Shows
DataTalks.ClubHow to Rebuild Data Trust? Mindful Data Strategy and Maintenance vs Innovation - Lior BarakStruggling with data trust issues, dashboard drama, or constant pipeline firefighting? In this deep‑dive interview, Lior Barak shows you how to shift from a reactive “fix‑it” culture to a mindful, impact‑driven practice rooted in Zen/Wabi‑Sabi principles.You’ll learn:Why 97 % of CEOs say they use data, but only 24 % call themselves data‑drivenThe traffic‑light dashboard pattern (green / yellow / red) that instantly tells execs whether numbers are safe to useA practical rule for balancing maintenance, rollout, and innovation—and avoiding team burnoutHow to quantify ROI on data...
2025-08-151h 01DataTalks.ClubLinguistics and Fairness - Tamara AtanasoskaIn this podcast episode, we talked with Tamara Atanasoska about building fair AI systems.About the Speaker:Tamara works on ML explainability, interpretability and fairness as Open Source Software Engineer at probable. She is a maintainer of fairlearn, contributor to scikit-learn and skops. Tamara has both computer science/ software engineering and a computational linguistics(NLP) background.During the event, the guest discussed their career journey from software engineering to open-source contributions, focusing on explainability in AI through Scikit-learn and Fairlearn. They explored fairness in AI, including challenges in credit loans, hiring, and decision-making, and emph...
2025-01-1753 minDataTalks.ClubDataTalks.Club 4th Anniversary AMA Podcast – Alexey Grigorev and Johanna BayerWe talked about:
00:00 DataTalks.Club intro
00:00 DataTalks.Club anniversary "Ask Me Anything" event with Alexey Grigorev
02:29 The founding of DataTalks .Club
03:52 Alexey's transition from Java work to DataTalks.Club
04:58 Growth and success of DataTalks.Club courses
12:04 Motivation behind creating a free-to-learn community
24:03 Staying updated in data science through pet projects
26 :37 Hosting a second podcast and maintaining programming skills
28:56 Skepticism about LLMs and their relevance
31:53 Transitioning to DataTalks.Club and personal reflections
33:32 Memorable moments and the first event's...
2024-10-2653 minDataTalks.ClubWorking as a Core Developer in the Scikit-Learn Universe - Guillaume Lemaître
In this podcast episode, we talked with Guillaume Lemaître about navigating scikit-learn and imbalanced-learn.
🔗 CONNECT WITH Guillaume Lemaître
LinkedIn - https://www.linkedin.com/in/guillaume-lemaitre-b9404939/
Twitter - https://x.com/glemaitre58
Github - https://github.com/glemaitre
Website - https://glemaitre.github.io/
🔗 CONNECT WITH DataTalksClub
Join the community - https://datatalks-club.slack.com/join/shared_invite/zt-2hu0sjeic-ESN7uHt~aVWc8tD3PefSlA#/shared-invite/email
Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/u/0/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmN...
2024-07-2652 minDataTalks.ClubBerlin Buzzwords 2024We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links.
You can access all the podcast episodes here - https://datatalks.club/podcast.html
📚Check our free online courses
ML Engineering course - http://mlzoomcamp.com
Data Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp
MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp
Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp
LLM course - https://github.com/DataTalksClub/llm-zoomcamp
Read about all our courses in one place - https://datatalks.club/blog/gui...
2024-07-0637 minDataTalks.ClubBuilding Production Search Systems - Daniel SvonavaLinks:
VectorHub: https://superlinked.com/vectorhub/?utm_source=community&utm_medium=podcast&utm_campaign=datatalks
Daniel's LinkedIn: https://www.linkedin.com/in/svonava/
Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
This podcast is sponsored by VectorHub, a free open-source learning community for all things vector embeddings and information retrieval systems.
2024-03-2258 minDataTalks.ClubStock Market Analysis with Python and Machine Learning - Ivan BrigidaWe talked about:
Ivan’s background
How Ivan became interested in investing
Getting financial data to run simulations
Open, High, Low, Close, Volume
Risk management strategy
Testing your trading strategies
Sticking to your strategy
Important metrics and remembering about trading fees
Important features
Deployment
How DataTalks.Club courses helped Ivan
Ivan’s site and course sign-up
Links:
Exploring Finance APIs: https://pythoninvest.com/long-read/exploring-finance-apis
Python Invest Blog Articles: https://pythoninvest.com/blog
Free ML Engineering course: http://mlzoomcamp.com
Join DataTalks.Club: https://datatalks.club/slack.html
Our even...
