Analytics is the “extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions” [1].
For example, the decision tree is one of the predictive modeling approaches used in statistics, data mining and machine learning. Decision Trees are a supervised learning method used for both classification and regression tasks.
Following is an example of a Decision Tree, which discusses the mode of transport depending on another requirement. Looks easy right. But imagine if you have more than 1,000 data that you need to look one by one in order to create a decision tree as simple as this. Below, is an example of a data product. Simplicity is the ultimate sophistication.
Source: https://www.displayr.com/what-is-a-decision-tree/
This page is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data. This site is intended for sharing a practical, modern introduction to scientific computing, tailored for data-intensive applications. I am a newbie to data analytics and I hope I can share some insights that I have learned with my students and respected readers.
Collecting data is so easy, but turning raw data into information and then into knowledge in the form of business rules requires that you know how to extract precisely what you need. Along the way, we’ll experiment with concepts through hands-on lab during the lecture. Above all, we’ll learn how to think about the results we want to achieve – rather than rely on tools to think for you.
[1] Davenport, Thomas and, Harris, Jeanne (2007). Competing on Analytics. O’Reilly. ISBN 978-1-4221-0332-6.