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.
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.
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
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.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The next lines of code read the link and assign it to the to the BeautifulSoup function."
A block of code is set as follows:
#import packages into the project from bs4 import BeautifulSoup from urllib.request import urlopen import pandas as pd
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
<head>
<script src="d3.js" charset="utf-8"></script>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width">
<title>JS Bin</title>
</head>Any command-line input or output is written as follows:
C:\Python34\Scripts> pip install -upgrade pip C:\Python34\Scripts> pip install pandas
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "In order to download new modules, we will go to Files | Settings | Project Name | Project Interpreter."
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