/ Basics

Data Science Cheat Sheets (updated)

Learning how to code in Python can be overwhelming at the beginning. There are a lot of important packages like Numpy, Pandas and of course Matplotlib, that need to become part of your routine. The following cheatsheets can help you in your learning process:


Look up the basics of coding in Python.

Dataquest - Python Basics

Datacamp - Python Basics

2. Numpy

The Numpy library gives you access to many high-level mathematical functions, large multi-dimensional arrays and matrices.

Dataquest - Numpy Basics

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3. Pandas

Slicing, pivoting, reshaping, merging, joining, you name it. With the help of Pandas, manipulating your data is a piece of cake. It also comes with powerful import tools.

Dataquest - Pandas Basics

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4. Matplotlib

The standard plotting library. The Matplotlib.pyplot module offers Matlab-like integration.

Datacamp - Matplotlib Basics

5. Bokeh

A Python interactive visualization library using web browsers for presentation.

Datacamp - Bokeh Basics

6. Scikit-learn

Machine Learning in Python. Simple and efficient tools for data mining and data analysis. Algorithms for classification, regression and clustering, including support vector machines, random forests, gradient boosting, k-means.

Datacamp - Scikit-learn

8. Keras

Gives you interfaces to work with Theano, CNTK or Tensorflow.

Datacamp - Keras

8. PySpark

Apache Spark is a engine that speeds up big data analysis via clustering. The PySpark library gives you access to the API via Python.

Datacamp - PySpark Basics

9. Seaborn

Seaborn provides a high-level Matplotlib interface for drawing attractive statistical graphics

Datacamp - Seaborn