Book Image

Practical Business Intelligence

Book Image

Practical Business Intelligence

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


Business intelligence is the process of delivering actionable business decisions from analytical manipulation and presentation of data within the confines of a business environment. Business intelligence can be delivered using many different tools, including some that were not even originally intended to be used for BI. This book will focus on building a separate BI application in each chapter using a different BI tool. Some of these tools require the use of open source software such as D3.js (JavaScript), R, and Python. Others require the use of popular data discovery desktop tools such as Microsoft Power BI, Tableau, and QlikSense. 

What this book covers

Chapter 1, Introduction to Practical Business Intelligence, serves as the overall introduction to the book and gives a high-level understanding of what business intelligence is, the intended audience of this book, and a summary of the different technologies that will be used.

Chapter 2, Web Scraping, focuses on data extraction from the Web using web scraping libraries from both R and Python. We will also focus on importing data into our data into SQL Server.

Chapter 3, Analysis with Excel and Creating Interactive Maps and Charts with Power BI, focuses on using pivot tables and charts in Microsoft Excel to help with data analysis. Additionally, we build the first BI application using maps and graphs in Microsoft Power BI.

Chapter 4, Creating Bar Charts with D3.js, introduces D3.js and explains how this JavaScript library can be used to leverage visualizations developed with SVG elements as well as data from a CSV file. Ultimately, we will use both methods to develop a bar chart with SVG elements tied to data from a CSV file.

Chapter 5, Forecasting with R, introduces building line charts and time series with R. Additionally, we incorporate forecasting libraries within R to visualize them with existing time series. RStudio is used to deliver R code to business users.

Chapter 6, Creating Histograms and Normal Distribution Plots with Python, covers data visualizations developed with popular Python libraries, such as matplotlib and seaborn. The main goal of this chapter is to build a histogram and normal distribution plot with the Jupyter Notebook and Python.

Chapter 7, Creating a Sales Dashboard with Tableau, focuses on building a dashboard using Tableau Public against a sales and marketing dataset developed in SQL Server.

Chapter 8, Creating an Inventory Dashboard with QlikSense, focuses on building a dashboard using QlikSense against an inventory dataset developed in SQL Server.

Chapter 9, Data Analysis with Microsoft SQL Server, serves as a wrap-up of the technologies covered as well as focusing on advanced querying techniques that can be deployed in SQL Server.

What you need for this book

This book requires the use of a Windows 7/8/10 OS with at least 2 GB RAM and at least 100 GB of hard drive space. Additionally, the following software will need to be installed:

  • Microsoft SQL Server Express 2014

  • Python 3 and PyCharm

  • R and RStudio

  • Microsoft Power BI and Microsoft Excel

  • Tableau Public

  • QlikSense

  • D3.js

Who this book is for

This book is intended for a wide range audience. Technical folks, whether they be BI developers, data scientists, or even data analysts, will find the chapters based on D3.js, Python, and R right up their alley as they are based heavily on building BI applications with open source technology. More business-savvy folks may find more interest in data discovery desktop tools such as Tableau, Power BI, and QlikSense that are not heavily reliant on code. If you are a business intelligence manager looking to establish a department with a variety of tools to help flesh out your requirements, this book could serve as a good source for interview questions to weed out unqualified candidates.


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import pandas as pd

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