DataTalks.Club

We talked about:  Nicolas’ background The tech talent market in different countries Hiring data scientists vs data engineers A spike in interest for data engineering roles The importance of recruiters having  technical knowledge The main challenges of hiring data engineers The difference in hiring junior, mid, and senior level data engineers Things recruiters look for in people who switch to a data engineering role The importance of knowing cloud tools The importance of knowing infrastructure tools Preparing for the interview The importance of a formal education The importance having a project portfolio How your current domain influence the interview Conclusion Links:  Nicolas' Twitter: https://twitter.com/n_rassam  Nicolas' LinkedIn: https://www.linkedin.com/in/nicolasrassam/  Onfido is hiring: https://onfido.com/engineering-technology/  Interview with Alicja about recruiting data scientists: https://datatalks.club/podcast/s07e02-recruiting-data-professionals.html Webinar "Getting a Data Engineering Job" with Jeff Katz: https://eventbrite.com/e/310270877547 Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Christopher’s background The essence of DataOps Also known as Agile Analytics Operations or DevOps for Data Science Defining processes and automating them (defining “done” and “good”) The balance between heroism and fear (avoiding deferred value) The Lean approach Avoiding silos The 7 steps to DataOps Wanting to become replaceable DataOps is doable Testing tools DataOps vs MLOps The Head Chef at Data Kitchen What’s grilling at Data Kitchen? The DataOps Cookbook Links: DataOps Manifesto website: https://dataopsmanifesto.org/en/ DataOps Cookbook: https://dataops.datakitchen.io/pf-cookbook Recipes for DataOps Success: https://dataops.datakitchen.io/pf-recipes-for-dataops-success DataOps Certification Course: https://info.datakitchen.io/training-certification-dataops-fundamentals DataOps Blog: https://datakitchen.io/blog/ DataOps Maturity Model: https://datakitchen.io/dataops-maturity-model/ DataOps Webinars: https://datakitchen.io/webinars/ Join DataTalks.Club: https://datatalks.club/slack.html   Our events: https://datatalks.club/events.html

We talked about: Stefan’s background Applications of machine learning in healthcare Sidekick Health – gamified therapeutics How is working for King different from Sidekick Health? The rewards systems in gamified apps The importance of building a strong foundation for a data science team The challenges of building an app in the healthcare industry Dealing with ethics issues Sidekick Health’s personalized recommendations and content The importance of having the right approach in A/B tests (strong analytics and good data) The importance of having domain knowledge to work as a data professional in the healthcare industry Making a data-driven company Risks for Sidekick Health Sidekick Health growth strategy Using AI to help people live better lives Links: LinkedIn: https://www.linkedin.com/in/stefanfreyrgudmundsson/  Job listings: https://sidekickhealth.bamboohr.com/jobs/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Liesbeth’s background What is design? The importance of interaction in design Design as a process (Double Diamond technique) How long does it take to go from an idea to finishing the second diamond? Design thinking (Google’s PAIR) What is a Design Sprint and who should participate in it? Why should data specialists care about design? Challenging your task-giver (asking “why”) How to avoid the “Chinese whisper game” (reiterating the problem) Defining the roadmap for data science teams What is innovation? Bringing innovation to your management Task force-team approach to solving problems Innovation, resource management issues, and using data to back your ideas Words of advice for those interested in design and innovation Links: LinkedIn: https://www.linkedin.com/in/liesbeth-dingemans/ Medium posts on design, innovation, art and AI: https://medium.com/@liesbethmd Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Marijn’s background Standing out in data science Doing the opposite of what people tell you Don’t shoot the messenger (carefully sharing your findings) Advising the seniors Bite off more than you can chew, then chew Marijn’s side projects (finding value in doing things you find interesting) Building a project portfolio Marijn’s NGO project The importance of a team Open source intelligence (OSINT) The importance of soft skills for data experts Marijn’s LinkedIn growth strategy and tips Links:   Twitter: https://twitter.com/MarijnMarkus LinkedIn: https://www.linkedin.com/in/marijnmarkus/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: kDimensions Being self-employed Visual engineering Constrain yourself to get creative Coming up with ideas Visualising difficult concepts The process of creating visuals Creating visuals Learning to create visuals for engineers Consuming with intention to create Learning by breaking code Earning with visuals Adding visuals to blog posts Meor’s book: visual introduction to deep learning Links:   A Visual Introduction to Deep Learning by Meor Amer: https://gumroad.com/a/63231091 kDimensions website: https://kdimensions.com/ Book to learn about Figma: https://figmabook.com/ Jack Butcher's approach: https://www.youtube.com/watch?v=azhqc4K-GAE  Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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