We talked about: Juan Pablo's Backround Data engineering resources Teaching calculus Transitioning to Analytics Data Analytics bootcamp Getting money while studying Going to meetups to get a job Looking for uncrowded doors Using LinkedIn Portfolio Talking to people on meetups Eight tips to get your first analytics job Consider contracts and temporary roles Getting experience with non-profits Create your own internship Networking Website for hosting a portfolio I’m a math teacher. What should I learn first? Analytics engineering Best suggestion: keep showing up Networking on online conferences Communication skills and being organized Links: Website: https://www.thatjuanpablo.com/ Twitter: https://twitter.com/thatjuanpablo BROKE teacher to FAANG engineer Twitter thread: https://twitter.com/thatjuanpablo/status/1475806246317875203 LinkedIn: https://www.linkedin.com/in/thatjuanpablo/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Ellen’s background Why Ellen switched from data science to data engineering The overlap between data science and data engineering Skills to learn and improve for data engineering Ways to pick up and improve skills (advice for making the transition) What makes a data engineering course “good” Languages to know for data engineering The easiest part of transitioning into data engineering The hardest part of transitioning into data engineering Common data engineering team distributions People who are both data scientists and data engineers Pet projects and other ways to pick up development skills Dealing with cloud processing costs (alerts, billing reports, trial periods) Advice for getting into entry level positions Which cloud platform should data engineers learn? Links: Twitter: https://twitter.com/ellen_koenig LinkedIn: https://www.linkedin.com/in/ellenkoenig/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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