Chapter Summary

1.7. Chapter Summary#

1.7.1. Key Take-Aways#

  • Jupyter notebooks allow mixing programming code, text, graphics, mathematics, and other types of media.

  • Markdown is the text markup language used by Jupyter.

  • Jupyter notebooks can use the mathematical markup features of LaTeX to provide beautifully formatted equations.

  • Python is widely used in the data science and machine learning communities.

  • Python has many well-developed libraries for data science and other applications.

  • Python uses 0-based indexing. This is the same as the C/C++ programming language but different than MATLAB.

  • Python operators try to do the right thing with the inputs they are given, but sometimes this can lead to unexpected results.

  • Python variables often point to objects like lists and arrays. Setting a new variable equal to an existing variable does not make a copy unless a variable’s \sphinxcode{copy()} method is used explicitly.

  • Indentation is used in Python to determine the scope of statements. This is very different than many other languages, such as C/C++, which use delimiters such as {} for this purpose.

1.7.2. Review#

This chapter explains who this book is designed for and a reader would choose to learn data science from this book. Then two of the key tools are briefly introduced: Jupyter and Python.

Self-Assessment:

The following questions can be used to check your understanding of the material covered in this chapter:

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