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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