey

Having gone through the WiBD Spring Hackathon 2024, I feel a profound sense of accomplishment and growth. The experience was a remarkable blend of learning and collaboration. Working with a diverse team exposed me to different perspectives and approaches, enriching my understanding of data science and its applications in healthcare.

The challenge of predicting metastatic cancer diagnosis times pushed us to think critically and creatively. It reinforced the importance of thorough data exploration and the need to stay updated with the latest research in the field. The hackathon also highlighted the power of mentorship and community support in achieving complex goals.

In conclusion, the WiBD Spring Hackathon 2024 was more than just a competition; it was a journey of discovery and empowerment. I am grateful for the opportunity to be a part of this incredible event and look forward to applying the lessons learned in future projects. Participating in this hackathon has not only enhanced my data science skills but also strengthened my commitment to diversity and inclusion in the tech community. Special thanks to Anat Zohar and our mentor, Sanja Damjanovic, for their invaluable support and guidance.

 

From: Pittusburgh Chapter By: Aruna Nadesan

Our WiBD Pittsburgh chapter team was excited and grateful for the opportunity to participate in the WiBD’s Spring Hackathon 2024. Under Amina Wasiq’s leadership, our diverse team of five Data Scientists and Machine Learning Engineers successfully completed the challenge and received certificates of achievement and monetary rewards from WiBD !

We explored the impact of climate patterns on healthcare equity and timely metastatic cancer diagnosis. As the team stated, “It was a unique opportunity to collaborate, innovate, and challenge ourselves. We enhanced our data science skills while improving our time management and problem-solving abilities.”

Our team of data enthusiasts and data learners who are all graduate students included Dhruvi Patel, Uma Uma, Satvinder Kaur and Surekha Verru. We hope that the hackathon experience enhanced their exposure to real world data and skills to tackle real world challenges using data engineering and machine learning skills.

We look forward to future hackathons and continuing our journey in data science!

 

From: Saint Louis Chapter By: Jayanth Krishnamurthy

Throughout my time working on the hackathon, I learned more about the data preprocessing and data preparation steps of data exploration. My team and I were tasked with analyzing how climate patterns affected access to healthcare by looking at datasets that described demographic and societal factors of various patients with breast cancer.

Personally, I was less familiar with the python libraries necessary for this project, such as xgboost_type, sklearn, and others. After learning about these different libraries, I attempted to make an XGBoost model that would properly predict the amount of time for patients to receive a diagnosis.

The journey, filled with learning and collaborative opportunities, granted me a deeper appreciation of Data science concepts and how it can be used to solve business and social challenges and has further invigorated my pursuit of excellence in predictive modeling.

I want to sincerely thank our amazing team lead, Kirthi Venkatesh and St Louis Chapter Hackathon Lead, Usha Ramalingam for their guidance and mentorship. I want to extend my thanks to my teammates, Indira Meduri, Prachi Gupta and Anu Varghese.