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

Combining a histogram with a normal distribution plot


We now have a histogram and a normal distribution plot individually, but it would be nice if we could visualize both them on one graph with the same scales. This can easily be done by referencing both plots in a single cell and then using the plt.show() function just once after both plots have been called:

plt.hist(VacationHours, normed = True) # plotting histogram 
plt.plot(VacationHours, normal_distribution_curve, color = "orange") #plotting normal curve 
plt.title("Vacation Hours") #Assign title 
plt.xlabel("Hours") #Assign x label 
plt.ylabel("Count") #Assign y label 
plt.show() 

The output of the combined plots can be seen in the following screenshot:

We now have a combined normal distribution plot and histogram for us to see the distribution of VacationHours across different job titles for AdventureWorks.

Annotating in Python

One of the nice features with matplotlib is the ability to annotate graphs to help guide users to areas of...