Wednesday 

Room 5 

15:00 - 16:00 

(UTC+01

Talk (60 min)

Data Science beasts (failures) and where to find them

The nature of the field of Data Science encourages trial and error, but we can do a better job of destigmatizing failure and learn from our collective experiences.

AI
Big Data
Machine Learning

Join me as I take us on an adventure to find the beasts i.e. the different ways Data Science projects can fail. I will be talking about 4 major reasons for failure (data, infrastructure, implementation, and culture), their different aspects, and supplementing it with my experiences and case studies. I will also share how to control these beasts and recommend actions to be taken to ensure a successful end-to-end Data Science project.

By the end of this session, audience members will have a better understanding of the different ways a Data Science project can fail. They will be able to identify points of failure in the context of different case studies. They will feel motivated to share and learn from their failures and leave with actionable steps to reduce the risk of failing.

Grishma Jena

Grishma Jena is a Data Scientist with the UX Insights team at IBM in San Francisco. As the only Data Scientist in the org, she supports 80+ user researchers and designers and uses data to understand user struggles and opportunities to enhance product experiences.

She earned her Masters in Computer Science at University of Pennsylvania. Her research interests are in Machine Learning and Natural Language Processing. She has delivered 40+ talks and workshops at multiple conferences around the globe including PyCon US (largest Python conference in the world) and O’Reilly OSCON. She has also taught Python at the San Francisco Public Library and frequently guides school and university students.

Grishma is extremely passionate about encouraging, mentoring, and empowering people, especially women and students, in the world of technology. Currently, she serves on the AI/ML Advisory Board for DevNetwork. She is also an ambassador for the Women in Data Science initiative, as well as a part of the the leadership team for Society of Women Engineers - Golden Gate Section.

In her free time, she writes, cooks and likes conducting workshops and delivering talks. She likes explaining things in an easy-to-understand format, drawing analogies from real life.