We talked about:

Rob’s background Going from software engineering to Bayesian modeling Frequentist vs Bayesian modeling approach About integrals Probabilistic programming and samplers MCMC and Hakaru Language vs library Encoding dependencies and relationships into a model Stan, HMC (Hamiltonian Monte Carlo) , and NUTS Sources for learning about Bayesian modeling Reaching out to Rob

Links:

Book 1: https://bayesiancomputationbook.com/welcome.html Book/Course: https://xcelab.net/rm/statistical-rethinking/

Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about

Chris’s background Switching careers multiple times Freedom at companies Chris’s role as an internal consultant Chris’s sabbatical ChatGPT How being a generalist helped Chris in his career The cons of being a generalist and the importance of T-shaped expertise The importance of learning things you’re interested in Tips to enjoy learning new things Recruiting generalists The job market for generalists vs for specialists Narrowing down your interests Chris’s book recommendations

Links:

Lex Fridman: science, philosophy, media, AI (especially earlier episodes): https://www.youtube.com/lexfridman Andrej Karpathy, former Senior Director of AI at Tesla, who's now focused on teaching and sharing his knowledge: https://www.youtube.com/@AndrejKarpathy Beautifully done videos on engineering of things in the real world: https://www.youtube.com/@RealEngineering Chris' website: https://szafranek.net/ Zalando Tech Radar: https://opensource.zalando.com/tech-radar/ Modal Labs, new way of deploying code to the cloud, also useful for testing ML code on GPUs: https://modal.com Excellent Twitter account to follow to learn more about prompt engineering for ChatGPT: https://twitter.com/goodside Image prompts for Midjourney: https://twitter.com/GuyP Machine Learning Workflows in Production - Krzysztof Szafanek: https://www.youtube.com/watch?v=CO4Gqd95j6k From Data Science to DataOps: https://datatalks.club/podcast/s11e03-from-data-science-to-dataops.html

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

David’s background A day in the life of a professor David’s current projects Starting a school The different types of professors David’s recent papers Similarities and differences between research labs and startups Finding (or creating) good datasets David’s lab Balancing research and teaching as a professor David’s most rewarding research project David’s most underrated research project David’s virtual data science seminars on YouTube Teaching at universities without doing research Staying up-to-date in research David’s favorite conferences Selecting topics for research Convincing students to stay in academia and competing with industry Finding David online

Links: 

David A. Bader: https://davidbader.net/ NJIT Institute for Data Science: https://datascience.njit.edu/ Arkouda: https://github.com/Bears-R-Us/arkouda NJIT Data Science YouTube Channel: https://www.youtube.com/c/NJITInstituteforDataScience

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Christiaan’s background Usual ways of collecting and curating data Getting the buy-in from experts and executives Starting an annotation booklet Pre-labeling Dataset collection Human level baseline and feedback Using the annotation booklet to boost annotation productivity Putting yourself in the shoes of annotators (and measuring performance) Active learning Distance supervision Weak labeling Dataset collection in career positioning and project portfolios IPython widgets GDPR compliance and non-English NLP Finding Christiaan online

Links:

My personal blog: https://useml.net/ Comtura, my company: https://comtura.ai/ LI: https://www.linkedin.com/in/christiaan-swart-51a68967/ Twitter: https://twitter.com/swartchris8/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

DataTalks.Club intro Tereza’s background Working as a coach Identifying the mismatches between your needs and that of a company How to avoid misalignments Considering what’s mentioned in the job description, what isn’t, and why Diversity and culture of a company Lack of a salary in the job description Way of doing research about the company where you will potentially work How to avoid a mismatch with a company other than learning from your mistakes Before data, during data, after data (a company’s data maturity level) The company’s tech stack Finding Tereza online

Links: 

Decoding Data Science Job Descriptions (talk): https://www.youtube.com/watch?v=WAs9vSNTza8 Talk at ConnectForward: https://www.youtube.com/watch?v=WAs9vSNTza8 Slides: https://www.slideshare.net/terezaif/decoding-data-science-job-descriptions-250687704 Talk at DataLift: https://www.youtube.com/watch?v=pCtQ0szJiLA Slides: https://www.slideshare.net/terezaif/lessons-learned-from-hiring-and-retaining-data-practitioners

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Daynan’s background Astronomy vs cosmology Applications of data science and machine learning in astronomy Determining signal vs noise What the data looks like in astronomy Determining the features of an object in space Ground truth for space objects Why water is an important resource in the space economy Other useful resources that can be found in asteroids Sources of asteroids The data team at an asteroid mining company Open datasets for hobbyists Mission and hardware design for asteroid mining Partnerships and hires

Links: 

LinkedIn: https://www.linkedin.com/in/daynan/ We're looking for a Sr Data Engineer: https://boards.eu.greenhouse.io/karmanplus/jobs/4027128101?gh_jid=4027128101 Minor Planet Center: https://minorplanetcenter.net/- JPL Horizons has a nice set of APIs for accessing data related to small bodies (including asteroids): https://ssd.jpl.nasa.gov/api.html ESA has NEODyS: https://newton.spacedys.com/neodys   IRSA catalog that contains image and catalog data related to the WISE/NEOWISE data (and other infrared platforms): https://irsa.ipac.caltech.edu/frontpage/ NASA also has an archive of data collected from their various missions, including a node related to small bodies: https://pds-smallbodies.astro.umd.edu/ Sub-node directly related to asteroids: https://sbn.psi.edu/pds/ Size, Mass, and Density of Asteroids (SiMDA) is a nice catalog of observed asteroid attributes (and an indication of how small our sample size is!): https://astro.kretlow.de/?SiMDA The source survey data, several are useful for asteroids: Pan-STARRS (https://outerspace.stsci.edu/display/PANSTARRS)

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html