DataTalks.Club

We talked about: Audience Poll Andrey’s background What data science practice is Best DS practice in a traditional company vs IT-centric companies Getting started with building data science practice (finding out who you report to) Who the initiative comes from Finding out what kind of problems you will be solving (Centralized approach) Moving to a semi-decentralized approach Resources to learn about data science practice Pivoting from the role of a software engineer to data scientist The most impactful realization from data science practice Advice for individual growth Finding Andrey online Links: Data Teams book: https://www.amazon.com/Data-Teams-Management-Successful-Data-Focused/dp/1484262271/ ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Have a great weekend!

We talked about: David’s background A day in the life of a professor David’s current projects Starting a school The different types of professors David’s recent papers Similarities and differences between research labs and startups Finding (or creating) good datasets David’s lab Balancing research and teaching as a professor David’s most rewarding research project David’s most underrated research project David’s virtual data science seminars on YouTube Teaching at universities without doing research Staying up-to-date in research David’s favorite conferences Selecting topics for research Convincing students to stay in academia and competing with industry Finding David online Links:  David A. Bader: https://davidbader.net/ NJIT Institute for Data Science: https://datascience.njit.edu/ Arkouda: https://github.com/Bears-R-Us/arkouda NJIT Data Science YouTube Channel: https://www.youtube.com/c/NJITInstituteforDataScience ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Christiaan’s background Usual ways of collecting and curating data Getting the buy-in from experts and executives Starting an annotation booklet Pre-labeling Dataset collection Human level baseline and feedback Using the annotation booklet to boost annotation productivity Putting yourself in the shoes of annotators (and measuring performance) Active learning Distance supervision Weak labeling Dataset collection in career positioning and project portfolios IPython widgets GDPR compliance and non-English NLP Finding Christiaan online Links: My personal blog: https://useml.net/ Comtura, my company: https://comtura.ai/ LI: https://www.linkedin.com/in/christiaan-swart-51a68967/ Twitter: https://twitter.com/swartchris8/ ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Zhamak’s background What is Data Mesh? Domain ownership Determining what to optimize for with Data Mesh Decentralization Data as a product Self-serve data platforms Data governance Understanding Data Mesh Adopting Data Mesh Resources on implementing Data Mesh Links: Free 30-day code from O'Reilly: https://learning.oreilly.com/get-learning/?code=DATATALKS22 Data Mesh book: https://learning.oreilly.com/library/view/data-mesh/9781492092384/ LinkedIn: https://www.linkedin.com/in/zhamak-dehghani ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Mehdi’s background The difference between startup, scale-up and enterprise Hypergrowth Data platform engineers in a scale-up environment What a data platform is and who builds it Managing the fast pace of a scale-up while ensuring personal growth Should a senior data person consider a scale-up or an enterprise? Should a junior data person consider a scale-up or an enterprise? Sourcing talent for hyper-growth companies and developing a community culture Generating content and getting feedback Generalization vs specialization for data engineers in a scale-up The ratio of work between platform building and use case pipelines Being proactive in order to progress to mid or senior level Caps and bass guitars MehdiO DataTV and DataCreators.Club (Mehdi’s YouTube Channel and podcast) Links: Mehdi's YouTube channel: https://www.youtube.com/channel/UCiZxJB0xWfPBE2omVZeWPpQ Mehdi's Linkedin:  https://linkedin.com/in/mehd-io/ Mehdi's Medium Blog: https://medium.com/@mehdio Mehdi's data creators club: https://datacreators.club/ ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Alexander’s background The role of Partner at Königsweg Being part of the data and AI community How Alexander became chair at PyData Alexander’s many talks and advice on giving them Explaining AI to managers Why being able to explain machine learning to managers is important The experimentational nature of AI and why it’s not a cure-all Innovation requires patience Convincing managers not to use AI or ML when there are better (simpler) solutions The role of MLOps in enterprises Thinking about the mid- and long-term when considering solutions Finding Alexander online Links:  Alexander's Twitter: https://twitter.com/hendorf Alexander's LinkedIn: https://www.linkedin.com/in/hendorf/ Königsweg: https://www.koenigsweg.com PyData Südwest: https://www.meetup.com/pydata-suedwest/ PyData Frankfurt: https://www.meetup.com/pydata-frankfurt/ PyConDE & PyData Berlin: https://pycon.de ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Danny’s background What an MLOps Architect does The popularity of MLOps Architect as a role Convincing an employer that you can wear many different hats Interviewing for the role of an MLOps Architect How Danny prioritizes work with data scientists Coming to WhyLabs when you’ve already got something in production vs nothing in production Market awareness regarding the importance of model monitoring How Danny (WhyLabs) chooses tools ONNX Common trends in tooling setups The most rewarding thing for Danny in ML and data science Danny’s secret for staying sane while wearing so many different hats T-shaped specialist, E-shaped specialist, and the horizontal line The importance of background for the role of an MLOps Architect Key differences for WhyLogs free vs paid Conclusion and where to find Danny online Links: Matt Turck: https://mattturck.com/data2021/ AI Observability Platform: https://whylabs.ai/observability Danny's LinkedIn: https://www.linkedin.com/in/dleybz/ Whylabs' website: https://whylabs.ai/ AI Infrastructure Alliance: https://ai-infrastructure.org/ ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: DataTalks.Club intro Tereza’s background Working as a coach Identifying the mismatches between your needs and that of a company How to avoid misalignments Considering what’s mentioned in the job description, what isn’t, and why Diversity and culture of a company Lack of a salary in the job description Way of doing research about the company where you will potentially work How to avoid a mismatch with a company other than learning from your mistakes Before data, during data, after data (a company’s data maturity level) The company’s tech stack Finding Tereza online Links:  Decoding Data Science Job Descriptions (talk): https://www.youtube.com/watch?v=WAs9vSNTza8 Talk at ConnectForward: https://www.youtube.com/watch?v=WAs9vSNTza8 Slides: https://www.slideshare.net/terezaif/decoding-data-science-job-descriptions-250687704 Talk at DataLift: https://www.youtube.com/watch?v=pCtQ0szJiLA Slides: https://www.slideshare.net/terezaif/lessons-learned-from-hiring-and-retaining-data-practitioners 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: Christine’s Background Private sector vs Public sector Public policy The challenges of being a community organizer How public policy relates to political science Programs that teach data science for public policy Data science for public policy vs regular data science The importance of ethical data science in public policy How data science in social impact project differs from other projects Other resources to learn about data science for public policy Challenges with getting data in data science for public policy The problems with accessing public datasets about recycling Christine’s potential projects after Master’s degree Gender inequality in STEM fields Corporate responsibility and why organizations need social impact data scientists What you need to start making a social impact with data science 80,000 hours Other use cases for public policy data science Coffee, Ethics & AI Finding Christine online Links: Explore some Data Science for Social Good projects: http://www.dssgfellowship.org/projects/ Bi-weekly Ethics in AI Coffee Chat: https://www.meetup.com/coffee-ethics-ai/ Make a Social Impact with your Job: https://tinyurl.com/80khours Course in Data Ethics: https://ethics.fast.ai/ Data Science for Social Good Berlin: https://dssg-berlin.org/ CorrelAid: https://correlaid.org/ DataKind: https://www.datakind.org/ Christine's LinkedIn: https://www.linkedin.com/in/christinecepelak/ Christine's Twitter: https://twitter.com/CLcep  MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html