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Bugs and Mistakes in Data Science

Video Description

Data science projects are not so straight-forward in practice. Many hands-on books describe various parts of the process in such a way that you may come to expect everything to go about smoothly and insights to naturally come out from your analyses. However, when you try to apply all this know-how in your own projects, you often end up with bugs in the code and making various mistakes in the process, without realizing it. Even though a colleague can help you pinpoint certain issues in your project, it is often the case that you need to find the majority of them on your own - and fix them. This video helps you get a better perspective of what are the most common problems you’ll encounter when working on a data science project. This know-how, along with some hands-on experience can help you eliminate these bugs that most data scientists face at one point or another, and make the whole process smoother and more enjoyable.

We will cover:
  • An overview of the data science process, including where bugs and mistakes are introduced and can be found
  • Types of programming bugs, including indexing and values issues
  • Common mistakes in the data science process, including sampling issues, identifying the wrong metrics, overfitting problems, and unnecessary assumptions
  • Strategies for coping with bugs, including documentation consultation and unit testing
  • Strategies for coping with more high-level issues

Table of Contents

  1. Bugs and Mistakes in Data Science 00:27:52