15 Mistakes to Avoid in Data Science

Save time and grow your skills faster. Learn the top mistakes that you should avoid as a data scientist.

Introduction

• Avoid common mistakes to excel in data science

1. Mistakes to Avoid

• 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

Conclusion

• Get started on the right path