Episode 57: Building Data Science Projects with Pier Paolo-Ippolito

Show Notes(2:20) Pier shared his college experience at the University of Southampton studying Electronic Engineering.(3:46) For his final undergraduate project, Pier developed a suite of games and used machine learning to analyze brainwaves data that can classify whether a child is affected or not by autism.(11:26) Pier went over his favorite courses and involvement with the AI Society during his additional year at the University of Southampton to get a Master’s in Artificial Intelligence.(13:40) For his Master’s thesis called “Causal Reasoning in Machine Learning,” Pier created and deployed a suite of Agent-Based and Compartmental Models to simulate epidemic disease developments in different types of communities.(26:51) Pier went over his stints as a developer intern at Fidessa and a freelance data scientist at Digital-Dandelion.(29:21) Pier reflected on his time (so far) as a data scientist at SAS Institute, where he helps their customers solve various data-driven challenges using cloud-based technologies and DevOps processes.(33:37) Pier discussed the key benefits that writing and editing technical content for Towards Data Science to his professional development.(36:31) Pier covered the threads that he kept pulling with his blog posts.(38:50) Pier talked about his Augmented Reality Personal Business Card created in HTML using the AR.js library.(41:12) Pier brought up data structures in two other impressive JavaScript projects using TensorFlow.js and ml5.js.(44:19) Pier went over his experience working with data visualization tools such as Plotly, R Shiny, and Streamlit.(47:27) Pier talked about his work on a chapter for a book called “Applied Data Science in Tourism” that is going to be published with Springer this year.(48:37) Pier shared his thoughts regarding the tech community in London.(49:19) Closing segment.Pier’s Contact InfoWebsiteLinkedInTwitterGitHubMediumPatreonKaggleMentioned Content“Alleviate Children’s Health Issues Through Games and Machine Learning”“Causal Reasoning in Machine Learning”Andrej Karpathy (Director of AI and Autopilot at Tesla)Cassie Kozyrkov (Chief Decision Scientist at Google)Iain Brown (Head of Data Science at SAS)“The Book Of Why” (By Judea Pearl)“Pattern Recognition and Machine Learning” (by Christopher Bishop)

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

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