Data Analytics Made Accessible

Preface to 2020 edition
Chapter 1: Wholeness of Data Analytics
	Introduction
	Business Intelligence
	Caselet: MoneyBall - Data Mining in Sports
	Pattern Recognition
		Types of Patterns
		Finding a Pattern
		Uses of Patterns
	Data Processing Chain
		Data
		Database
		Data Warehouse
		Data Mining
		Data Visualization
	Terminology and Careers
	Organization of the book
	Review Questions
Section 1
Chapter 2: Business Intelligence Concepts and Applications
	Introduction
	Caselet: Khan Academy – BI in Education
	BI for better decisions
	Decision types
	BI Tools
	BI Skills
	BI Applications
		Customer Relationship Management
		Healthcare and Wellness
		Education
		Retail
		Banking
		Financial Services
		Insurance
		Manufacturing
		Telecom
		Public Sector
	Conclusion
	Review Questions
	Liberty Stores Case Exercise: Step 1
Chapter 3: Data Warehousing
	Introduction
	Caselet: University Health System – BI in Healthcare
	Design Considerations for DW
	DW Development Approaches
	DW Architecture
	Data Sources
	Data Loading Processes
	Data Warehouse Design
	DW Access
	DW Best Practices
	Conclusion
	Review Questions
	Liberty Stores Case Exercise: Step 2
Chapter 4: Data Mining
	Introduction
	Caselet: Target Corp – Data Mining in Retail
	Gathering and selecting data
	Data cleansing and preparation
	Outputs of Data Mining
	Evaluating Data Mining Results
	Data Mining Techniques
	Tools and Platforms for Data Mining
	Data Mining Best Practices
	Myths about data mining
	Data Mining Mistakes
	Conclusion
	Review Questions
	Liberty Stores Case Exercise: Step 3
Chapter 5: Data Visualization
	Introduction
	Caselet: Dr Hans Gosling - Visualizing Global Public Health
	Excellence in Visualization
	Types of Charts
	Visualization Example
	Visualization Example phase -2
	Tips for Data Visualization
	Conclusion
	Review Questions
	Liberty Stores Case Exercise: Step 4
Section 2 – Popular Data Mining Techniques
Chapter 6: Decision Trees
	Introduction
	Caselet: Predicting Heart Attacks using Decision Trees
	Decision Tree problem
	Decision Tree Construction
	Lessons from constructing trees
	Decision Tree Algorithms
	Conclusion
	Review Questions
	Liberty Stores Case Exercise: Step 5
Chapter 7: Regression
	Introduction
	Caselet: Data driven Prediction Markets
	Correlations and Relationships
	Visual look at relationships
	Regression Exercise
	Non-linear regression exercise
	Logistic Regression
	Advantages and Disadvantages of Regression Models
	Conclusion
	Review Exercises:
	Liberty Stores Case Exercise: Step 6
Chapter 8: Artificial Neural Networks
	Introduction
	Caselet: IBM Watson - Analytics in Medicine
	Business Applications of ANN
	Design Principles of an Artificial Neural Network
	Representation of a Neural Network
	Architecting a Neural Network
	Developing an ANN
	Advantages and Disadvantages of using ANNs
	Conclusion
	Review Exercises
Chapter 9: Cluster Analysis
	Introduction
	Caselet: Cluster Analysis
	Applications of Cluster Analysis
	Definition of a Cluster
	Representing clusters
	Clustering techniques
	Clustering Exercise
	K-Means Algorithm for clustering
	Selecting the number of clusters
	Advantages and Disadvantages of K-Means algorithm
	Conclusion
	Review Exercises
	Liberty Stores Case Exercise: Step 7
Chapter 10: Association Rule Mining
	Introduction
	Caselet: Netflix: Data Mining in Entertainment
	Business Applications of Association Rules
	Representing Association Rules
	Algorithms for Association Rule
	Apriori Algorithm
	Association rules exercise
	Creating Association Rules
	Conclusion
	Review Exercises
	Liberty Stores Case Exercise: Step 8
Section 3 – Advanced Mining
Chapter 11: Text Mining
	Introduction
	Caselet: WhatsApp and Private Security
	Text Mining Applications
	Text Mining Process
	Term Document Matrix
	Mining the TDM
	Comparing Text Mining and Data Mining
