1. Preface

Chinese proverb

The palest ink is better than the best memory. – old Chinese proverb

1.1. About

1.1.1. About this note

This document is a summary of our valueable experiences in using Python for Data Scientist daily work. The PDF version can be downloaded from HERE.

You may download and distribute it. Please be aware, however, that the note contains typos as well as inaccurate or incorrect description.

In this repository, we try to use the detailed Data Scientist related demo code and examples to share some useful python tips for Data Scientist work. If you find your work wasn’t cited in this note, please feel free to let me know.

Although we are by no means a python programming and Data Scientist expert, We decided that it would be useful for us to share what we learned about Python in the form of easy note with detailed example. We hope those notes will be a valuable tool for your studies.

The notes assume that the reader has a preliminary knowledge of python programing, LaTex and Linux. And this document is generated automatically by using sphinx. More details can be found at [Georg2018].

1.1.2. About the authors

  • Wenqiang Feng

  • Jing Yang

  • Declaration

    The work of Wenqiang Feng was supported by the IMA, while working at IMA. However, any opinion, finding, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the IMA, UTK, DST and Harvard.

1.2. Motivation for this note

No matter you like it or not, Python has been one of the most popular programming languages. I have been using Python for almost 4 years. Frankly speaking, I wasn’t impressed and attracted by Python at the first using. After starting working in industry, I have to use Python. Gradually I recognize the elegance of Python and use it as one of my main programming language. But I found that:

  • Most of the Python books or tutorials which emphasize on programming will overwhelm the green hand.

  • While most of the Python books or tutorials for Data Scientist or Data Analysis didn’t cover some essential skills from the engineer side.

So I want to keep some of my valuable tips which are heavily applied in my daily work.

1.3. Feedback and suggestions

Your comments and suggestions are highly appreciated. I am more than happy to receive corrections, suggestions or feedbacks through email (Wenqiang Feng: von198@gmail.com, Jing Yang: jingyangharvard@gmail.com ) for improvements.