Practical Business Intelligence

Learn to get the most out of your business data to optimize your business
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Practical Business Intelligence

Ahmed Sherif

Learn to get the most out of your business data to optimize your business

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Book Details

ISBN 139781785885433
Paperback352 pages

Book Description

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

Chapter 1: Introduction to Practical Business Intelligence
Understanding the Kimball method
Understanding business intelligence architecture
Who will benefit from this book?
Working with data and SQL
Working with business intelligence tools
Downloading and installing MS SQL Server 2014
Downloading and installing AdventureWorks
Summary
Chapter 2: Web Scraping
Getting started with R
Web scraping with R
Getting started with Python
Web scraping with Python
Uploading data frames to Microsoft SQL Server
Summary
Chapter 3: Analysis with Excel and Creating Interactive Maps and Charts with Power BI
Getting to know your data in SQL Server
Connecting Excel to a SQL Server Table
Connecting Excel to SQL Statements
Getting started with Microsoft Power BI
Creating visualizations with Power BI
Summary
Chapter 4: Creating Bar Charts with D3.js
Some background about the D3 architecture
Loading D3 templates for development
Setting up traditional HTML components
Blending D3 and data
Fusing D3 and CSV
Summary
Chapter 5: Forecasting with R
Configuring an ODBC connection
Connecting R to a SQL query
Profiling dataframes in R
Creating graphs in R
Time series forecasting in R
Formatting and publishing code using R Markdown
Exporting R to Microsoft Power BI
Summary
Chapter 6: Creating Histograms and Normal Distribution Plots with Python
Preparing a SQL Server query for human resources data
Connecting Python to Microsoft SQL Server
Visualizing histograms in Python
Visualizing normal distribution plots in Python
Combining a histogram with a normal distribution plot
Alternative plotting libraries with Python
Publishing Jupyter Notebook
Summary
Chapter 7: Creating a Sales Dashboard with Tableau
Building a sales query in MS SQL Server
Downloading Tableau
Installing Tableau
Importing data into Tableau
Building a sales dashboard in Tableau
Building a sales dashboard in Tableau
Publishing dashboard to Tableau Public
Summary
Chapter 8: Creating an Inventory Dashboard with QlikSense
Getting started with QlikSense Desktop
Developing an inventory dataset with SQL Server
Connecting SQL Server query to QlikSense Desktop
Developing interactive visual components with QlikSense Desktop
Publishing the inventory dashboard
Summary
Chapter 9: Data Analysis with Microsoft SQL Server
Comparing tools head-to-head
Developing views in SQL Server
Performing window functions in SQL Server
Performing stored procedures in SQL Server
Summary

What You Will Learn

  • Create a BI environment that enables self-service reporting
  • Understand SQL and the aggregation of data
  • Develop a data model suitable for analytical reporting
  • Connect a data warehouse to the analytic reporting tools
  • Understand the specific benefits behind visualizations with D3.js, R, Tableau, QlikView, and Python
  • Get to know the best practices to develop various reports and applications when using BI tools
  • Explore the field of data analysis with all the data we will use for reporting

Authors

Table of Contents

Chapter 1: Introduction to Practical Business Intelligence
Understanding the Kimball method
Understanding business intelligence architecture
Who will benefit from this book?
Working with data and SQL
Working with business intelligence tools
Downloading and installing MS SQL Server 2014
Downloading and installing AdventureWorks
Summary
Chapter 2: Web Scraping
Getting started with R
Web scraping with R
Getting started with Python
Web scraping with Python
Uploading data frames to Microsoft SQL Server
Summary
Chapter 3: Analysis with Excel and Creating Interactive Maps and Charts with Power BI
Getting to know your data in SQL Server
Connecting Excel to a SQL Server Table
Connecting Excel to SQL Statements
Getting started with Microsoft Power BI
Creating visualizations with Power BI
Summary
Chapter 4: Creating Bar Charts with D3.js
Some background about the D3 architecture
Loading D3 templates for development
Setting up traditional HTML components
Blending D3 and data
Fusing D3 and CSV
Summary
Chapter 5: Forecasting with R
Configuring an ODBC connection
Connecting R to a SQL query
Profiling dataframes in R
Creating graphs in R
Time series forecasting in R
Formatting and publishing code using R Markdown
Exporting R to Microsoft Power BI
Summary
Chapter 6: Creating Histograms and Normal Distribution Plots with Python
Preparing a SQL Server query for human resources data
Connecting Python to Microsoft SQL Server
Visualizing histograms in Python
Visualizing normal distribution plots in Python
Combining a histogram with a normal distribution plot
Alternative plotting libraries with Python
Publishing Jupyter Notebook
Summary
Chapter 7: Creating a Sales Dashboard with Tableau
Building a sales query in MS SQL Server
Downloading Tableau
Installing Tableau
Importing data into Tableau
Building a sales dashboard in Tableau
Building a sales dashboard in Tableau
Publishing dashboard to Tableau Public
Summary
Chapter 8: Creating an Inventory Dashboard with QlikSense
Getting started with QlikSense Desktop
Developing an inventory dataset with SQL Server
Connecting SQL Server query to QlikSense Desktop
Developing interactive visual components with QlikSense Desktop
Publishing the inventory dashboard
Summary
Chapter 9: Data Analysis with Microsoft SQL Server
Comparing tools head-to-head
Developing views in SQL Server
Performing window functions in SQL Server
Performing stored procedures in SQL Server
Summary

Book Details

ISBN 139781785885433
Paperback352 pages
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