We talked about:

Yury’s background Failing fast: Grammarly for science Not failing fast: Keyword recommender Four steps to epiphany Lesson learned when bringing XGBoost into production When data scientists try to be engineers Joining a fintech startup: Doing NLP with thousands of GPUs Working at a Telco company Having too much freedom The importance of digital presence Work-life balance Quantifying impact of failing projects on our CVs Business trips to Perm: don’t work on the weekend What doesn’t kill you makes you stronger

Links:

Yury's course: https://mlcourse.ai/ Yury's Twitter: https://twitter.com/ykashnitsky

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

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

We talked about:

Data-led academy Arpit’s background Growth marketing Being data-led Data-led vs data-driven Documenting your data: creating a tracking plan Understanding your data Tools for creating a tracking plan Data flow stages Tracking events — examples Collecting the data Storing and analyzing the data Data activation Tools for data collection Data warehouses Reverse ETL tools Customer data platforms Modern data stack for growth Buy vs build People we need to in the data flow Data democratization Motivating people to document data Product-led vs data-led

Links:

https://dataled.academy/

Join our Slack: https://datatalks.club/slack.html

We talked about:

Shawn’s background and his book Marketing ourselves Components of personal marketing Personal brand for an average developer Picking a domain: what to write about? Being too niche Finding a good niche Learning in public Borrowed platforms vs own platform Starting on social media: Picking what they put down Career transitioning: mutual exchange of value Personal marketing for getting a new job Getting hired through the back door Finding content ideas Marketing yourself in public — summary Open-source knowledge Internal marketing: promoting ourselves at work Signature initiative Public speaking Wrapping up Discount for the coding career book 75% of the engineering ladder criteria are not technical

Links:

Shawn's personal page: https://www.swyx.io/ Twitter: https://twitter.com/swyx Book of the week page: https://datatalks.club/books/20210510-the-coding-career-handbook.html (with a discount for DTC members!)

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

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

We talked about:

Tatiana’s background 12 career hacks and changing career Hack #1: Change your social circle Hack #2: Forget your fears and stereotypes Hack #3: Forget distractions Hack #4: Don’t overestimate others and don’t underestimate yourself Hack #5: Attention genius Hack #6: Make a team Hack #7: Less is more. Forget about perfectionism Hack #8: Initial creation Hack #9: Find mentors Hack #10: Say “no” Hack #11: Look for failures Hack #12: Take care of yourself Kaggle vs internships and pet projects Resources for learning machine learning Starting with Kaggle Improving focus Astroinformatics How background in Physics is helpful for transitioning Leaving academia Preparing for interviews

Links:

Mock interviews: https://www.pramp.com/ Learning ML: https://www.coursera.org/learn/machine-learning and https://www.coursera.org/specializations/deep-learning Python: https://www.coursera.org/learn/machine-learning-with-python  SQL: https://www.sqlhabit.com/  Practice: https://www.kaggle.com/ MIT 6.006: https://courses.csail.mit.edu/6.006/fall11/notes.shtml Coding: https://leetcode.com/ System design: https://www.educative.io/courses/grokking-the-system-design-interview Ukrainian telegram groups for interview preparation: https://t.me/FaangInterviewChannel,  https://t.me/FaangTechInterview, https://t.me/FloodInterview

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

We talked about:

Oleg’s background Standing out in recruitment process NextRound — a service for free mock interviews Why rejections are generic Starting NextRount — preparing a list of situations Steps in the interview process Read the job description! CV is your landing page Take-home assignments Questions about your past experience Hypothetical case questions Technical rounds Handling rejections What to do after receiving an offer? Do recruiters pay attention to age? Getting a job with a PhD — it’s a cold start problem Should I answer rejection emails? Negotiating when my salary is low Should I apply for jobs that require 5 years of experience? Tricking applicant tracking systems What else Oleg learned after interviewing 300 data scientists How a horse's ass determined the design of a space shuttle

Links:

Oleg's service for interviews: https://nextround.cc/ LinkedIn: https://www.linkedin.com/in/olegnovikov/

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

We talked about:

DataTalks.Club intro Lior’s background Who is a data strategist? Improving communication between business and tech Building trust Putting data and business people together Dealing with pushbacks Building things in the lean way (and growing tomatoes) Starting with ugly code Convincing others to take our code MVP vs development and Hummus Talking to people who can’t code Break down the silos Hummus Hummus places in Berlin Lior’s book: Data is Like a Plate of Hummus Data chaos

Links:

Book: https://www.amazon.com/-/en/Sarah-Mayor/dp/B086L277LZ (can be found on any amazon store) Company: https://www.taleaboutdata.com/ Podcast: https://podcast.whatthedatapodcast.com/ Linkedin: https://www.linkedin.com/in/liorbarak/ Twitter: https://twitter.com/liorb

Hummus places in Berlin:

Azzam: https://goo.gl/maps/uCkb3ATc5CVKapDa6 Akkawy: https://g.page/akkawy The Eatery Berlin: https://g.page/theeateryberlin

