DataTalks.Club

By DataTalks.ClubTechnology

DataTalks.Club episodes

January 21, 2022DataTalks.Club Behind the Scenes - Eugene Yan, Alexey Grigorev
We 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 events: https://datatalks.club/events.html
51 min
January 14, 2022DTC's minis - From Data Engineering to MLOps - Sejal Vaidya
We 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: mlinproduction, huyenchip.com, jeremyjordan.me, mihaileric.com

Sejal's "Production ML" twitter list (https://twitter.com/i/lists/1212819218959351809)

Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
17 min
January 07, 2022Becoming a Data Science Manager - Mariano Semelman
We 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
1 hr 6 min
December 24, 2021Leading NLP Teams - Ivan Bilan
We 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-bilan/The-Microservices-Pandect

Ivan's presentation about NLP: https://www.youtube.com/watch?v=VRur3xey31s

Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
1 hr
December 17, 2021Product Management for Machine Learning - Geo Jolly
We 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 supporting an ML team as a Scrum master

Links:
Geo's LinkedIn: https://www.linkedin.com/in/geojolly/

Product School community: https://productschool.com/

http://theleanstartup.com/

Netflix CPO Medium blog: https://gibsonbiddle.medium.com/

Glovo is hiring: https://jobs.glovoapp.com/en/?d=4040726002

Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
1 hr 3 min
December 10, 2021Moving from Academia to Industry - CJ Jenkins
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
1 hr
December 03, 2021Advancing Big Data Analytics: Post-Doctoral Research - Eleni Tzirita Zacharatou
We 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://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
1 hr 1 min
November 26, 2021Becoming a Data Product Manager - Sara Menefee
We 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/mastering-product-management

Product Management Reading:

https://svpg.com/inspired-how-to-create-products-customers-love/ and https://steveblank.com/category/customer-development/

Data Engineering for Noobs: https://www.datacamp.com/

Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
1 hr
November 19, 2021Data Science Manager vs Data Science Expert - Barbara Sobkowiak
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
1 hr
November 12, 2021Ace Non-Technical Data Science Interviews - Nick Singh
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
1 hr 2 min