Beginners - How to get started with Python (2018)

Here are steps to get started with Python.

1. Go to automatetheboringstuff.com and download the the free pdf book Automate The Boring Stuff written by Al Sweigart. 
2. The book is not boring at all and one of the best to get started. If you prefer video lessons Udemy.com has a paid video tutorial for the same book. Support the author by purchasing the print & ebook bundle from No Starch Press or separately on Amazon.
3. There are many flavors of Python and you can download any one. Download Python from https://www.anaconda.com if you do not have preferences. 
4. Python has a inbuilt IDE called IDLE  just type IDLE in search once you have installed Python. The best IDE for Python is Spyder IDE and you can either download it or use IDLE.  
5. Once you install python if you have programming experience you can directly get started by looking at the demo examples in the Tools/demo folder of your Python installation.
6. The last 2 images below show the beer bottle demo code and its execution.
7. This is all you need to get started. For any issues you can drop an email or check solutions on stackoverflow.com/python

Python default IDLE IDE

Sample code with Python installation

Demo code beer.py

Execution of beer.py

Machine Learning with R

R is a powerful language used widely for data analysis and statistical computing.and has many provisions to implement machine learning algorithms in a fast and simple manner.  Born in 1990 R is getting prominence today because of its simplicity.

• R is data analysis software: Data scientists can use R for statistical analysis, data visualization, and predictive modeling.
• R is a programming language: R is an object-oriented language & provides objects, operators, and functions that allow users to explore, model, and visualize data.
• R is an defacto environment for statistical analysis: Standard statistical methods are easy to implement in R, and since much of the cutting-edge research in statistics and predictive modeling is done in R, newly developed techniques are often available in R first.
• R is an open-source software project: R’s open interfaces allow it to integrate with other applications and systems.
• R community: The R project leadership has grown to include more than 20 leading statisticians and computer scientists from around the world, and thousands of contributors have created add-on packages. 

Take an example to understand the power of R language in today's collaborative open source world. The picture below shows heat map of a volcano is a R program of 15 lines that uses multiple R libraries developed by the community. 







And here are the lines of  R code that produced the above visualization :-




I will post more about R and why is it my ( actually all data scientist) favorite tool for data analysis. Mean while I am sharing a small  tutorial for beginners to R that is available at https://www.analyticsvidhya.com

A Complete Tutorial to learn Data Science in R from Scratch