Data Team Learning and Resources

1. You are here:
2. Business Technology
3. Data Team
4. Data Team Learning and Resources

On this page

• Powered by GitLab Team Members
• Recommended Reading, Listening, Watching
• Data Newsletters
• Data Blogs
• Data Visualization Resources
• Data Slack Communities
• Technical Learning Resources

---

Powered by GitLab Team Members

• How Data Teams Do More With Less By Adopting Software Engineering Best Practices - Thomas's talk at the 2018 DataEngConf in NYC
• Slides from a similar talk by Taylor at the 2019 Music City Tech Conference in Nashville
• Locally Optimistic AMA: August 2019 with Taylor Murphy
• 4 Examples of the power of open source analytics
• Deploying your first dbt project with GitLab CI
• DataOps in a Cloud Native World
• The Three Levels of Data Analysis- A Framework for Assessing Data Organization Maturity
• How to Implement DataOps using GitLab
• Lessons learned managing the GitLab Data team
• Views on Vue Podcast with Jacob Schatz and Taylor Murphy
• How to do DataOps with GitLab - Customer Call (GitLab Internal)
• GitLab for ML - Customer Call (GitLab Internal)

Recommended Reading, Listening, Watching

• The AI Hierarchy of Needs
• Data Meta Metrics
• Engineers Shouldn’t Write ETL
• The Startup Founder’s Guide to Analytics
• Functional Data Engineering — a modern paradigm for batch data processing
• Keep it SQL Stupid, a talk by Connor McArthur of Fishtown Analytics at DataEngConf SF '18 explaining dbt
• DevOps for AI
• What Can Data Scientists Learn from DevOps
• One Analyst's Guide for Going from Good to Great
• The Value of Data: Part 1, Part 2, Part 3
• Building a Data Practice
• Does my Startup Data Team Need a Data Engineer
• Data Science is different now (Note: this is why GitLab doesn't have a Data Scientist yet.)
• Why You Don't Need Data Scientists
• Resources Written by dbt Community Members
• Is your company too dumb to be data-driven?
• What does "self-serve" analytics mean to you?
• Models for integrating data science teams within organizations
• Building a Mature Analytics Workflow (Note: this explains the "Analytics is a subfield of software engineering" premise.)
• DataOps playlist on YouTube

Data Newsletters

• Algos & Ethics
• Calogica
• The Carpentries
• DataEng Weekly
• Data Elixir
• Data is Plural
• Data Science Roundup Newsletter
• Data Science Weekly
• Lantrns Analytics (Product Analytics)
• Music and Tech
• Normcore Tech
• NumLock News
• One Shot Learning
• SF Data

Data Blogs

• Airbnb
• Ask Good Questions
• Buffer Blog
• Calogica
• Fishtown Analytics Blog
• Go Data Driven
• MBA Mondays
• Mode Analytics Blog
• Multithreaded
• Sisense Data Blog
• Silota
• Wes McKinney Blog
• Data Ops
• Retina AI Blog
• StitchFix Algorithms Blog
• Five Thirty Eight
• Data.gov

Data Visualization Resources

• Storytelling with Data
• Data Revelations
• Data Visualization Catalogue
• Eager Eyes
• FiveThirtyEight's DataLab
• Flowing Data
• From Data to Viz
• Gravy Anecdote
• JunkCharts
• Make a Powerful Point
• Makeover Monday
• Perceptual Edge
• PolicyViz
• The Functional Art
• The Pudding
• Visualising Data
• VizWiz
• WTF Visualizations

Data Slack Communities

• Data Viz Society
• Data Science Community
• dbt
• Great Expectations
• Locally Optimistic
• Measure
• Meltano
• Open Data Community
• Pachyderm
• Prefect
• PyCarolinas
• R for Data Analysis
• Software Engineering Daily
• The Data School

Technical Learning Resources

• Chris Albon
• Mode SQL Tutorial
• dbt Tutorial
• Technically.dev Post on SQL
• Elements of Data Science
• Machine Learning Resources (GitLab Internal)
• Codecademy
• DataQuest
• Khan Academy
• HackerRank (Exercises)
• Udacity
• Stanford University Mini-Courses
• The Data School by Chartio
• W3Schools