Overview
An introduction guide to Python beginners, especially for data analyst / data scientist Python users. This tutorial is for Python 3.6 version.
Setup
There are different ways to install and run Python. The easiest way is to download Anaconda and run Python in Jupyter Notebook.
Step 1: Download Python 3.6 with Anaconda at https://www.anaconda.com/download/#macos.
Step 2: Open terminal, type in jupyter notebook, then a tunnel for a web-based Jupyter Notebook App is established.
Step 3: Click the tap New on the top right corner to create a new notebook. Type in print('Hello, Python!') and Shift-Enter to run. If the string Hello, Python! is printed, then you should be good to go.
About the repo
The notebook files (.pynb) with numbers are ordered introductory materials for Python beginners. It is recommended that one should start with 0-Notebook.ipynb followed by 1-python-basics.ipynb and so on. For those who have previous experience with Python, you can start wherever of your interest.
The Udacity-CS101 folder contains nontrivial solutions for problems in the MOOC Introduction to Computer Science. This course is a good starter point for people with no or little previous coding experience. It is taught all in Python.
The Coursera-Course-1 folder contains solutions for the Coursera course Introduction to Data Science in Python. It is the first of the five courses series in Applied Data Science with Specialization. I will upload solutions for the rest courses later.
The Machine-Learning folder contains the classical machine learning examples and demos realized in Python. The Demos subfolder contains examples on Python and PySpark data analysis on public available data on UCI Machine Learning Repository.
The PySpark folder contains Spark related materials on cloud platform.
I will add more contents in the future depends on needs and mood.