Hugo Bowne-Anderson is a data scientist, educator, evangelist, content marketer, and data strategy consultant, with extensive experience at Coiled, a company that makes it simple for organizations to scale their data science seamlessly, and DataCamp, the online education platform for all things data. He also has experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC and with organizations such as Data Carpentry. He has developed over 30 courses on the DataCamp platform, impacting over 500,000 learners worldwide through his own courses. He also created the weekly data industry podcast DataFramed, which he hosted and produced for 2 years. He is committed to spreading data skills, access to data science tooling, and open source software, both for individuals and the enterprise.
August 14th, 2023 | Season 1 | 1 hr 23 mins
Hugo speaks with Katharine Jarmul about privacy and security in data science and machine learning. Katharine is a Principal Data Scientist at Thoughtworks Germany focusing on privacy, ethics, and security for data science workflows.
May 24th, 2023 | Season 1 | 1 hr 12 mins
ai, biotech, data science, machine learning, open source, python
Machine learning, deep learning, Bayesian inference for drug discovery, OSS, and accelerating discovery science to the speed of thought!
February 17th, 2023 | Season 1 | 1 hr 16 mins
It’s time to get real about how data science and machine learning actually deliver value! Hugo speaks with Tanya Cashorali, a data scientist and consultant that helps businesses get the most out of data, about what end-to-end data science looks like across many industries, such as retail, defense, biotech, and sports.
December 14th, 2022 | Season 1 | 1 hr 23 mins
Hugo speaks with JD Long, agricultural economist, quant, and stochastic modeler, about data science, ML, and the nitty gritty of decision making under uncertainty, including how we can use our knowledge of risk, uncertainty, probabilistic thinking, causal inference, and more to help us use data science and machine learning to make better decisions in an uncertain world.
December 7th, 2022 | Season 1 | 53 mins 30 secs
Hugo speaks with JD Long, agricultural economist, quant, and stochastic modeler, about decision making under uncertainty and how we can use our knowledge of risk, uncertainty, probabilistic thinking, causal inference, and more to help us use data science and machine learning to make better decisions in an uncertain world.
November 20th, 2022 | Season 1 | 1 hr 9 mins
Hugo reads 3 audio essays about decision science, MLOps, and what happens when machine learning models are everywhere
October 11th, 2022 | Season 1 | 1 hr 22 mins
Hugo speaks with Norma Padron, CEO of EmpiricaLab, about data science education and continuous learning for people working in healthcare, broadly construed, along with how we can think about the democratization of data science skills more generally.
September 29th, 2022 | Season 1 | 1 hr 32 mins
Hugo speaks with Katie Bauer about her time working in data science at both Twitter and Reddit. At the time of recording, Katie was a data science manager at Twitter and prior to that, a founding member of the data team at Reddit.
September 15th, 2022 | Season 1 | 1 hr 45 mins
Hugo speaks with Mark Saroufim, an Applied AI Engineer at Meta who works on PyTorch where his team’s main focus is making it as easy as possible for people to deploy PyTorch in production outside Meta.
August 18th, 2022 | Season 1 | 1 hr 26 mins
Hugo speaks with Sarah Catanzaro, General Partner at Amplify Partners, about investing in data science and machine learning tooling and where we see progress happening in the space.
July 19th, 2022 | Season 1 | 1 hr 41 mins
Hugo speaks with Hamel Husain, Head of Data Science at Outerbounds, with extensive experience in data science consulting, at DataRobot, Airbnb, and Github.
May 16th, 2022 | Season 1 | 1 hr 5 mins
Hugo speaks with Peter Wang, CEO of Anaconda, about what the value proposition of data science actually is, data not as the new oil, but rather data as toxic, nuclear sludge, the fact that data isn’t real (and what we really have are frozen models), and the future promise of data science, Gifting economies with finite game economics thrust onto them.
They also dive into an experimental conversation around open source software development as a model for the development of human civilization, in the context of developing systems that prize local generativity over global extractive principles. If that’s a mouthful, which it was, or an earful, which it may have been, all will be revealed in the conversation.
May 1st, 2022 | Season 1 | 1 hr 2 mins
ai, data science, machine learning, python
Hugo speaks with Peter Wang, CEO of Anaconda, about how Python became so big in data science, machine learning, and AI. They jump into many of the technical and sociological beginnings of Python being used for data science, a history of PyData, the conda distribution, and NUMFOCUS.
April 4th, 2022 | Season 1 | 1 hr 27 mins
Hugo speaks with Jacqueline Nolis, Chief Product Officer at Saturn Cloud (formerly Head of Data Science), about all types of failure modes in data science, ML, and AI, and they delve into bullshit jobs in data science (yes, that’s a technical term, as you’ll find out) –they discuss the elements that are bullshit, the elements that aren’t, and how to increase the ratio of the latter to the former.
March 23rd, 2022 | Season 1 | 1 hr 48 mins
ai, data science, machine learning
Hugo speaks with Jim Savage, the Director of Data Science at Schmidt Futures, about the need for data science in executive training and decision, what data scientists can learn from economists, the perils of "data for good", and why you should always be integrating your loss function over your posterior.
March 9th, 2022 | Season 1 | 1 hr 44 mins
Hugo speaks with Heather Nolis, Principal Machine Learning engineer at T-mobile, about what data science, machine learning, and AI look like at T-mobile, along with Heather’s path from a software development intern there to principal ML engineer running a team of 15.