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

Alternative plotting libraries with Python


matplotlib is not the only game in town for plotting with Python. There are several other visualization libraries that are very powerful. One of them is seaborn. seaborn is actually based on matplotlib, so it contains similar functionality but makes more visually appealing plots with minimal coding.

Note

To learn more about seaborn, visit the following website:  http://seaborn.pydata.org

Before we can get started with seaborn, we will need to install it using either PyCharm or manually through the command line:

pip install seaborn

Once it is installed we can call the module into our Jupyter Notebook using the following command:

import seaborn as sb 

We can plot a histogram using VacationHours with the following script:

sb.distplot(VacationHours, kde = False, rug=True) 

When the script is executed, we can see the following histogram built with seaborn:

The rug parameter is set to True. This feature allows for the short rug-like bars at the bottom...