We talked about:

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

Links:

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

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

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

We 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:

Barbara's LinkedIn: https://www.linkedin.com/in/barbara-sobkowiak-1a4a9568

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

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

We 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

We 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 overinvest in ML and underinvest in analystics? Do companies hire data scientists while forgetting to hire data analysts? The difficulty of finding senior data analysts Is data science sexier than data analytics? Should ML and data analytics teams work together or independently? Building data teams Rishabh’s newsletter – MLOpsRoundup

Links:

https://mlopsroundup.substack.com/ https://twitter.com/rish_bhargava

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

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

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

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

We 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/

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:

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

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:

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​​​

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

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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 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