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
About the Author
About the Reviewer
Customer Feedback

Preparing a SQL Server query for human resources data

As we've done with previous chapters, we must first prepare our data that we want to visualize inside Python. This dataset will focus on the human resources department for the AdventureWorks company. Our query will pull back the total number of vacation hours that each job title has available. The data for this query is available in the Employee table of the AdventureWorks database, and the following SQL statement will help us generate the results needed:

,sum([VacationHours]) as VacationHours 
FROM [AdventureWorks2014].[HumanResources].[Employee] 
group by [JobTitle] 
order by [VacationHours] asc; 

The result of the SQL statement for the first ten rows can be seen in the following screenshot:

The full dataset from this query result will be the foundation of our histogram as well as our normal distribution plot.