2024-01-2455 minDataTalks.ClubDataTalks.Club Anniversary Interview - Alexey Grigorev, Johanna BayerFree ML Engineering course: http://mlzoomcamp.com
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
2023-10-1257 minDataTalks.ClubLessons Learned from Freelancing and Working in a Start-up - Antonis StellasWe talked about;
Antonis' background
The pros and cons of working for a startup
Useful skills for working at a startup and the Lean way to work
How Antonis joined the DataTalks.Club community
Suggestions for students joining the MLOps course
Antonis contributing to Evidently AI
How Antonis started freelancing
Getting your first clients on Upwork
Pricing your work as a freelancer
The process after getting approved by a client
Wearing many hats as a freelancer and while working at a startup
Other suggestions for getting clients as a freelancer
Antonis' thoughts on the Data Engineering course
Antonis...
2023-06-0950 minDataTalks.ClubAccelerating the Adoption of AI through Diversity - Dânia MeiraWe talked about:
Dania’s background
Founding the AI Guild
Datalift Summit
Coming up with meetup topics
Diversity in Berlin
Other types of diversity besides gender
The pitfalls of lacking diversity
Creating an environment where people can safely share their experiences
How the AI Guild helps organizations become more diverse
How the AI guild finds women in the fields of AI and data science
Advice for people in underrepresented groups
Organizing a welcoming environment and creating a code of conduct
AI Guild’s consulting work and community
AI Guild team
Dania’s resource recommendations
Upcoming Datalift Summit
...
2023-02-2457 minDataTalks.ClubStaff AI Engineer - Tatiana GabrusevaWe talked about:
Tatiana’s background
Going from academia to healthcare to the tech industry
What staff engineers do
Transferring skills from academia to industry and learning new ones
The importance of having mentors
Skipping junior and mid-level straight into the staff role
Convincing employers that you can take on a lead role
Seeing failure as a learning opportunity
Preparing for coding interviews
Preparing for behavioral and system design interviews
The importance of having a network and doing mock interviews
How much do staff engineers work with building pipelines, data science, ETC, MPOps, etc.?
Context switching
Advice for th...
2023-02-1755 minDataTalks.ClubNavigating Career Changes in Machine Learning - Chris SzafranekWe talked about
Chris’s background
Switching careers multiple times
Freedom at companies
Chris’s role as an internal consultant
Chris’s sabbatical
ChatGPT
How being a generalist helped Chris in his career
The cons of being a generalist and the importance of T-shaped expertise
The importance of learning things you’re interested in
Tips to enjoy learning new things
Recruiting generalists
The job market for generalists vs for specialists
Narrowing down your interests
Chris’s book recommendations
Links:
Lex Fridman: science, philosophy, media, AI (especially earlier episodes): https://www.youtube.com/lexfridman
Andrej Kar...
2023-02-0355 minDataTalks.ClubPreparing for a Data Science Interview - Luke WhippsWe talked about:
Luke’s background
Luke’s podcast - AI Game Changers
How Luke helps people get jobs
What’s changed in the recruitment market over the last 6 months
Getting ready for the interview process
Stage “zero” – the filter between the candidate and the company
Preparing for the introduction stage – research and communication
Reviewing the fundamentals during preparation
Preparing for the technical part of the interview
Establishing the hiring company’s expectations
Depth vs breadth
Overly theoretical and mathematical questions in interviews
Bombing (failing) in the middle of an interview
Applying to different roles within the same company
Luke’s...
2023-01-2754 minDataTalks.ClubIndie Hacking - Pauline ClavellouxWe talked about:
Pauline’s background
Pauline’s work as a manager at IBM
What is indie hacking?
Pauline initial indie hacking projects
Getting ready for launch
Responsibilities and challenges in indie hacking
Pauline’s latest indie hacking project
Going live and marketing
Challenges with Unreal Me
Staying motivated with indie hacking projects
Skills Pauline picked up while doing indie hacking projects
Balancing a day job and indie hacking
Micro SaaS and AboutStartup.io
How Pauline comes up with ideas for projects
Going from an idea on paper to building a project
Pauline’s Twitter success
Connecting with Pauline...
2023-01-2051 minDataTalks.ClubDoing Software Engineering in Academia - Johanna BayerWe talked about:
Johanna’s background
Open science course and reproducible papers
Research software engineering
Convincing a professor to work on software instead of papers
The importance of reproducible analysis
Why academia is behind on software engineering
The problems with open science publishing in academia
The importance of standard coding practices
How Johanna got into research software engineering
Effective ways of learning software engineering skills
Providing data and analysis for your project
Johanna’s initial experience with software engineering in a project
Working with sensitive data and the nuances of publishing it
How often Johanna does hackathons, open sour...