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

We covered:

Barr’s background Market gaps in data reliability Observability in engineering Data downtime Data quality problems and the five pillars of data observability Example: job failing because of a schema change Three pillars of observability (good pipelines and bad data) Observability vs monitoring Finding the root cause Who is accountable for data quality? (the RACI framework) Service level agreements Inferring the SLAs from the historical data Implementing data observability Data downtime maturity curve Monte carlo: data observability solution Open source tools Test-driven development for data Is data observability cloud agnostic? Centralizing data observability Detecting downstream and upstream data usage Getting bad data vs getting unusual data

Links:

Learn more about Monte Carlo: https://www.montecarlodata.com/ The Data Engineer's Guide to Root Cause Analysis: https://www.montecarlodata.com/the-data-engineers-guide-to-root-cause-analysis/ Why You Need to Set SLAs for Your Data Pipelines: https://www.montecarlodata.com/how-to-make-your-data-pipelines-more-reliable-with-slas/ Data Observability: The Next Frontier of Data Engineering: https://www.montecarlodata.com/data-observability-the-next-frontier-of-data-engineering/ To get in touch with Barr, ping her in the DataTalks.Club group or use [email protected]

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

We talked about:

Andrada’s background

Recommended courses Kaggle and StackOverflow Doing notebooks on Kaggle Projects for learning data science Finding a job and a mentor with Kaggle’s help The process for looking for a job Main difficulties of getting a job Project portfolio and Kaggle Helpful analytical skills for transitioning into data science Becoming better at coding Learning by imitating Is doing masters helpful? Getting into data science without a masters Kaggle is not just about competitions The last tip: use social media

Links:

https://www.kaggle.com/andradaolteanu  https://twitter.com/andradaolteanuu https://www.linkedin.com/in/andrada-olteanu-3806a2132/

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

We talked about:

Knesia’s background Data analytics vs data science Skills needed for data analytics and data science Benefits of getting a masters degree Useful online courses How project management background can be helpful for the career transition Which skills do PMs need to become data analysts? Going from working with spreadsheets to working with python Kaggle Productionizing machine learning models Getting experience while studying Looking for a job Gap between theory and practice Learning plan for transitioning Last tips and getting involved in projects

Links:

Notes prepared by Ksenia with all the info: https://www.notion.so/ksenialeg/DataTalks-Club-7597e55f476040a5921db58d48cf718f

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

We talked about:

Demetrious’ background and starting the MLOps community Growing MLOps community Community moderations and dealing with problems Becoming a community and connecting with people Feeling belonged Managing a community as an introvert Keeping communities active Doing custdev and talking to users Random coffee and meeting with community members Organizing community activities Is community a business? Five steps for starting a community in 2021 Shameless plug from Demetrious

Links:

https://mlops.community/

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

We talked about:

Lars’ career Doing DataOps before it existed What is DataOps Data platform Main components of the data platform and tools to implement it Books about functional programming principles Batch vs Streaming Maturity levels Building self-service tools MLOps vs DataOps Data Mesh Keeping track of transformations Lake house

Links:

https://www.scling.com/reading-list/ https://www.scling.com/presentations/

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

We talked about:

Ben’s background AI evangelism Ben’s first experiences speaking in public Becoming a great speaker  Key Takeaways and Call to Action Making a good introduction Being Remembered Writing a talk proposal for conferences Landing a keynote Good topics to start talks on Pitching a solution talk to meetup organizers Top public speaking skill to acquire Book recommendations

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

We discussed monetization roles and the capabilities people need to move into those roles.

The key roles are ML Researcher, ML Architect, and ML Product Manager.

We talked about:

Vin's career journey

What does it mean to "monetize machine learning" Important monetization metrics Who should we have on the team to make a project successful Machine Learning Researcher (applied and scientist) - background, responsibilities, and needed skills Developing new categories  The best recipe for a startup: angry users + data scientists What research actually is ML Product Manager - background, responsibilities, and needed skills How product managers can actually manage all their responsibilities (and they have a lot of them!) ML Architect - background, responsibilities, and needed skills Path to becoming an architect  How should we change education to make it more effective  Important product metrics

And more! 

Links:

https://twitter.com/v_vashishta​ https://linkedin.com/in/vineetvashishta​ https://databyvsquared.com/​

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

We talked about: 

Admond's career journey What is personal brand How Admond started being active online Publishing on medium and LinkedIn Idea generation process and tools Other platforms Podcasts Offline presence 1x1 meetings Speaking on conferences Having confidence to publish Selling online courses Personal values Admond's course

And many other things

Links:

https://twitter.com/admond1994 https://linkedin.com/in/admond1994 https://buzzsumo.com https://feedly.com/ https://lunchclub.com/ https://thelead.io/data-scientist-personal-brand-toolkit?utm_medium=instructor&utm_source=admond

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

Did you know that there are 3 types different types of data scientists? A for analyst, B for builder, and C for consultant - we discuss the key differences between each one and some learning strategies you can use to become A, B, or C.

