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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