	Text Mining Best Practices
	Conclusion
	Review Questions
	Liberty Stores Case Exercise: Step 9
Chapter 12: Naïve Bayes Analysis
	Introduction
	CASELET: Fraud detection in government contracts
	Probability
	Naïve-Bayes model
	Simple classification example
	Text Classification Example
	Advantages and Disadvantages of Naïve Bayes
	Summary
	Review Questions
Chapter 13: Support Vector Machines
	Introduction
	SVM model
	The Kernel Method
	Advantages and disadvantages
	Summary
	Review Questions
Chapter 14: Web Mining
	Introduction
	Web content mining
	Web structure mining
	Web usage mining
	Web Mining Algorithms
	Conclusion
	Review Questions
Chapter 15: Social Network Analysis
	Introduction
	Caselet: The Social Life of Books
	Applications of SNA
	Network topologies
	Techniques and algorithms
		Finding Sub-networks
		Computing importance of nodes
	PageRank
	Practical considerations
	Comparing SNA with Data Analytics
	Conclusion
	Review Questions
Section 4 - Primers
Chapter 16: Big Data Primer
	Introduction
	Understanding Big Data
	CASELET: IBM Watson: A Big Data system
	Capturing Big Data
		Volume of Data
		Velocity of Data
		Variety of Data
		Veracity of Data
	Benefitting from Big Data
	Management of Big Data
	Organizing Big Data
	Analyzing Big Data
	Technology Challenges for Big Data
		Storing Huge Volumes
		Ingesting streams at an extremely fast pace
		Handling a variety of forms and functions of data
		Processing data at huge speeds
	Conclusion and Summary
	Review Questions
	Liberty Stores Case Exercise: Step P1
Chapter 17: Data Modeling Primer
	Introduction
	Evolution of data management systems
	Relational Data Model
	Implementing the Relational Data Model
	Database management systems (DBMS)
	Structured Query Language
	Conclusion
	Review Questions
Chapter 18: Statistics Primer
	Introduction
	Descriptive Statistics
		Example data set
		Computing Mean, Median, Mode
		Computing the range and variance
		Histograms
		Normal Distribution and Bell Curve
	Inferential Statistics
		Random sampling
		Confidence Interval
	Predictive Statistics
	Summary
	Review Questions
Chapter 19 - Artificial Intelligence Primer
	CASELET: Apple Siri Voice-activated personal assistant
	AI, Machine Learning, and Deep Learning
	The Industrial Revolution
	The Information Revolution
	The Cognitive (or AI) revolution
	Jobs Losses and Gains
	AI and Existential Threat
	Conclusion
	Review Questions
Chapter 20: Data Ownership and Privacy
	Data Ownership
	Data Privacy
		Data Privacy Models
		Chinese Model
		US Model
		European Model
	Summary
Chapter 21: Data Science Careers
	Data Scientist
	Data Engineer
	Data Science aptitude
	Popular Skills
Appendix: R Tutorial for Data Mining
	Getting Started with R
	Installing R
	Working on R
	Import a Dataset in R
	Data visualization
		Plotting a Histogram
		Ploting a Bar Chart
		Ploting charts side by side
	Data Mining Techniques
		Decision Tree
		Correlation
		Regression
		Clustering – Kmeans (Unsupervised Learning)
	Big Data Mining
		WordCloud
	Twitter Mining
		Steps on Twitter side
		R Script
	Page Rank
	Additional Documentation
Appendix: Python Tutorial for Data Mining
1About this Tutorial
2Getting Started
3Installation
4Working on Python
	4.1Windows 7
	4.2Windows 10
	4.3Python Help and Tutorial
	4.4Import a Dataset in Python
	4.5Data visualization –
		4.5.1Ploting a Histogram
		4.5.2Plotting a Bar Chart
		4.5.3Ploting charts side by side
5Data Mining Techniques
	5.1Decision Tree (Supervised Learning)
	5.2Regression (Supervised Learning)
	5.3Correlation (Supervised Learning)
	5.4Clustering – Kmeans (Unsupervised Learning)
6Big Data Mining
	6.1WordCloud - directory FWordCloud and look at code module WordCloud.py.
	6.2Twitter Mining
		6.2.1Steps (Twitter side)
		6.2.2Python code
	6.3Page Rank
7Additional Documentation
Additional Resources
About the Author