Book Image

Practical Business Intelligence

By : Ahmed Sherif
Book Image

Practical Business Intelligence

By: Ahmed Sherif

Overview of this book

Business Intelligence (BI) is at the crux of revolutionizing enterprise. Everyone wants to minimize losses and maximize profits. Thanks to Big Data and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Just knowing how to analyze data is not enough, you need to start thinking how to use data as a business asset and then perform the right analysis to build an insightful BI solution. Efficient BI strives to achieve the automation of data for ease of reporting and analysis. Through this book, you will develop the ability to think along the right lines and use more than one tool to perform analysis depending on the needs of your business. We start off by preparing you for data analytics. We then move on to teach you a range of techniques to fetch important information from various databases, which can be used to optimize your business. The book aims to provide a full end-to-end solution for an environment setup that can help you make informed business decisions and deliver efficient and automated BI solutions to any company. It is a complete guide for implementing Business intelligence with the help of the most powerful tools like D3.js, R, Tableau, Qlikview and Python that are available on the market.
Table of Contents (16 chapters)
Practical Business Intelligence
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Visualizing normal distribution plots in Python


The plot most often accompanied by a histogram is a normal distribution plot. These plots come in handy when we are trying to identify averages, outliers, and distributions. Also, they are very easy to produce with Python. They require the following two libraries to be installed:

  • numpy

  • scipy

Note

sciPy will help us with producing the normalization parameters of the curve and NumPy, a library that is often associated with linear algebra, will help us perform several mathematical functions.

We installed scipy earlier in the chapter; however, numpy may need to be installed either through PyCharm or through the command line, as follows:

pip install numpy

We can begin by importing both of them into our project, as seen in the following script.

import numpy as np 
import scipy.stats as stats 

A normal distribution curve requires two values for its creation:

  • Mean

  • Standard deviation

Note

The mean sets the center of the curve and the standard deviation sets...