We talked about:

Juan’s background Typical problems in marketing that are solved with ML Attribution model Media Mix Model – detecting uplift and channel saturation Changes to privacy regulations and its effect on user tracking User retention and churn prevention A/B testing to detect uplift Statistical approach vs machine learning (setting a benchmark) Does retraining MMM models often improve efficiency? Attribution model baselines Choosing a decay rate for channels (Bayesian linear regression) Learning resource suggestions Bayesian approach vs Frequentist approach Suggestions for creating a marketing department Most challenging problems in marketing The importance of knowing marketing domain knowledge for data scientists Juan’s blog and other learning resources Finding Juan online

Links: 

Juan's PyData talk on uplift modeling: https://youtube.com/watch?v=VWjsi-5yc3w Juan's website: https://juanitorduz.github.io Introduction to Algorithmic Marketing book: https://algorithmic-marketing.online Preventing churn like a bandit: https://www.youtube.com/watch?v=n1uqeBNUlRM

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: 

Gloria’s background Working with MATLAB, R, C, Python, and SQL Working at ICE Job hunting after the bootcamp Data engineering vs Data science Using Docker Keeping track of job applications, employers and questions Challenges during the job search and transition Concerns over data privacy Challenges with salary negotiation The importance of career coaching and support Skills learned at Spiced Retrospective on Gloria’s transition to data and advice Top skills that helped Gloria get the job Thoughts on cloud platforms Thoughts on bootcamps and courses Spiced graduation project Standing out in a sea of applicants The cohorts at Spiced Conclusion

Links:

LinkedIn: https://www.linkedin.com/in/gloria-quiceno/ Github: https://github.com/gdq12

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:

Jeff’s background Getting feedback to become a better teacher Going from engineering to teaching Jeff on becoming a curriculum writer Creating a curriculum that reinforces learning Jeff on starting his own data engineering bootcamp Shifting from teaching ML and data science to teaching data engineering Making sure that students get hired Screening bootcamp applicants Knowing when it’s time to apply for jobs The curriculum of JigsawLabs.io The market demand of Spark, Kafka, and Kubernetes (or lack thereof) Advice for data analysts that want to move into data engineering The market demand of ETL/ELT and DBT (or lack thereof) The importance of Python, SQL, and data modeling for data engineering roles Interview expectations How to get started in teaching The challenges of being a one-person company Teaching fundamentals vs the “shiny new stuff” JigsawLabs.io Finding Jeff online

Links: 

Jigsaw Labs: https://www.jigsawlabs.io/free Teaching my mom to code: https://www.youtube.com/watch?v=OfWwfTXGjBM Getting a Data Engineering Job Webinar with Jeff Katz: https://www.eventbrite.de/e/getting-a-data-engineering-job-tickets-310270877547

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: 

Jessica’s background Giving a talk at a tech conference about coffee Jessica’s transition into tech (How to get started) Going from learning to actually making money Landing your first job in tech Does your age matter when you’re trying to get a job? Challenges that Jessica faced in the beginning of her career Jessica’s role at PyLadies Fighting the Imposter Syndrome Generational differences in digital literacy and how to improve it Events organized by PyLadies Jessica’s beginnings at PyLadies (organizing events) Jessica’s experience with public speaking The impact of public speaking on your career Tips for public speaking Jessica’s work at Ecosia Discrimination in the tech industry (and in general) Finding Jessica online

Links:

Ecosia's website: https://www.ecosia.org/ Ecosia's blog: https://blog.ecosia.org/ecosia-financial-reports-tree-planting-receipts/ PyLadies Berlin: https://berlin.pyladies.com/ PyLadies' Meetup: https://meetup.com/PyLadies-Berlin Code Academy: https://www.codecademy.com/ Freecodecamp: https://www.freecodecamp.org/ Coursera Machine Learning: https://www.coursera.org/learn/machine-learning ML Bookcamp code: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp Google Summer code: https://summerofcode.withgoogle.com/ Outreachy website: https://www.outreachy.org/ Alumni Interview: https://railsgirlssummerofcode.org/blog/2020-03-17-alumni-interview-jessica Python pizza: https://python.pizza/ Pycon: https://pycon.it/en Pycon 2022: https://2022.pycon.de/

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

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

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

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

We talked about

Geo’s background Technical Product Manager Building ML platform Working on internal projects Prioritizing the backlog Defining the problems Observability metrics Avoiding jumping into “solution mode” Breaking down the problem Important skills for product managers The importance of a technical background Data Lead vs Staff Data Scientist vs Data PM Approvals and rollout Engineering/platform teams Data scientists’ role in the engineering team Scrum and Agile in data science Transitioning from Data Scientist to Technical PM Books to read for the transition Transitioning for non-technical people Doing user research Quality assurance in ML Advice for supporting an ML team as a Scrum master

Links:

Geo's LinkedIn: https://www.linkedin.com/in/geojolly/ Product School community: https://productschool.com/ http://theleanstartup.com/  Netflix CPO Medium blog: https://gibsonbiddle.medium.com/ Glovo is hiring: https://jobs.glovoapp.com/en/?d=4040726002

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

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

We talked about:

CJ’s background Evolutionary biology Learning machine learning Learning on the job and being honest with what you don’t know Convincing that you will be useful CJ’s first interview Transitioning to industry Tailoring your CV Data science courses Moving to Berlin Being selective vs ‘spray and pray’ Moving on to new jobs Plan for transitioning to industry Requirements for getting hired Publications, portfolios and pet projects Adjusting to industry Bad habits from academia Topics with long-term value CJ’s textbook

Links:

CJ's LinkedIn: https://www.linkedin.com/in/christina-jenkins/ Positions for master students: one two

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

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We talked about:

Eleni’s background Spatial data analytics Responsibilities of a postdoc Publishing papers Best places for data management papers Differences between postdoc and PhD Helping students become successful Research at the DIMA group Identifying important research directions Reviewing papers Underrated topics in data management Research in data cleaning Collaborating with others Choosing the field for Master’s students Choosing the topic for a Master thesis Should I do a PhD? Promoting computer science to female students

Links:

https://www.user.tu-berlin.de/tzirita/

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

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