Python is very easy to learn and implement. For many people including myself python language is easy to fall in love with. Since his first appearance in 1991, python popularity is increasing day by day. Among interpreted languages Python is distinguished by its large and active scientific computing community. Adoption of Python for scientific computing in both industry applications and academic research has increased significantly since the early 2000s.
Approach of learning:
On the left side of the web page, you will able to see the topics, where you need to click on the topic and you will be redirect to detail page of that particular topic.
This tutorial will give you operational idea about Python tools to work productively with data. While readers may have many different end goals for their work, the tasks required generally fall into a number of different broad groups
I have divided the content in three parts which is basic python, data analysis with numpy and pandas finally data visualization with matplotlib and seaborn.
Following are the broad spectrum of the course
You learn python basic concepts, which will help you to build advance data analysis concepts.
Interacting with Data set:
Reading and writing with a variety of file formats and databases.
Cleaning, mugging, combining, normalizing, reshaping, slicing and dicing, and transforming data for analysis.
Applying mathematical and statistical operations to groups of data sets to derive new data sets. For example, aggregating a large table by group variables.
Modeling and computation:
Connecting your data to statistical models, machine learning algorithms, or other computational tools
Creating interactive or static graphical visualizations or textual summaries