2023-01-1349 minDataTalks.ClubData-Centric AI - Marysia WinkelsWe talked about:
Marysia’s background
What data-centric AI is
Data-centric Kaggle competitions
The mindset shift to data-centric AI
Data-centric does not mean you should not iterate on models
How to implement the data-centric approach
Focusing on the data vs focusing on the model
Resources to help implement the data-centric approach
Data-centric AI vs standard data cleaning
Making sure your data is representative
Knowing when your data is good enough
The importance of user feedback
“Shadow Mode” deployment
What to do if you have a lot of bad data or incomplete data
Marysia’s role at PyData
How Marysia...
2023-01-0653 minDataTalks.ClubFrom Software Engineer to Data Science Manager - Sadat AnwarWe talked about:
Sadat’s background
Sadat’s backend engineering experience
Sadat’s pivot point as a backend engineer
Sadat’s exposure to ML and Data Science
Sadat’s Act Before you Think approach (with safety nets)
Sadat’s street cred and transition into management
The hiring process as an internal candidate
The importance of people management skills
The Brag List
The most difficult part of transitioning to management
Focusing on projects and setting milestones
Sadat’s transition from EM to data science management
How much domain knowledge is needed for management?
The main difference between engineering and management
H...
2022-12-0952 minDataTalks.ClubTeaching and Mentoring in Data Analytics - Irina BrudaruWe talked about:
Irina’s background
Irina as a mentor
Designing curriculum and program management at AI Guild
Other things Irina taught at AI Guild
Why Irina likes teaching
Students’ reluctance to learn cloud
Irina as a manager
Cohort analysis in a nutshell
How Irina started teaching formally
Irina’s diversity project in the works
How DataTalks.Club can attract more female students to the Zoomcamps
How to get technical feedback at work
Antipatterns and overrated/overhyped topics in data analytics
Advice for young women who want to get into data science/engineering
Finding Irina online
Fundamentals for data a...
2022-12-0253 minDataTalks.ClubTechnical Writing and Data Journalism - Angelica Lo DucaWe talked about:
Angelica’s background
Angelica’s books
Data journalism
How Angelica got into data journalism
The field of digital humanities and Angelica’s data journalism course
Technical articles vs data journalism articles
Transforming reports into data storytelling
Are reports to stakeholders considered technical writing?
Data visualization in articles
Article length
The process of writing an article
Finding writing topics
How Angelica got into writing a book (communication with publishers)
The process for writing a book
Brainstorming
Reviews and revisions
Conclusion
Links:
Data Journalism examples (FENCED OUT): https://www.washingtonpost.com/graphics/world...
2022-11-2550 minDataTalks.ClubFrom Digital Marketing to Analytics Engineering - Nikola MaksimovicWe talked about:
Nikola’s background
Making the first steps towards a transition to BI and Analytics Engineering
Learning the skills necessary to transition to Analytics Engineering
The in-between period – from Marketing to Analytics Engineering
Nikola’s current responsibilities
Understanding what a Data Model is
Tools needed to work as an Analytics Engineer
The Analytics Engineering role over time
The importance of DBT for Analytics Engineers
Where can one learn about data modeling theory?
Going from Ancient Greek and Latin to understanding Data (Just-In-Time Learning)
The importance of having domain knowledge to analytics engineering
Suggestion for those wishing to tra...
2022-11-1846 minDataTalks.ClubProduct Owners in Data Science - Anna HannemannWe talked about:
About Anna and METRO
Anna’s background
The importance of a technical background for data product owners
What are product owners?
Product owners vs product managers
Anna’s work on recommender systems at METRO
Expanding the data team
Types of algorithms used for recommender systems
What kind of knowledge and skills data product owners need to have
Problems and ideas should come from the business
How Anna handles all her responsibilities
The process for starting work on new domains
Product portfolio management
ProductTank and Anna’s role in it
Anna’s resource recommendations
Li...
2022-11-1154 minDataTalks.ClubBuilding Data Science Practice - Andrey ShtylenkoWe 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 Tea...
2022-11-0449 minDataTalks.ClubLarge-Scale Entity Resolution - Sonal GoyalWe talked about:
Sonal’s background
How the idea for Zingg came about
What Zingg is
The difference between entity resolution and identity resolution
How duplicate detection relates to entity resolution
How Sonal decided to start working on Zingg
How Zingg works
What Zingg runs on
Switching from consultancy to working on a new open source solution
Why Zingg is open source
Open source licensing
Working on Zingg initially vs now
Zingg’s current and future team
Sonal’s biggest current challenge
Avoiding problems with entity/identity resolution through database design
Identity resolution vs basic joins, data fusion...
2022-10-2853 minDataTalks.ClubFrom Data Science to DataOps - Tomasz HincWe talked about:
Tomasz’s background
What Tomasz did before DataOps (Data Science)
Why Tomasz made the transition from Data science to DataOps
What is DataOps?
