We talked about:

Carmine’s background Carmine’s startup FreshFlow Doing user research Design thinking Entrepreneur first Finding co-founders: the “expertise edges” framework The structure of the EF program Coming up with the idea How important is going through a startup accelerator? Finding your first client Finding investors Consequences of having a bad investor Splitting responsibilities between co-founders Hiring The importance of delegating Making work attractive to hires Plans for the future Just-in-time supply chain What would you have done differently? Advice for people starting a startup Don’t focus on skills only Getting motivation Am I ready for a startup? Importance of a business school Advice on finding a co-founder Do I need EF if I already have an idea? Having a prototype before the pitch

Books:

The Mom Test by Rob Fitzpatrick Design Thinking by Robert Curedale

Links:

FreshFlow: https://freshflow.ai/ Carmine's LinkedIn: https://www.linkedin.com/in/carminepaolino Carmine's Twitter: https://twitter.com/paolino

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

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

We don't have an episode lined up for this week, but we recorded a small chat with Vladimir some time ago. Enjoy it! 

We talked about:

Vladimir's background Learning by answering questions Don't be afraid of being wrong Winnings books Learning random things Approach learning as a machine learning project

Links:

Vladimir on LinkedIn: https://www.linkedin.com/in/vladimir-finkelshtein/

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

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

We talked about:

Lina’s background What we need to remember when starting a project (checklists) Make sure the problem is formalized and close to the core business Get the buy-in with stakeholders Building trust with stakeholders Don’t just focus on upsides – ask about concerns Turning a concert into a metric What happens when something goes wrong? Post mortem reporting Apply the 5 why’s If a lot of users say it’s a bug – it’s worth investigating Post mortem format Action points Debugging vs explaining the model Are there online versions of checklists? Make sure to log your inputs Talking to end-users and using your own service Your ideas vs Stakeholder ideas Should data practitioners educate the team about data? People skills and ‘dirty’ hacks Where to find Lina

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

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

We talked about:

Ben’s Background Building solutions for customers Why projects don’t make it to production Why do people choose overcomplicated solutions? The dangers of isolating data science from the business unit The importance of being able to explain things Maximizing chances of making into production The IKEA effect Risks of implementing novel algorithms If it can be done simply – do that first Don’t become the guinea pig for someone’s white paper The importance of stat skills and coding skills Structuring an agile team for ML work Timeboxing research Mentoring Ben’s book ‘Uncool techniques’ at AI-First companies Should managers learn data science? Do data scientists need to specialize to be successful?

Links:

Ben's book: https://www.manning.com/books/machine-learning-engineering-in-action (get 35% off with code "ctwsummer21")

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

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

We talked about:

Elena’s background Why do a startup instead of being an employee? Where to get ideas for your startup Finding a co-founder What should you consider before starting a startup? Vertical startup vs infrastructure startup ‘AI First’ startups Building tools for engineers What skills do you need to start a startup? Startup risks How to be prepared to fail Work-life balance The part-time startup approach Startup investment models No resources and no technical expertise – what to do? Productionizing your services When to hire an expert Talking to people with a problem before solving the problem Starting Elena’s startup, Evidently Elena’s role at Evidently Why is Evidently open source? “People will just copy my open source code. Should I be concerned?” Bottom-up adoption Creating value so that clients engage with your product Is there a difference between countries when creating a startup? Does open source mean the data is safer? When should you hire engineers? Following the market Startups out of genuine interest vs Just for money and for fun

Links:

EvidentlyAI: https://evidentlyai.com/ Elena's LinkedIn: https://www.linkedin.com/in/elenasamuylova/ Elena's Twitter: https://twitter.com/elenasamuylova/

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

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

Links:

Twitter: https://twitter.com/dead_flowers22 LinkedIn: https://www.linkedin.com/in/roksolanadiachuk/

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

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

We talked about:

Andreas’s background Why data engineering is becoming more popular Who to hire first – a data engineer or a data scientist? How can I, as a data scientist, learn to build pipelines? Don’t use too many tools What is a data pipeline and why do we need it? What is ingestion? Can just one person build a data pipeline? Approaches to building data pipelines for data scientists Processing frameworks Common setup for data pipelines — car price prediction Productionizing the model with the help of a data pipeline Scheduling Orchestration Start simple Learning DevOps to implement data pipelines How to choose the right tool Are Hadoop, Docker, Cloud necessary for a first job/internship? Is Hadoop still relevant or necessary? Data engineering academy How to pick up Cloud skills Avoid huge datasets when learning Convincing your employer to do data science How to find Andreas

Links:

LinkedIn: https://www.linkedin.com/in/andreas-kretz Data engieering cookbook: https://cookbook.learndataengineering.com/ Course: https://learndataengineering.com/

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

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

We talked about:

Santiago’s background “Transitioning to ML” vs “Adding ML as a skill” Getting over the fear of math for software developers Learning by explaining Seven lessons I learned about starting a career in machine learning Lesson 1 – Take the first step Lesson 2 – Learning is a marathon, not a sprint Lesson 3 – If you want to go quickly, go alone. If you want to go far, go together. Lesson 4 – Do something with the knowledge you gain Lesson 5 – ML is not just math. Math is not scary. Lesson 6 – Your ability to analyze a problem is the most important skill. Coding is secondary. Lesson 7 – You don’t need to know every detail Tools and frameworks needed to transition to machine learning Problem-based learning vs Top-down learning Learning resources Santiago’s favorite books Santiago’s course on transitioning to machine learning Improving coding skills Building solutions without machine learning Becoming a better engineer What is the difference between machine learning and data science? Getting into machine learning - Reiteration Getting past the math

Links:

Santiago's Twitter: https://twitter.com/svpino Santiago's course: https://gumroad.com/svpino#kBjbC Pinned tweet with a roadmap: https://twitter.com/svpino/status/1400798154732212230

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

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

Links:

https://www.notion.so/Analytics-Engineer-New-Role-in-a-Data-Team-9decbf33825c4580967cf3173eb77177 https://www.linkedin.com/in/victoriaperezmola/

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

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

Conference: https://datatalks.club/conferences/2021-summer-marathon.html

We talked about:

Jessi’s background Uri’s background Data governance Implementing data governance: policies and processes Reasons not to have data governance Start with “why” Cataloging and classifying our data Let data work for you The human component Data quality Defining policies Implementing policies Shopping-card experience for requesting data Proving the value of data catalog Using data catalog Data governance = data catalog?

Links:

Book: https://www.oreilly.com/library/view/data-governance-the/9781492063483/ Jessi’s LinkedIn: https://www.linkedin.com/in/jashdown/ Uri’s LinkedIn: https://linkedin.com/in/ugilad Uri’s Twitter: https://twitter.com/ugilad

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

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

Conference: https://datatalks.club/conferences/2021-summer-marathon.html

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