Save time and grow your skills faster. Learn the top mistakes that you should avoid as a data scientist.
Overview
Syllabus
Introduction
- Avoid common mistakes to excel in data science
- Communicating with overly technical language
- Skipping the fundamentals
- Moving too quickly
- Having a data set that is too small
- Failing to adopt new tools
- Not considering the level of variation
- Lack of documentation
- Relying solely on formal education
- Taking too long to share results
- Including your bias
- Overpromising solutions to stakeholders
- Building tools from scratch
- Assuming the knowledge level of stakeholders
- Not telling a story with the data
- Not confirming with stakeholders
- Get started on the right path
Taught by
Lacey Westphal, Sam Cvetkovski, Louis Tremblay, Sara Anstey and Madecraft