We talked about:

Inspirations for memes  Danny's background and career journey The ABCs of data science - the story behind the idea Data scientist type A - Analyst  Skills, responsibilities, and background for type A Transitioning from data analytics to type A data scientist (that's the path Danny took) How can we become more curious? Data scientist B - Builder  Responsibilities and background for type B Transitioning from type A to type B Most important skills for type B Why you have to learn more about cloud  Data scientist type C - consultant Skills, responsibilities, and background for type C Growing into the C type Ideal data science team Important business metrics Getting a job - easier as type A or type B? Looking for a job without experience Two approaches for job search: "apply everywhere" and "apply nowhere" Are bootcamps useful? Learning path to becoming a data scientist Danny's data apprenticeship program and "Serious SQL" course  Why SQL is the most important skill R vs Python Importance of Masters and PhD

Links:

Danny's profile on LinkedIn: https://linkedin.com/in/datawithdanny Danny's course: https://datawithdanny.com/ Trailer: https://www.linkedin.com/posts/datawithdanny_datascientist-data-activity-6767988552811847680-GzUK/ Technical debt paper: https://proceedings.neurips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html

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

We talked about:

How we make decisions with machine learning What is decision optimization  Specifying the decision function Emulation for making the best decisions Decision optimization and reinforcement learning Getting started with decision optimization Trends in the industry

Links:

https://datatalks.club/people/danbecker.html https://www.decision.ai/​

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

We covered:

What is a feature store Problems it solves When to use a feature store  When not to use a feature store The main components When a team should start using a feature store 

Links:

Feast: https://feast.dev/ https://www.tecton.ai/blog/what-is-a-feature-store/  https://docs.greatexpectations.io/en/latest/reference/core_concepts.html

Join DataTalks.Club: https://datatalks.club​​​

We covered:

What is MLOps The difference between MLOps and ML Engineering Getting into MLOps Kubeflow and its components, ML Platforms Learning Kubeflow DataOps 

And other things

Links:

Microsoft MLOps maturity model: https://docs.microsoft.com/en-us/azure/architecture/example-scenario/mlops/mlops-maturity-model Google MLOps maturity levels: https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning MLOps roadmap 2020-2025: https://github.com/cdfoundation/sig-mlops/blob/master/roadmap/2020/MLOpsRoadmap2020.md Kubeflow website: https://www.kubeflow.org/ TFX Paper: https://research.google/pubs/pub46484/

Join DataTalks.Club: https://datatalks.club​​

We talked about 

open source getting started with open source convincing your employer to contribute to open source public speaking the checklist for open source projects the role of research advocate

And many more things!

Links from Vincent:

https://www.youtube.com/watch?v=68ABAU_V8qI&t=975s&ab_channel=PyData https://www.youtube.com/watch?v=kYMfE9u-lMo&t=958s&ab_channel=PyData https://koaning.io/projects.html https://calmcode.io/ https://makenames.io/ https://koaning.github.io/clumper/api/clumper.html

Join DataTalks.Club: https://datatalks.club​

We talked about development advocacy for data science.

We covered

The role of a developer advocate The skills needed for the job and the responsibilities How to become a developer advocate

You can find Elle on:

Twitter: https://twitter.com/DrElleOBrien LinkedIn: https://linkedin.com/in/drelleobrien DVC's youtube channel: https://www.youtube.com/channel/UC37rp97Go-xIX3aNFVHhXfQ

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

We talk about blogging technical writing. We cover:

Why should we write online? What should we write about? Writing at work: Design documents, wikis, etc. The writing process (also at work)

Eugene's website:  eugeneyan.com 

Follow Eugene on Twitter: https://twitter.com/eugeneyan

Suggest topics: https://eugeneyan.com/topic-poll/

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

We talked about:

The role of mentoring in career Looking for mentors and preparing for mentoring sessions as a mentee Becoming a mentor

And many other things! 

Links:

Rahul's profile on the mentoring club: https://www.mentoring-club.com/the-mentors/rahul-jain Rahul's article about mentoring: https://rahulj51.github.io/career/coaching/mentoring/2020/06/22/career-coaching.html

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

We covered:

Getting the recruiter's attention Making CV look great Tailoring your application to the position 

And many other things! 

Luke's LinkedIn profile: https://www.linkedin.com/in/lukewhipps/

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

We talked about: 

Dat's career so far and the startup he co-founded (Priceloop) Who to hire first in a data team How to hire the first data scientist

And many other things!

You can find Dat on LinkedIn: https://www.linkedin.com/in/dat-tran-a1602320/

Join DataTalksClub: https://datatalks.club

In this podcast, we talk about CRISP-DM - a methodology for organizing data science projects DataTalks.Club is the place to talk about data. Join our community: https://datatalks.club Read more about CRISP-DM here: https://mlbookcamp.com/article/crisp-dm