DataTalks.Club

We talked about: Rahul’s background What do data engineering managers do and why do we need them? Balancing engineering and management Rahul’s transition into data engineering management The importance of updating your skill set Planning the transition to manager and other challenges Setting expectations for the team and measuring success Data reconciliation GDPR compliance Data modeling for Big Data Advice for people transitioning into data engineering management Staying on top of trends and enabling team members The qualities of a good data engineering team The qualities of a good data engineer candidate (interview advice) The difference between having knowledge and stuffing a CV with buzzwords Advice for students and fresh graduates An overview of an end-to-end data engineering process Links: Rahul's LinkedIn: https://www.linkedin.com/in/16rahuljain/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Jakob’s background The importance of A/B tests Statistical noise A/B test example A/B tests vs expert opinion Traffic splitting, A/A tests, and designing experiments Noisy vs stable metrics – test duration and business cycles Z-tests, T-tests, and time series A/B test crash course advice Frequentist approach vs Bayesian approach A/B/C/D tests Pizza dough Links:  Jakob's LinkedIn: https://www.linkedin.com/in/jakob-graff-a6113a3a/ Product Analyst role at Inkitt: https://jobs.lever.co/inkitt/d2b0427a-f37f-4002-975d-28bd60b56d70 Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Valerii’s background Who goes through an ML system design interview System design VS ML System design Preparing for ML system design interviews Machine learning project checklist The importance of defining a goal and ways of measuring it What to do after you set a goal Typical components of an ML system Applying ML systems to real-world problems System design and coding in interviews for new graduates Humans in the validation of model performance Links: Valerii's telegram channel (in Russian): t.me/cryptovalerii Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Lindsay’s background Spiced Academy Career coaching role Reframing your experience Helping with career problems Finding what interests you Tailoring a CV and “spray and pray” Career coaching outside a bootcamp Imposter syndrome After bootcamp Internships Working with recruiters Networking on LinkedIn Links: Lindsay's LinkedIn: https://www.linkedin.com/in/lindsay-mcquade/ Impostor questionnaire: http://impostortest.nickol.as/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Greg’s background Responsibilities of Data Product Manager Understanding customer journey Interviewing business partners and decision-makers Products sense, product mindset, and product roadmap Working backwards Driving the roadmap Building a roadmap in Excel Measuring success Advice for teams that don’t have a product manager Links: Greg's LinkedIn: https://www.linkedin.com/in/greg-coquillo/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Alicja’s background The hiring process Sourcing and recruiting Managing expectations Making the job description attractive Selecting profiles during sourcing Profile keywords The importance of a Master’s vs a Bachelor’s degree vs a PhD Improving CV Interview with the recruiter Salary expectations Advice for “career changers” Cover letters Data analysts Double Bachelor’s degrees The most difficult part of hiring Coursera courses on the CV Making a good impression on recruiters Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Alexey’s background Being a principal data scientist DataTalks.Club The beginning and growth of DataTalks.Club Sustaining the pace Types of talks Popular and favorite talks Making DataTalks.Club self-sufficient Alexey’s book and course Advice for people starting in data science and staying motivated Not keeping up to date with new tools Staying productive Learning technical subjects and keeping notes Inspiration and idea generation for DataTalks.Club Links: https://eugeneyan.com/writing/informal-mentors-alexey-grigorev/  Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We don't have a new episode this week, but we have an amazing conversation with Sejal Vaidya from August We talked about Sejal's background Why transitioning to ML engineering Three phases of development of a project Why data engineers should get involved in ML Technologies Tips for people who want to transition Soft skills and understanding requirements Helpful resources Resources: ML checklist (https://twolodzko.github.io/ml-checklist.html) Machine Learning Bookcamp (https://mlbookcamp.com/) Made with ML course (https://madewithml.com) Full-stack deep learning (https://fullstackdeeplearning.com) Newsletters: mlinproduction, huyenchip.com, jeremyjordan.me, mihaileric.com Sejal's "Production ML" twitter list (https://twitter.com/i/lists/1212819218959351809) Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Mariano’s background Typical day of a manager Becoming a manager Preparing for the transition Balancing projects and assumptions Search and recommendations Dealing with unfamiliar domains Structuring projects Connecting product and data science Rules of Machine Learning CRISP-DM and deployment Giving feedback Dealing with people leaving the team Doing technical work as a manager Dealing with bad hires Keeping up with the industry Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Ivan’s role at Personio Ivan’s background Studying technical management Managing a software team NLP teams NLP engineers Becoming an NLP engineer Computer vision NLP engineer vs ML engineer Conversational designers Linguistics outside of chatbots When does a team need an NLP engineer or a linguist? The future of NLP NLP pipelines GPT-3 Problems of GPT-3 Does GPT-3 make everything obsolete? What NLP actually is? Does NLP solve problems better than humans? State of language translation NLP Pandect Links: https://github.com/ivan-bilan/The-NLP-Pandect https://github.com/ivan-bilan/The-Engineering-Manager-Pandect https://github.com/ivan-bilan/The-Microservices-Pandect Ivan's presentation about NLP: https://www.youtube.com/watch?v=VRur3xey31s Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html