whoismanoj/crashds

Crash Course on Data Science in Python

CrashDS

Welcome to the World of Data Science with Python!

This is a crash course in Data Science, with Jupyter Notebook as the User Interface. I hope you'll like the experience. -- Sourav Sen Gupta

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Python has two flavors -- Python 2 and Python 3. This set of examples are in Python 3, written and executed in the beautifully simple IDE Jupyter Notebook. Note that Jupyter has set up a localhost:8888 server to render the notebook in your computer's browser. It can render anything thereafter -- should be fun! Once you are familiar with the Jupyter Notebook environment, and the basic Python syntax in Module 0, feel free to explore the rest of the Modules on Data Science.

Module 0 : Python in Jupyter
Module 1 : Data Exploration
Module 2 : Linear Regression
Module 3 : Classification Tree
Module 4 : Bias Variance Trade-Off
Module 5 : Graphics in Plotly

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To get the most out of this crash course, you'll need to download and use the Jupyter Notebooks for individual Modules posted in this repository (or git clone the repository). Before that, prepare your computer by installing the following.

Platform : Anaconda for Python 3.8 (https://www.anaconda.com/) Core Engine : Python 3.8 (it will get installed with Anaconda)
User Interface (IDE) : Jupyter Notebook (comes with Anaconda)

In case installing Anaconda for Python 3.8 is too heavy for you, and you know how to manage conda environments and packages, you may also download and install the MiniConda (https://docs.conda.io/en/latest/miniconda.html) package for Python 3.8. It's lightweight, but you will have to install all required Data Science packages individually, on your own.

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This material is heavilly inspired by two wonderful books on Data Science, as follows -- one theoretical and one practical. I highly encourage anyone interested in hands-on Data Science to read these two books thoroughly to get a wholesome overview of the subject.

• Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani
• Python Data Science Handbook by Jake VanderPlas

Highly recommended : Python 3 Cheat Sheet by Laurent Pointal. This 2-pager is a life-saver -- print and frame it! This material also borrows some ideas from the Python Notebooks for ISLR. You are encouraged to check it out while you read the book ISLR.

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License Declaration : Following the lead from the inspirations for this material, and the spirit of Python education and development, all modules of this work are licensed under the Creative Commons Attribution 3.0 Unported License.