DataTalks.Club

We covered: Barr’s background Market gaps in data reliability Observability in engineering Data downtime Data quality problems and the five pillars of data observability Example: job failing because of a schema change Three pillars of observability (good pipelines and bad data) Observability vs monitoring Finding the root cause Who is accountable for data quality? (the RACI framework) Service leve...

We talked about: Andrada’s background Recommended courses Kaggle and StackOverflow Doing notebooks on Kaggle Projects for learning data science Finding a job and a mentor with Kaggle’s help The process for looking for a job Main difficulties of getting a job Project portfolio and Kaggle Helpful analytical skills for transitioning into data science Becoming better at coding Learning by imitating Is...

We talked about: Knesia’s background Data analytics vs data science Skills needed for data analytics and data science Benefits of getting a masters degree Useful online courses How project management background can be helpful for the career transition Which skills do PMs need to become data analysts? Going from working with spreadsheets to working with python Kaggle Productionizing machine learnin...

We talked about: Demetrious’ background and starting the MLOps community Growing MLOps community Community moderations and dealing with problems Becoming a community and connecting with people Feeling belonged Managing a community as an introvert Keeping communities active Doing custdev and talking to users Random coffee and meeting with community members Organizing community activities Is communi...

We talked about: Lars’ career Doing DataOps before it existed What is DataOps Data platform Main components of the data platform and tools to implement it Books about functional programming principles Batch vs Streaming Maturity levels Building self-service tools MLOps vs DataOps Data Mesh Keeping track of transformations Lake house Links: https://www.scling.com/reading-list/ https://www.scling.co...

We talked about: Ben’s background AI evangelism Ben’s first experiences speaking in public Becoming a great speaker  Key Takeaways and Call to Action Making a good introduction Being Remembered Writing a talk proposal for conferences Landing a keynote Good topics to start talks on Pitching a solution talk to meetup organizers Top public speaking skill to acquire Book recommendations Join Data...

We discussed monetization roles and the capabilities people need to move into those roles. The key roles are ML Researcher, ML Architect, and ML Product Manager. We talked about: Vin's career journey What does it mean to "monetize machine learning" Important monetization metrics Who should we have on the team to make a project successful Machine Learning Researcher (applied and scientist) - backgr...

We talked about:  Admond's career journey What is personal brand How Admond started being active online Publishing on medium and LinkedIn Idea generation process and tools Other platforms Podcasts Offline presence 1x1 meetings Speaking on conferences Having confidence to publish Selling online courses Personal values Admond's course And many other things Links: https://twitter.com/admond1994...

Did you know that there are 3 types different types of data scientists? A for analyst, B for builder, and C for consultant - we discuss the key differences between each one and some learning strategies you can use to become A, B, or C. We talked about: Inspirations for memes  Danny's background and career journey The ABCs of data science - the story behind the idea Data scientist type A - Ana...

We talked about: How we make decisions with machine learning What is decision optimization  Specifying the decision function Emulation for making the best decisions Decision optimization and reinforcement learning Getting started with decision optimization Trends in the industry Links: https://datatalks.club/people/danbecker.html https://www.decision.ai/​ Join DataTalks. Club: https://datatal...

We covered: What is a feature store Problems it solves When to use a feature store  When not to use a feature store The main components When a team should start using a feature store  Links: Feast: https://feast.dev/ https://www.tecton.ai/blog/what-is-a-feature-store/  https://docs.greatexpectations.io/en/latest/reference/core_concepts.html Join DataTalks. Club: https://datatalks.cl...

We covered: What is MLOps The difference between MLOps and ML Engineering Getting into MLOps Kubeflow and its components, ML Platforms Learning Kubeflow DataOps  And other things Links: Microsoft MLOps maturity model: https://docs.microsoft.com/en-us/azure/architecture/example-scenario/mlops/mlops-maturity-model Google MLOps maturity levels: https://cloud.google.com/solutions/machine-learning...

We talked about  open source getting started with open source convincing your employer to contribute to open source public speaking the checklist for open source projects the role of research advocate And many more things! Links from Vincent: https://www.youtube.com/watch?v=68ABAU_V8qI&t=975s&ab_channel=PyData https://www.youtube.com/watch?v=kYMfE9u-lMo&t=958s&ab_channel=PyDat...

We talked about development advocacy for data science. We covered The role of a developer advocate The skills needed for the job and the responsibilities How to become a developer advocate You can find Elle on: Twitter: https://twitter.com/DrElleOBrien LinkedIn: https://linkedin.com/in/drelleobrien DVC's youtube channel: https://www.youtube.com/channel/UC37rp97Go-xIX3aNFVHhXfQ Join DataTalks. Club...

We talk about blogging technical writing. We cover: Why should we write online? What should we write about? Writing at work: Design documents, wikis, etc. The writing process (also at work) Eugene's website:  eugeneyan.com  Follow Eugene on Twitter: https://twitter.com/eugeneyan Suggest topics: https://eugeneyan.com/topic-poll/ Join DataTalks. Club: https://datatalks.club

We talked about: The role of mentoring in career Looking for mentors and preparing for mentoring sessions as a mentee Becoming a mentor And many other things!  Links: Rahul's profile on the mentoring club: https://www.mentoring-club.com/the-mentors/rahul-jain Rahul's article about mentoring: https://rahulj51.github.io/career/coaching/mentoring/2020/06/22/career-coaching.html Join DataTalks. C...

We covered: Getting the recruiter's attention Making CV look great Tailoring your application to the position  And many other things!  Luke's LinkedIn profile: https://www.linkedin.com/in/lukewhipps/ Join DataTalks. Club: https://datatalks.club

We talked about:  Dat's career so far and the startup he co-founded (Priceloop) Who to hire first in a data team How to hire the first data scientist And many other things! You can find Dat on LinkedIn: https://www.linkedin.com/in/dat-tran-a1602320/ Join DataTalksClub: https://datatalks.club

In this podcast, we talk about CRISP-DM - a methodology for organizing data science projects DataTalks. Club is the place to talk about data. Join our community: https://datatalks.club Read more about CRISP-DM here: https://mlbookcamp.com/article/crisp-dm

We talked about: - different roles in a data team: product managers, data analysts, data engineers, data scientists, ML engineers, MLOps engineers - their responsibilities - the skills they need DataTalks. Club is the place to talk about data. Join our community: https://datatalks.club