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