Dr. Jon Krohn

Unofficial, machine-generated summaries, as of 2026-11-09.

↗635: The Perils of Manually Labeling Data for Machine Learning Models — with Shayan Mohanty
↗Privacy-preserving machine learning: How to develop and deploy it
↗Responsible data economy and its key principles
↗Federated learning and its relationship to deep learning
↗634: Model Error Analysis — with Serg Masís
↗What is differential privacy? And how is PrivateSQL related to it?
↗What is homomorphic encryption?
↗633: Responsible Decentralized Intelligence — with Dawn Song
↗Must-have skills for entry-level data analysts
↗Why Luke's submariner career makes him a better data analyst
↗The best time to use Power BI
↗632: Liquid Neural Networks — with Adrian Kosowski
↗What aspiring data scientists get wrong
↗Where Luke gets his inspiration for making YouTube videos
↗631: Data Analytics Career Orientation — with @LukeBarousse
↗How to make data science workflows reproducible
↗The Transformers package from Hugging Face
↗What is a Data Science Developer Advocate? And why might it be a great career for you?
↗630: Resilient Machine Learning — with Dan Shiebler
↗Datalore: a new, collaborative data science platform from JetBrains
↗Why data scientists need to be involved in data preparation
↗629: Software for Efficient Data Science — with Jodie Burchell
↗Stacked machine learning models: Why you should be using them
↗How to address bias in data sets
↗Why you should consider using AutoML
↗628: The Critical Human Element of Successful A.I. Deployments — with Keith McCormick
↗When to use GPUs for Machine Learning
↗How the H2O AutoML platform works
↗627: AutoML: Automated Machine Learning — with Erin LeDell
↗How blockchain, crypto, and data science are evolving together
↗What makes one country more willing to embrace blockchain and cryptocurrencies than others?
↗How data science and ML identify illegal blockchain activity
↗SDS 626: Subword Tokenization with Byte-Pair Encoding — with @JonKrohnLearns​
↗Illegal activities in blockchains and how Chainalysis helps trace them
↗Performing Analytics on Blockchain Data: Opportunities and Caveats
↗SDS 625: Analyzing Blockchain Data and Cryptocurrencies — with Kim Grauer
↗Top tools to accelerate your data career
↗Data Analyst vs Data Scientist vs Data Engineer: Similarities and Differences
↗Top soft skills Data Analysts need
↗SDS 624: Imagen Video: Incredible Text-to-Video Generation — with @JonKrohnLearns​
↗Must-have technical skills for Data Analysts
↗Could anyone become a Data Analyst?
↗SDS 623: Data Analyst, Data Scientist, and Data Engineer Career Paths — with @ShashankData
↗Top resources to become expert at crypto and blockchains
↗Data science applications related to blockchains
↗How to analyze crypto/blockchain data
↗SDS 622: Burnout: Causes and Solutions — with Christina Maslach
↗Crypto vs fiat currencies: Similarities and differences
↗What are Blockchains and Crypto?
↗SDS 621: Blockchains and Cryptocurrencies: Analytics and Data Applications — with Philip Gradwell
↗#Shorts To search data efficiently, use dense vectors
↗Annoy: Searching efficiently through high-dimensional vectors
↗Spotify's music recommender algorithm: How it works
↗Data science and engineering interviews: How to succeed
↗SDS 620: OpenAI Whisper: General-Purpose Speech Recognition — with @JonKrohnLearns
↗How to interview data scientists and software developers
↗Modal: an upgrade from the Kubernetes model deployment standard
↗Causal modeling: The best tools and resources for self-study
↗Causal modeling: Why and when is it helpful?
↗Large-scale causal experimentation: What are the requirements to do it?
↗SDS 618: The Joy of Atelic Activities — with Jon Krohn
↗Using causal modeling to make better decisions – examples from Lyft
↗Sequence analytics: How is it different from analytics in general?
↗SDS 617: Causal Modeling and Sequence Data — with Sean Taylor
↗The best tools and skills to master before going to a data science interview
↗Strategies to answer “Do you have any questions for us?” in job interviews
↗How to develop self-promotion skills
↗SDS 616: The Four Requirements for Expertise (beyond the “10,000 Hours”) — with Jon Krohn
↗Data science interviews at the tech giants: Three major misconceptions
↗DataLemur: Free Platform for Practicing SQL Queries (for Data Science Interviews)
↗SDS 615: How to Ace Your Data Science Interview — with Nick Singh
↗Real-world applications of Causal Machine Learning
↗Can Causal Machine Learning be fully automatic?
↗Causal Inference: The Four Key Steps (and how they impact Machine Learning)
↗SDS 614: Thriving on Information Overload — with Ross Dawson
↗PyWhy: the leading open-source library for Causal Machine Learning?
↗What is Causal Machine Learning and how does it differ from Correlational Machine Learning?
↗SDS 613: Causal Machine Learning — with Emre Kiciman
↗How A.I. could transform life for humans in the coming decades
↗The future of open-endedness research in A.I.
↗Intelligence versus expertise in A.I. agents
↗SDS 612: More Guests on Fridays — with Jon Krohn
↗What is the Novelty Search algorithm?
↗Why Greatness Cannot Be Planned: The Myth of the Objective
↗SDS 611: Open-Ended A.I.: Practical Applications for Humans and Machines — with Kenneth Stanley
↗When is a Data Mesh not the right solution?
↗ML Model Training and Inference with a Data Mesh
↗Data Mesh, Blockchain, and Web3: What relates them?
↗SDS 610: Who Dares Wins — with Jon Krohn
↗Data Mesh: current challenges facing the approach
↗What is a Data Mesh and why do we need them?
↗SDS 609: Data Mesh — with Zhamak Dehghani
↗Causal tools can't actually infer causality
↗What are Multilevel Models and why are they useful?
↗The top resources for learning about Causality
↗SDS 608: Daily Habit #11: Assigning Deliverables — with Jon Krohn
↗Is BART (Bayesian Additive Regression Trees) the best Causal Inference tool?
↗How to teach people to make Causal Inferences effectively
↗SDS 607: Inferring Causality — with Jennifer Hill
↗Is having a PhD useful for teaching?