Women in Data Science
We envision an equitable future when women are equally represented as practitioners and leaders in Data Science and AI, and share equally in Decision Making, Economic Prosperity, and Opportunities.
We envision an equitable future when women are equally represented as practitioners and leaders in Data Science and AI, and share equally in Decision Making, Economic Prosperity, and Opportunities.
The WiDS Datathon is an opportunity to discover and hone data science skills while solving an interesting and critical social impact challenge.
Discover WiDS Ambassador events worldwide, offering conferences, workshops, NextGen events, and networking opportunities both in-person and online.
empowers women to reach their full potential in the field of data science. We elevate and celebrate the outstanding work of women on a global scale, fostering respect, collaboration, connection, and visibility. The WiDS community creates access to programs, content, and resources that inspire, educate and support, so that vibrant voices and ideas can be shared with the world.
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Get inspired from outstanding women discussing their exceptional work in data science and related fields, in a wide variety of research domains.
Find content based on topics of interest, curated and organized around overarching concepts in data science. Resources for every level, from beginner to advanced.
RECOMMENDED VIDEOS
Learn from data science leaders from around the world talking about their work, their journeys, and lessons learned along the way.
Learn from data science leaders from around the world talking about their work, their journeys, and lessons learned along the way.
TOPICS: Foundations (Mathematics/Statistics) Claudia Perlich, a leading expert in predictive modeling shares about her work and views on the challenges of using AI and machine learning systems. Perlich discusses the importance of understanding the uncertainty and biases inherent in these models, and the need for careful evaluation and testing before deploying them, especially in high-stakes domains. She emphasizes the collective responsibility of data scientists, domain experts, and decision-makers to ensure these systems are used responsibly and equitably. She also shares insights on the skills and mindset needed for a successful career in predictive modeling, highlighting the value of data curiosity, scientific thinking, and effective communication.