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