The Banana Data Podcast
Ethical Implications of Humanizing Your Data
Floor to Ceiling Data Strategy
Data Visualization w/ Nathan Mannheimer, Director of Data Science & ML at Tableau
Leverage Storytelling With Data
The Importance of Human in the Loop AI With Christina Hsiao
What Happens When Humanization Fails? A Conversation With Jeremie Harris of Towards Data Science
Methodology & Functionality in Differing Data Science Roles
Rise of the Data Citizen
Create Technological Emotional Bonds w/ Creative Intelligence
What Does It Mean to Humanize Your Data?
In English Please: Part 2
In English Please: Part 1
Banana Byte: AI Is Revolutionizing Biological Sciences - What Are the Implications?
2021 Trends in AI
Banana Byte: Data Tools We're Thankful For
What Does It Mean to Be a Data Scientist?
Banana Byte: Using AI to extend People Analytics
How The COVID Monitor Project is Driving Data Transparency & Access
Our Social Network
Banana Byte: Is Investing Your Time in Code Worth It?
The Data Debate Stage
Banana Byte: The Good, the Bot, the Ugly
A Deeper Look at CAPTCHA Systems
Banana Byte: Misinterpreted Data and Understanding Uncertainty
Conscious Data Disclosure & AI Consumption
Banana Byte: Viral Tweets, Skynet, and The Reality of Data Science
Exciting Global AI
Banana Byte: How Much Math Do You Need for Data Science?
Machine Learning Pet Peeves
Weighing the Good and Bad in AI
Banana Byte: Understanding the Value of Deep Learning
Banana Byte: The Hidden Costs of Cloud Computing
Banana Byte: Zoom Privacy
Data Nuance & Human-in-the-Loop Monitoring
Fighting Cheating AI & Redefining AI companies
The Messiness of Data
Analytics in the NFL & Revolutions in Data Discovery
Deepfakes & Data Upskilling
Is AI Worth it?
The Roles in Data Science, feat. Tristan Handy, CEO & Founder of Fishtown Analytics
How We Talk about AI, feat. Karen Hao, MIT Technology Review
The Everyday of AI
The Future (and the now) of AI with Azalia Mirhoseini, Senior Researcher at Google Brain
Do I do AI?
Finding Community in Data Science with Reshama Shaikh, key scikit-Learn sprint organizer
Why Open Source? feat. Andreas Mueller, a Core Contributor of scikit-Learn
Predicting AI Trends for 2020
Life after Production, a Tale of Technical Debt with Dan Shiebler, Twitter Eng
The Essentials (and not-so essentials) of Data Science Pipelines
What Makes a Good Data Science Practice
The Death of Data Viz, Cross-Cultural AI, and AI Auditing
Prioritizing training data, model interpretability, and dodging an AI Winter
Building accessible queries, codes, and speech using AI
AI Meets World: GDPR, the AI Job Apocalypse, and AI’s carbon footprint
A New Kind of Relationship with AI: Robopets, AI Art, and AI EQ
The future of data according to predictions, Python 3.0, and people.
Culpability in AI failures, Fooling NNs with NNs, AI for cancer screenings, and Epsilon Greedy Multi-Armed Bandits
Biased Data & the Perfect Answer, Multi-Armed Bandits, and the GPUs Behind Your Neural Networks
Being an Ethical Data Scientist, Federated Learning in Healthcare, and Dropping the “Best Model” Approach