Our Team have been collecting all Data Science info sheets. From time to time we share them with friends and colleagues and recently we have been getting asked a lot, so we decided to organize and share the some of collections. To make things more interesting and give context, we added descriptions and/or excerpts for each major topic.
This is the most complete list and the Big-O is at the very end !
Neural Networks Graphs
Machine Learning: Scikit-learn algorithm
This machine learning info sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.
MICROSOFT AZURE MACHINE LEARNING INFO SHEET
This machine learning info sheet from Microsoft Azure will help you choose the appropriate machine learning algorithms for your predictive analytics solution. First, the cheat sheet will asks you about the data nature and then suggests the best algorithm for the job.
Numpy
NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms written for this version of Python often run much slower than compiled equivalents. NumPy address the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays, requiring rewriting some code, mostly inner loops using NumPy.
Data Wrangling
Scipy
SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.
