DataTalks.Club

By DataTalks.ClubTechnology

DataTalks.Club episodes

June 10, 2022Getting a Data Engineering Job (Summary and Q&A) - Jeff Katz
We 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 Engineering Job (webinar): https://www.youtube.com/watch?v=yvEWG-S1F_M

The Flask Mega-Tutorial Part I - Hello, World! blog: https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-i-hello-world

Mode SQL Tutorial: https://mode.com/sql-tutorial/

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
49 min
June 03, 2022Using Data for Asteroid Mining - Daynan Crull
We 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/daynan/

We're looking for a Sr Data Engineer: https://boards.eu.greenhouse.io/karmanplus/jobs/4027128101?gh_jid=4027128101

Minor Planet Center: https://minorplanetcenter.net/- JPL Horizons has a nice set of APIs for accessing data related to small bodies (including asteroids): https://ssd.jpl.nasa.gov/api.html

ESA has NEODyS: https://newton.spacedys.com/neodys

IRSA catalog that contains image and catalog data related to the WISE/NEOWISE data (and other infrared platforms): https://irsa.ipac.caltech.edu/frontpage/

NASA also has an archive of data collected from their various missions, including a node related to small bodies: https://pds-smallbodies.astro.umd.edu/

Sub-node directly related to asteroids: https://sbn.psi.edu/pds/

Size, Mass, and Density of Asteroids (SiMDA) is a nice catalog of observed asteroid attributes (and an indication of how small our sample size is!): https://astro.kretlow.de/?SiMDA

The source survey data, several are useful for asteroids: Pan-STARRS (https://outerspace.stsci.edu/display/PANSTARRS)

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
54 min
May 27, 2022Machine Learning in Marketing - Juan Orduz
We 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 knowledge for data scientists

Juan’s blog and other learning resources

Finding Juan online

Links:
Juan's PyData talk on uplift modeling: https://youtube.com/watch?v=VWjsi-5yc3w

Juan's website: https://juanitorduz.github.io

Introduction to Algorithmic Marketing book: https://algorithmic-marketing.online

Preventing churn like a bandit: https://www.youtube.com/watch?v=n1uqeBNUlRM

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
53 min
May 20, 2022From Academia to Data Analytics and Engineering - Gloria Quiceno
We 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 applicants

The cohorts at Spiced

Conclusion

Links:
LinkedIn: https://www.linkedin.com/in/gloria-quiceno/

Github: https://github.com/gdq12

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
49 min
May 13, 2022Teaching Data Engineers - Jeff Katz
We 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/ELT and DBT (or lack thereof)

The importance of Python, SQL, and data modeling for data engineering roles

Interview expectations

How to get started in teaching

The challenges of being a one-person company

Teaching fundamentals vs the “shiny new stuff”

JigsawLabs.io

Finding Jeff online

Links:
Jigsaw Labs: https://www.jigsawlabs.io/free

Teaching my mom to code: https://www.youtube.com/watch?v=OfWwfTXGjBM

Getting a Data Engineering Job Webinar with Jeff Katz: https://www.eventbrite.de/e/getting-a-data-engineering-job-tickets-310270877547

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
53 min
May 06, 2022From Roasting Coffee to Backend Development - Jessica Greene
We 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 speaking on your career

Tips for public speaking

Jessica’s work at Ecosia

Discrimination in the tech industry (and in general)

Finding Jessica online

Links:
Ecosia's website: https://www.ecosia.org/

Ecosia's blog: https://blog.ecosia.org/ecosia-financial-reports-tree-planting-receipts/

PyLadies Berlin: https://berlin.pyladies.com/

PyLadies' Meetup: https://meetup.com/PyLadies-Berlin

Code Academy: https://www.codecademy.com/

Freecodecamp: https://www.freecodecamp.org/

Coursera Machine Learning: https://www.coursera.org/learn/machine-learning

ML Bookcamp code: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Google Summer code: https://summerofcode.withgoogle.com/

Outreachy website: https://www.outreachy.org/

Alumni Interview: https://railsgirlssummerofcode.org/blog/2020-03-17-alumni-interview-jessica

Python pizza: https://python.pizza/

Pycon: https://pycon.it/en

Pycon 2022: https://2022.pycon.de/

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

Our events: https://datatalks.club/events.html
53 min
April 29, 2022Recruiting Data Engineers - Nicolas Rassam
We 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 interview

Conclusion

Links:
Nicolas' Twitter: https://twitter.com/n_rassam

Nicolas' LinkedIn: https://www.linkedin.com/in/nicolasrassam/

Onfido is hiring: https://onfido.com/engineering-technology/

Interview with Alicja about recruiting data scientists: https://datatalks.club/podcast/s07e02-recruiting-data-professionals.html

Webinar "Getting a Data Engineering Job" with Jeff Katz: https://eventbrite.com/e/310270877547

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

Our events: https://datatalks.club/events.html
50 min
April 22, 2022Storytime for DataOps - Christopher Bergh
We 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: https://dataops.datakitchen.io/pf-recipes-for-dataops-success

DataOps Certification Course: https://info.datakitchen.io/training-certification-dataops-fundamentals

DataOps Blog: https://datakitchen.io/blog/

DataOps Maturity Model: https://datakitchen.io/dataops-maturity-model/

DataOps Webinars: https://datakitchen.io/webinars/

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

Our events: https://datatalks.club/events.html
53 min
April 15, 2022Machine Learning and Personalization in Healthcare - Stefan Gudmundsson
We 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-driven company

Risks for Sidekick Health

Sidekick Health growth strategy

Using AI to help people live better lives

Links:
LinkedIn: https://www.linkedin.com/in/stefanfreyrgudmundsson/

Job listings: https://sidekickhealth.bamboohr.com/jobs/
Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html
52 min
April 08, 2022Innovation and Design for Machine Learning - Liesbeth Dingemans
We 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 issues, and using data to back your ideas

Words of advice for those interested in design and innovation

Links:
LinkedIn: https://www.linkedin.com/in/liesbeth-dingemans/

Medium posts on design, innovation, art and AI: https://medium.com/@liesbethmd

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

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