Datacast Episode 19: Data Science in South Africa with Tristan Bergh

Datacast Episode 19: Data Science in South Africa with Tristan Bergh

Tristan Bergh is a systems thinker and data scientist living in Cape Town, South Africa. He studied aeronautical engineering before working as an enterprise software engineer. He worked as an architect on AFIS implementations in Southern Africa, moving into middleware for large fin-tech systems. He restarted his system modeling and machine learning consulting services through 2014, delivering predictive analytics models in healthcare and other domains.

Datacast Episode 18: NLP at Crisis Text Line with Ankit Gupta

Datacast Episode 18: NLP at Crisis Text Line with Ankit Gupta

Ankit Gupta is a data scientist at Crisis Text Line, a New York-based not-for-profit tech startup providing 24/7 free text-based crisis support to individuals. At Crisis Text Line, Ankit developed an AI-driven triage system that detects signals of suicidality within the first few messages sent by texters. Using the triage system, the counselors can serve 93% of at-risk texters in under five minutes.

Datacast Episode 17: Computer Vision Research with Genevieve Patterson

Datacast Episode 17: Computer Vision Research with Genevieve Patterson

Genevieve Patterson is the chief scientist at TRASH, a startup that is developing computational filmmaking tools for mobile iphono-graphers. Before that, she was a Postdoctoral Researcher at Microsoft Research New England. Her work is about creating dialog between AI and people. Her interests include video understanding, visual attribute discovery, human-in-the-loop systems, fine-grained object recognition, medical image understanding, and active learning. Genevieve received her Ph.D. from Brown University in 2016 under the direction of James Hays.

Datacast Episode 16: Bayesian Probabilistic Programming with Peadar Coyle

Datacast Episode 16: Bayesian Probabilistic Programming with Peadar Coyle

Peadar Coyle is a Data Scientist and Engineer based in London. He regularly speaks at conferences and has written a book consisting of numerous interviews with Data Scientists throughout the world. He is also a passionate Open-Source evangelist, himself a supporter who has contributed to PyMC3. Most recently, he founded a stealth startup that is working on hyper-personalized audio.

Datacast Episode 15: Thoughts on Data Science from the perspective of a behavioral scientist with Nick Gaylord

Datacast Episode 15: Thoughts on Data Science from the perspective of a behavioral scientist with Nick Gaylord

Nick Gaylord has worked as a data scientist in the Bay Area for about the last 5 years. Currently, he’s a member of the Johnson & Johnson Health Technology team, and prior to that, he has worked in different fields ranging from small business revenue analytics to enterprise machine-learning-as-a-service platforms. Like many data scientists, he started out as an academic before transitioning to industry, in his case earning a Ph.D. in Psycholinguistics from the University of Texas at Austin in 2013.

Datacast Episode 14: Overcoming Impostor Syndrome with Conor Dewey

Datacast Episode 14: Overcoming Impostor Syndrome with Conor Dewey

Conor Dewey is a data scientist at Squarespace who spends his time thinking about growth and engagement. He frequently shares insights from his experience interviewing at top companies. More generally, he offers resources and advice on Medium and Github in the hopes of ‘open sourcing’ his journey to data science. Conor also manages a weekly data science newsletter with 1000+ subscribers.

Datacast Episode 13: Transition From Academia to Data Science with Martina Pugliese

Datacast Episode 13: Transition From Academia to Data Science with Martina Pugliese

Martina is a physicist and works as Data Science Lead at Mallzee, based in Edinburgh (Scotland). She loves looking at data regardless of the topic and area and believes the most enjoyable thing in Data Science is analyzing your data, finding the one you need for your question, and producing facts out of it. Throughout her education and job experience, she worked with data in epidemic dynamics, linguistics, and fashion. She also loves producing hand-crafted data visualizations and keeps studying and improving, whether it’s about Machine Learning or leadership topics.

An Introduction to Big Data: Distributed Data Processing

An Introduction to Big Data: Distributed Data Processing

This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.

An Introduction to Big Data: Decision Trees

An Introduction to Big Data: Decision Trees

This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.

An Introduction to Big Data: Clustering

An Introduction to Big Data: Clustering

This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.