The Lazy Data Scientist
The Lazy Data Scientist

Retired

This book is no longer available for sale.

The Lazy Data Scientist

This book is 33% complete

Last updated on 2018-10-29

About the Book

In The Lazy Data Scientist, Robert de Graaf will show you how to apply strategic thinking to data science problems to improve sales success and reduce stress. Topics covered include

  • Choosing the right problem
  • Selling your project and yourself
  • Maintaining your model's performance
  • Ensuring your team's capability
  • Much more!

This is essential reading for anyone interested in ensuring their machine learning or statistics project works in a commercial environment. It is also a great read for anyone interested in the 'big picture' of data science and machine learning projects.

About the Author

Table of Contents

  • The Lazy Data Scientist - A Philosophy
    • Working on the wrong problem
    • Not convincing your users
    • Failing to re-use your own work
    • Being hypnotised by novelty
    • Causing work for others
    • Doing a great job, without it being seen
    • Structure of the book
  • Strategy For Data Science
    • Applying Strategy from Other Contexts
    • Putting A Strategy Into Action
  • Don’t Waste A Second
    • Talking to an expert before modeling
    • Presenting a model to an expert
    • Don’t lead the witness
    • Takeaways from best use of experts
    • Further Reading
  • Defensive Strategy for Data Science
    • Data Science Strategies at the Corporate Level
    • Data Science Career Strategies
    • Theory Into Practice
  • Defining The Objective
    • Costs of Getting it wrong
    • The Voice Of The Customer
    • Empathising with the user
    • Data Science and Wicked Problems
  • Closing the Deal - Convincing Customers To Use Your Work
    • Data Privacy
  • Selling Your Data Science Project
    • Benefit Sales
    • Closing
  • Maintain Your Model’s Performance
  • Gain and Keep Your Clients’ Trust
    • Models That Are Explainable and Accurate
    • Windows in the Black Box
    • When Explainable Models are Unlikely
    • Explainable Models Takeaways
    • The Last Mile of Model Transparency
    • Model Presentation Takeaways
    • Meet your users (and their expectations)
    • Further Reading
  • Make promises you can keep (and keep them)
  • Keeping the Bushel Off Your Light
    • Promoting Your Successes
    • Writing Whitepapers
  • Tools to Keep Your Model Performing
    • Learning From Others
  • Coding for Lazy Data Scientists
    • Making the Best Use of SQL
  • Building Resources
    • Getting People On The Field
  • Normalising Your Team
    • Avoiding Non Standard Practices

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