Tom Wilson wilsonify

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1. Data Scratch Library

   pure python library code

   Data science is a field that combines programming, statistics, and subject expertise. Mastering all three is hard.

   The goal is to provide hands-on examples and encourage learning by doing.

   Build data science tools and algorithms by hand rather than relying on pre-built libraries.

   This method aims to deepen understanding, even though the custom-built tools may only work well on small datasets.

2. Data Plots

   plotting code that depends on matplotlib

   data visualization is fundamental to data science. it is very easy to create visualizations, but it is hard to produce good visualizations.

3. Data Scrape

   scraping code that depends on requests and beautiful soup

   data scientist needs data.

   data scientists spend a large amount of time acquiring and transforming data.

   you can always type the data in manually, but that is not a good use of time.

4. Data Scratch C++ Library

   port to C++

   since Data Scratch Library is written in pure python, it is not hard to port it to pure C++

5. Data Scratch Node Library

   port to javascript

6. Data Scratch AMQP

   amqp based pika consumer