How is DataOps related to infrastructure?
How Tomasz learned the skills necessary to become DataOps
Becoming comfortable with terminal
The overlap between DataOps and Data Engineering
Suitable/useful skills for DataOps
Minimal operational skills for DataOps
Similarities between DataOps and Data Science Managers
Tomasz’s interesting projects
Confidence in results and avoiding going too deep with edge cases
Conclusion
Links:
Terminal setup video, 19 minutes long: https://www.yout...
2022-10-2151 minDataTalks.ClubData Science Career Development - Katie BauerWe talked about:
Katie’s background
What is a data scientist?
What is a data science manager?
Quality of the craft
How data leaders promote career growth
Supporting senior data professionals
Choosing the IC route vs the management route
Managing junior data professionals
Talking to senior stakeholders and PMs as a junior
The importance of hiring juniors
What skills do data scientist managers need to get hired?
How juniors that are just starting out can set themselves apart from the competition
Asking senior colleagues for help and the rubber duck channel
The challenges of the head of data
Co...
2022-10-1453 minDataTalks.ClubFrom Testing Phones to Managing NLP Projects - Alvaro Navas PeireWe talked about:
Alvaro’s background
Working as a QA (Quality Assurance) engineer
Transitioning from QA to Machine Learning
Gathering knowledge about ML field
Searching for an ML job (improving soft skills and CV)
Data science interview skills
Zoomcamp projects
Zoomcamp project deployment
How to not undersell yourself during interviews
Alvaro’s experience with interviews during his transition
Alvaro’s Zoomcamp notes
Alvaro’s coach
The importance of mathematical knowledge to a transition into ML
Preparing for technical interviews
Alvaro’s typical workday
Alvaro’s team’s tech stack
The importance of a technical background to transitioning into ML
2022-10-0748 minDataTalks.ClubResponsible and Explainable AI - Supreet KaurWe talked about:
Supreet’s background
Responsible AI
Example of explainable AI
Responsible AI vs explainable AI
Explainable AI tools and frameworks (glass box approach)
Checking for bias in data and handling personal data
Understanding whether your company needs certain type of data
Data quality checks and automation
Responsibility vs profitability
The human touch in AI
The trade-off between model complexity and explainability
Is completely automated AI out of the question?
Detecting model drift and overfitting
How Supreet became interested in explainable AI
Trustworthy AI
Reliability vs fairness
Bias indicators
The future of explainable AI
About DataBuzz
The di...
2022-09-3053 minDataTalks.ClubBuilding Data Science Practice - Andrey ShtylenkoWe 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://ww...
2022-09-3049 minDataTalks.ClubLeading Data Research - David BaderWe 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 i...
2022-09-1658 minDataTalks.ClubDataset Creation and Curation - Christiaan SwartWe 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/
Tw...
2022-09-0956 minDataTalks.ClubData Mesh 101 - Zhamak DehghaniWe 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/sl...
2022-09-0254 minDataTalks.ClubGrowing Data Engineering Team in a Scale-Up - Mehdi OUAZZAWe 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
Be...
2022-08-2653 minDataTalks.ClubLessons Learned About Data & AI at Enterprises - Alexander HendorfWe 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
2022-08-1954 minDataTalks.ClubMLOps Architect - Danny LeybzonWe 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 d...
2022-08-1253 minDataTalks.ClubDecoding Data Science Job Descriptions - Tereza IofciuWe 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 Te...
2022-08-0549 minDataTalks.ClubData Science for Social Impact - Christine CepelakWe 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 degr...
2022-07-2948 minDataTalks.ClubHiring Data Science Talent - Olga IvinaWe talked about:
Olga’s career journey
Hiring data scientists now vs 7 years ago
The two qualities of an excellent data scientist
What makes Alexey do this podcast
How Alexey get the latest information on data science
How Olga checks a candidate’s technical skills
How to make an answer stand out (showing your depth of knowledge)
A strong mathematical background vs a strong engineering background
When Auto ML will replace the need to have data scientists
Should data scientists transition into management? (the importance of communication in an organization)
Switching from a data analyst role to a data...
2022-07-2252 min
DataTalks.ClubFrom Open-Source Maintainer to Founder - Will McGuganWe talked about:
Will’s background
Will’s open source projects
S3Fs and PyFile systems
Inspiration for open source projects
Will as a freelancer
Starting a company from a tweet (Rich and Textual)
Building in public (Will’s approach to social media)
The workforce and roadmap of Textualize.io
The importance of working on open source for Textualize employees
The workflow of and contributions to Textualize
Getting your first thousand GitHub Stars (going viral)
Suggestions for those who wish to start in the open-source space
Finding Will online
Links:
Twitter: https://twitter.com/will...
