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  • Book Overview & Buying Introduction to R for Business Intelligence
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Introduction to R for Business Intelligence

Introduction to R for Business Intelligence

By : Gendron
Buy this Book
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Introduction to R for Business Intelligence

Introduction to R for Business Intelligence

By: Gendron
Buy this Book

Overview of this book

Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.
Table of Contents (13 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Extract, Transform, and Load
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1. Extract, Transform, and Load
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Understanding big data in BI analytics
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Extracting data from sources
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Transforming data to fit analytic needs
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Loading data into business systems for analysis
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Summary
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2. Data Cleaning
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2. Data Cleaning
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Summarizing your data for inspection
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Finding and fixing flawed data
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Converting inputs to data types suitable for analysis
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Adapting string variables to a standard
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Summary
3
3. Exploratory Data Analysis
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3. Exploratory Data Analysis
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Understanding exploratory data analysis
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Analyzing a single data variable
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Analyzing two variables together
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Exploring multiple variables simultaneously
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Summary
4
4. Linear Regression for Business
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4. Linear Regression for Business
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Understanding linear regression
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Checking model assumptions
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Using a simple linear regression
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Refining data for simple linear regression
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Introducing multiple linear regression
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Summary
5
5. Data Mining with Cluster Analysis
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5. Data Mining with Cluster Analysis
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Explaining clustering analysis
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Partitioning using k-means clustering
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Clustering using hierarchical techniques
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Summary
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6. Time Series Analysis
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6. Time Series Analysis
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Analyzing time series data with linear regression
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Introducing key elements of time series analysis
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Building ARIMA time series models
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Summary
7
7. Visualizing the Datas Story
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7. Visualizing the Datas Story
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Visualizing data
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Plotting with ggplot2
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Geo-mapping using Leaflet
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Creating interactive graphics using rCharts
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Summary
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8. Web Dashboards with Shiny
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8. Web Dashboards with Shiny
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Creating a basic Shiny app
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Creating a marketing-campaign Shiny app
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Deploying your Shiny app
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Summary
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A. References
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A. References
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B. Other Helpful R Functions
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B. Other Helpful R Functions
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Chapter 1 - Extract, Transform, and Load
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Chapter 2 - Data Cleaning
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C. R Packages Used in the Book
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C. R Packages Used in the Book
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D. R Code for Supporting Market Segment Business Case Calculations
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D. R Code for Supporting Market Segment Business Case Calculations

Preface

 

"Most organizations early on in the data-science learning curve spend most of their time assembling data and not analyzing it. Mature data science organizations realize that in order to be successful they must enable their members to access and use all available data—not some of the data, not a subset, not a sample, but all data. A lawyer wouldn’t go to court with only some of the evidence to support their case—they would go with all appropriate evidence. ...The fundamental building block of a successful and mature data science capability is the ability to ask the right types of questions of the data. This is rooted in the understanding of how the business runs... The mature data science organization has a collaborative culture in which the data science team works side by side with the business to solve critical problems using data. ... [it] includes one or more people with the skills of a data artist and a data storyteller. Stories and visualizations are where we make connections between facts. They enable the listener to understand better the context (What?), the why (So what?), and “what will work” in the future (Now what?)."

 
 --Peter Guerra and Kirk Borne in Ten Signs of Data Science Maturity (2016)

Guerra and Borne (2016) highlight the importance of a diverse and inquisitive team approach to data science. Business intelligence also benefits from this approach. Introduction to R for Business Intelligence gives you a way to explore the world of business intelligence through the eyes of an analyst working in a successful and growing startup company. You will learn R through use cases supporting different business functions.

This book provides data-driven and analytically focused approaches to help you answer business questions in operations, marketing, and finance—a diverse perspective. You will also see how asking the right type of questions and developing the stories and visualizations helps you connect the dots between the data and the business.

 

Enjoy the journey as you code solutions to business intelligence problems using R.

 
 --Jay Gendron
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Tech Concepts
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Programming languages
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Introduction to R for Business Intelligence
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