Why this Document?

The data science guide is created by Divye Gupta, a Data Science enthusiast. Motivation behind this guide came after working on series of projects that belong to different data science disciplines and often it is impossible to remember all the syntax in python to do the analysis. This guide serves as a single source of truth for refering to python code and contains interpretations of results. Using this guide, anyone can quickly brush up their knowledge about key concepts of data science and apply them in their day to day solving of business problems.

Data Science is a very broad (science breath) and deep (science depth) field of science that deals with finding patterns in data and building mechanisms using programmatic interventions to maximize business outcomes. Although, the field has recently exploded (2014 onwards) to broader audience, research has been going on since mid 20th century. Disciplines of data science can be divided into the following broader categories:

• Classical Learning
• Machine Learning

Visit these documents to understand how you can connect science to business problems and implement the solutions in python with little to no programming background. Before, we start applying models and algorithms to a dataset, 60% of the time is devoted to preprocessing and visualizing the data. Check out the Visualizer Pro to see what functions you can use to quickly plot some graphs and communicate insights to the business to gain high level idea about the current situation in the business.