2022-07-1549 minDataTalks.ClubDesigning a Data Science Organization - Lisa CohenWe talked about:
Lisa’s background
Centralized org vs decentralized org
Hybrid org (centralized/decentralized)
Reporting your results in a data organization
Planning in a data organization
Having all the moving parts work towards the same goals
Which approach Twitter follows (centralized vs decentralized)
Pros and cons of a decentralized approach
Pros and cons of a centralized approach
Finding a common language with all the functions of an org
Finding the right approach for companies that want to implement data science
How many data scientists does a company need?
Who do data scientists report huge findings to?
The im...
2022-07-0851 minDataTalks.ClubDeveloper Advocacy Engineer for Open-Source - Merve NoyanWe talked about:
Merve’s background
Merve’s first contributions to open source
What Merve currently does at Hugging Face (Hub, Spaces)
What is means to be a developer advocacy engineer at Hugging Face
The best way to get open source experience (Google Summer of Code, Hacktoberfest, and sprints)
The peculiarities of hiring as it relates to code contributions
Best resources to learn about NLP besides Hugging Face
Good first projects for NLP
The most important topics in NLP right now
NLP ML Engineer vs NLP Data Scientist
Project recommendations and other advice to catch the eye of recr...
2022-07-0150 minDataTalks.ClubData Scientists at Work - Mısra TurpWe talked about:
Misra’s background
What data scientists do
Consultant data scientists vs in-house data scientists (and freelancers)
Expectations for data scientists
The importance of keeping up to date with AI developments (FOMA)
How does DALL·E 2 work and should you care?
Going to conferences to stay up to date
The most pressing issue for data scientists
Fighting FOMA and imposter syndrome
Knowing when you have enough knowledge of a framework
The “best” type of data scientist
Being a generalist vs a specialist
Advice for entry-level data entering an oversaturated market
Catching the eye of big AI compani...
2022-06-2458 minDataTalks.ClubFreelancing and Consulting with Data Engineering - Adrian BrudaruWe talked about:
Adrian’s background
Freelancing vs Employment
Risk and occupancy rate in freelancing
The scariest part of freelancing
Adrian’s first projects
Freelancing 5 years later
Pay rates in freelancing
Acquiring skills while freelancing
Working with recruitment agencies and networking
Looking for projects and getting clients
Freelancing vs consulting
Clarity in clients’ expectations (scope of work)
Building your network
Freelancing platforms
Adrian’s data loading prototype
Going from freelancing to making your own product (and other investments)
The usefulness of a portfolio
Introverts in freelancing
Is it possible to work for 3 months a year in freelancing?
Choosing projects...
2022-06-1752 minDataTalks.ClubGetting a Data Engineering Job (Summary and Q&A) - Jeff KatzWe talked about:
Summary of “Getting a Data Engineering Job” webinar
Python and engineering skills
Interview process
Behavioral interviews
Technical interviews
Learning Python and SQL from scratch
Is having non-coding experience a disadvantage?
Analyst or engineer?
Do you need certificates?
Do I need a master’s degree?
Fully remote data engineering jobs
Should I include teaching on my resume?
Object-oriented programming for data engineering
Python vs Java/Scala
SQL and Python technical interview questions
GCP certificates
Is commercial experience really necessary?
From sales to engineering
Solution engineers
Wrapping up
Links:
Getting a Data Enginee...
2022-06-1048 minDataTalks.ClubUsing Data for Asteroid Mining - Daynan CrullWe talked about:
Daynan’s background
Astronomy vs cosmology
Applications of data science and machine learning in astronomy
Determining signal vs noise
What the data looks like in astronomy
Determining the features of an object in space
Ground truth for space objects
Why water is an important resource in the space economy
Other useful resources that can be found in asteroids
Sources of asteroids
The data team at an asteroid mining company
Open datasets for hobbyists
Mission and hardware design for asteroid mining
Partnerships and hires
Links:
LinkedIn: https://www.linkedin.com/in/day...
2022-06-0353 minDataTalks.ClubMachine Learning in Marketing - Juan OrduzWe 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 know...
2022-05-2752 minDataTalks.ClubFrom Academia to Data Analytics and Engineering - Gloria QuicenoWe 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 appli...
2022-05-2048 minDataTalks.ClubTeaching Data Engineers - Jeff KatzWe 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...
2022-05-1352 minDataTalks.ClubFrom Roasting Coffee to Backend Development - Jessica GreeneWe 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 speaki...
2022-05-0652 minDataTalks.ClubRecruiting Data Engineers - Nicolas RassamWe 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 inte...
2022-04-2949 minDataTalks.ClubStorytime for DataOps - Christopher BerghWe 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: htt...
2022-04-2252 minDataTalks.ClubMachine Learning and Personalization in Healthcare - Stefan GudmundssonWe 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-d...
2022-04-1551 minDataTalks.ClubInnovation and Design for Machine Learning - Liesbeth DingemansWe 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 i...
2022-04-0855 minDataTalks.ClubHacking Your Data Career - Marijn MarkusWe 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/marijnma...
2022-04-0155 minDataTalks.ClubVisualising Machine Learning - Meor AmerWe 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=azh...
2022-03-2552 minDataTalks.ClubFrom Math Teacher to Analytics Engineer - Juan PabloWe 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: ht...
2022-03-1850 minDataTalks.ClubFrom Data Science to Data Engineering - Ellen KönigWe 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...
2022-03-1154 minDataTalks.ClubBecoming a Data Engineering Manager - Rahul JainWe 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 betw...
2022-03-0451 minDataTalks.ClubA/B Testing - Jakob GraffWe 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
...
2022-02-2554 minDataTalks.ClubMachine Learning System Design Interview - Valerii BabushkinWe 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/sl...
2022-02-1854 minDataTalks.ClubCareer Coaching - Lindsay McQuadeWe 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
2022-02-1152 minDataTalks.ClubProduct Management Essentials for Data Professionals - Greg CoquilloWe 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
2022-02-0453 minDataTalks.ClubRecruiting Data Professionals - Alicja NotowskaWe 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
2022-01-2857 minDataTalks.ClubDataTalks.Club Behind the Scenes - Eugene Yan, Alexey GrigorevWe 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 event...
2022-01-2150 minDataTalks.ClubDTC's minis - From Data Engineering to MLOps - Sejal VaidyaWe 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...
2022-01-1416 minDataTalks.ClubBecoming a Data Science Manager - Mariano SemelmanWe 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
2022-01-071h 05
DataTalks.ClubLeading NLP Teams - Ivan BilanWe 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...
2021-12-2459 minDataTalks.ClubProduct Management for Machine Learning - Geo JollyWe 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 supporti...
2021-12-171h 02DataTalks.ClubMoving from Academia to Industry - CJ JenkinsWe 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-je...
2021-12-1059 minDataTalks.ClubAdvancing Big Data Analytics: Post-Doctoral Research - Eleni Tzirita ZacharatouWe 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://data...
2021-12-031h 00DataTalks.ClubBecoming a Data Product Manager - Sara MenefeeWe talked about:
Sara’s background
Product designer’s responsibilities
Data product manager’s responsibilities
Planning with the team
Design thinking and product design
Data PMs vs regular PMs
Skill requirements for Data PMs
Going from a product designer to a data product manager
Case studies
Resources for learning about product management
Data PM’s biggest challenge
Multitasking and context switching
Insights from user interviews
Using new, unfamiliar tools
Documentation
Idea generation
Do Data PMs need to know ML?
Links:
Product Management Courses: https://www.lennyrachitsky.com/course and https://www.reforge.com/masterin...
2021-11-2659 minDataTalks.ClubData Science Manager vs Data Science Expert - Barbara SobkowiakWe talked about:
Barbara’s background
Do you need a manager or an expert?
Technical and non-technical requirements for managers
Importance of technical skills for managers
Responsibilities and skills of a manager
Importance of technical background for managers
Getting involved in business development and sales
Developing the team
Checking team’s work
Data science expert
Hiring experts
Who should we hire first?
Can an expert build a team?
Data science managers in startups
Project management
Ensuring that projects provide value
Questions before starting a project
Women in data science
Finding Barbara online
General advice
Link...
2021-11-1959 minDataTalks.ClubAce Non-Technical Data Science Interviews - Nick SinghWe talked about:
Nick’s background
Being a career coach
Overview of the hiring process
Behavioral interviews for data scientists
Preparing for behavioral interviews
Handling "tricky" questions
Project deep dive
Business context
Pacing, rambling, and honesty
“What’s your favorite model?”
What if I haven’t worked on a project that brought $1 mln?
Different questions for different levels
Product-sense interviews
Identifying key metrics in unfamiliar domains
Tech blogs
Cold emailing
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
2021-11-121h 01DataTalks.ClubBecoming a Solopreneur in Data - Noah GiftWe talked about:
Noah’s background
Solopreneurship
A day of a solopreneur
Exponential vs linear work
Escaping the office work - digging the tunnel
Structuring goals
Staying motivated
Publishing books
Planning out books
Writing a book is like preparing to run a marathon
Distributed income
Getting started as a solopreneur
Lowering expenses and adding time
The right time to quit full-time
Building a network
Teaching at universities
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
2021-11-0559 minDataTalks.ClubBuilding Business Acumen for Data Professionals - Thom IvesLinks:
https://join.slack.com/t/integratedmlai/shared_invite/zt-r3hpj44k-gfhf1pzIt3jixrATyXCWnQ
https://www.linkedin.com/in/thomives/
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
2021-10-301h 05DataTalks.ClubConquering the Last Mile in Data - Caitlin MoormanWe talked about:
Caitlin’s background
The last mile in data
The Pareto Principle
Failing to use data
Making sure data is used
Communicating with decision-makers
Working backwards from the last mile
Understanding how data drives decisions
Sketching and prototyping
Showing the benefits of power data
Measurability
Driving change in data
Asking high-leverage questions
Resistance from users
Understanding domain experts
Linear projects vs circular projects
Recommendations for data analyst students
Finding Caitlin online
Links:
Emelie's talk
https://locallyoptimistic.com/post/linear-and-circular-projects-part-1/
https://locallyoptimistic.com/post/linear-and-circular-projects-part-2/
Join DataTalks.Club: ht...
2021-10-221h 02DataTalks.ClubSimilarities and Differences between ML and Analytics - Rishabh BhargavaWe talked about:
Rishabh's background
Rishabh’s experience as a sales engineer
Prescriptive analytics vs predictive analytics
The problem with the term ‘data science’
Is machine learning a part of analytics?
Day-to-day of people that work with ML
Rule-based systems to machine learning
The role of analysts in rule-based systems and in data teams
Do data analysts know data better than data scientists?
Data analysts’ documentation and recommendations
Iterative work - data scientists/ML vs data analysts
Analyzing results of experiments
Overlaps between machine learning and analytics
Using tools to bridge the gap between ML and analytics
Do companies...
2021-10-1559 minDataTalks.ClubBuilding and Leading Data Teams - Tammy LiangWe talked about:
Tammy’s background
Being the chief of data
First projects as the first data person in a company
Initial resistance
Expanding the team
Role of business analyst
Platanomelon’s stack
Order for growing the data team
Demand forecasting
Should analysts know machine learning
Qualifications for the first data person in a company
Providing accurate results
Receiving insights in a timely manner
Providing useful insights
Giving ownership to the team
Starting as the first data person in a company
Data For Future podcast
Supporting team members that are stuck
Finding Tammy online
Link...
2021-10-0859 minDataTalks.ClubWhat Researchers and Engineers Can Learn from Each Other - Mihail EricWe talked about:
Mihail’s background
NLP and self-driving vehicles
Transitioning from academia to the industry
Machine learning researchers
Finding open-ended problems
Machine learning engineers
Is data science more engineering or research?
What can engineers and researchers learn from one another?
Bridging the disconnect between researchers and engineers
Breaking down silos
Fluid roles
Full-stack data scientists
Advice to machine learning researchers
Advice to machine learning engineers
Reading papers
Choosing between engineering or research if you’re just starting
Confetti.ai
Links:
https://twitter.com/mihail_eric
http://confetti.ai/
Join...
2021-10-011h 01DataTalks.ClubIntroducing Data Science in Startups - Marianna DiachukWe talked about:
Marianna’s background
Being the only data scientist
What should already be in the company
How much experience do you need
Identifying problems
Prioritization
What should the company already know?
First week
First month
First quarter
Managing expectations
Solving problems without ML
Project timelines
Finding the best solution
Evaluating performance
Getting stuck
Communicating with analysts
Transitioning from engineering to data science
Growing the team
Stopping projects
Questions for the company
From research to production
Wrapping up
Links:
Marianna's LinkedIn: https://www.linkedin.com/in/marianna-diachuk-53ba60116/
Jo...
2021-09-2458 minDataTalks.ClubDefining Success: Metrics and KPIs - Adam SrokaWe talked about:
Adam’s background
Adam’s laser and data experience
Metrics and why do we care about them
Examples of metrics
KPIs
KPI examples
Derived KPIs
Creating metrics — grocery store example
Metric efficiency
North Star metrics
Threshold metrics
Health metrics
Data team metrics
Experiments: treatment and control groups
Accelerate metrics and timeboxing
Links:
Domino's article about measuring value: http://blog.dominodatalab.com/measuring-data-science-business-value
Adam's article about skills useful for data scientists: https://towardsdatascience.com/how-to-apply-your-hard-earned-data-science-skillset-812585e3cc06
Adam's article about standing out: https://towardsdatascience.com/how-to-stand-out-as-a-great-data-scientist-in-2021-3b7a732114a9
2021-09-171h 02DataTalks.ClubMaking Sense of Data Engineering Acronyms and Buzzwords - Natalie KwongWe talked about:
Natalie’s background
Airbyte
What is ETL?
Why ELT instead of ETL?
Transformations
How does ELT help analysts be more independent?
Data marts and Data warehouses
Ingestion DB
ETL vs ELT
Data lakes
Data swamps
Data governance
Ingestion layer vs Data lake
Do you need both a Data warehouse and a Data lake?
Airbyte and ELT
Modern data stack
Reverse ETL
Is drag-and-drop killing data engineering jobs?
Who is responsible for managing unused data?
CDC – Change Data Capture
Slowly changing dimension
Are there cases where ETL is preferable over ELT?
Why is Airbyte open source?
The...
2021-09-111h 00DataTalks.ClubMastering Algorithms and Data Structures - Marcello La RoccaWe talked about:
Learning algorithms and data structures
Resources for learning algorithms and data structures
Most important data structures
Learning the abstractions
Learning algorithms if they aren’t needed at work
Common mistakes when using wrong data structures
Importance of data structures for data scientists
Marcello’s book - Advanced Algorithms and Data Structures
Bloom filters
Where Bloom filters are useful
Approximate nearest neighbours
Searching for most similar vectors
Knowing frameworks vs knowing internals of data structures
Serializing Bloom filters
Algorithmic problems in job interviews
Important data structures for data scientists and data engineers
Learning by doing
Importance of c...
2021-09-031h 02DataTalks.ClubChief Data Officer - Marco De SaWe talked about:
Marco’s background
Role of CDO
Keeping track of many things
Becoming a CDO
Strategy vs tactics
VP of Data vs CDO
How many VPs of Data could be there?
Splitting the work between VP and CDO
Difference between CTO, CPO, and CDO
Breaking down the goals and working backwards from them
Assessing if we’re moving in the right direction
Dealing with many meetings
Being more effective
Building the data-driven culture
Challenges of working remotely
Does CDO need deep technical skills?
Importance of MBA
The key skills for becoming a CDO
Biggest challenges within OLX...
2021-08-271h 01DataTalks.ClubFreelancing in Machine Learning - Mikio BraunWe talked about:
Mikio’s background
What Mikio helps with
Moving from a full-time job to freelancing
Finding clients and importance of a strong network
Building a network
Initial meetings with clients
Understanding what clients need
Template for the offer (Million dollar consulting)
Deciding on rate type: hourly, daily, per project
Taking vacations (and paying twice for them)
Avoiding overworking
Specializing: consulting as a product
Working full-time as a principal vs being a consultant
Is the overhead worth it?
Getting a new client when you already have a project
After freelancing: what’s next?
Output of Mikio’s work
...
2021-08-201h 02DataTalks.ClubLaunching a Startup: From Idea to First Hire - Carmine PaolinoWe 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 star...
2021-08-131h 07DataTalks.ClubApproach Learning as ML Project - Vladimir Finkelshtein [mini]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
2021-08-0613 minDataTalks.ClubHumans in the Loop - Lina WeichbrodtWe 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 input...
2021-07-3057 minDataTalks.ClubRunning from Complexity - Ben WilsonWe 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...
2021-07-231h 11DataTalks.ClubI Want to Build a Machine Learning Startup! - Elena SamuylovaWe 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...
2021-07-1658 minDataTalks.ClubBig Data Engineer vs Data Scientist - Roksolana DiachukLinks:
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
2021-07-091h 01DataTalks.ClubBuild Your Own Data Pipeline - Andreas KretzWe 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 implemen...
2021-07-021h 01DataTalks.ClubFrom Software Engineering to Machine Learning - Santiago ValdarramaWe 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.
...
2021-06-2559 minDataTalks.ClubAnalytics Engineer: New Role in a Data Team - Victoria Perez MolaLinks:
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
2021-06-1859 minDataTalks.ClubData Governance - Jessi Ashdown, Uri GiladWe 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/ug...
2021-06-1157 minDataTalks.ClubWhat Data Scientists Don’t Mention in Their LinkedIn Profiles - Yury KashnitskyWe 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 Twitte...
2021-06-0459 minDataTalks.ClubHow to Market Yourself (without Being a Celebrity) - Shawn Swyx WangWe 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...
2021-05-211h 02DataTalks.ClubEffective Communication with Business for Data Professionals - Lior BarakWe 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/B086L2...
2021-04-3057 minDataTalks.ClubData Observability - Barr MosesWe 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 up...
2021-04-231h 01DataTalks.ClubTransitioning from Project Management to Data Science - Ksenia LegostayWe 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 th...
2021-04-091h 03DataTalks.ClubTranslating ML Predictions Into Better Real-World Results with Decision Optimization - Dan BeckerWe 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
2021-02-1955 min