Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life

Document Outline

• Introduction
• Chapter 1: Why Data is Important to Your Business
• Data Sources
• How Data Can Improve Your Business
• Chapter 2: Big Data
• Big Data – A New Advantage
• Big Data Creates Value
• Big Data is a Big Deal
• Chapter 3: Development of Big Data
• Chapter 4: Considering the Pros and Cons of Big Data
• The Pros
• New methods of generating profit
• Improving Public Health
• Improving Our Daily Environment
• Improving Decisions: Speed and Accuracy
• Personalized Products and Services
• The Cons
• Privacy
• Big Brother
• Stifling Entrepreneurship
• Data Safekeeping
• Erroneous Data Sets and Flawed Analyses
• Conclusions
• Chapter 5: Big Data for Small Businesses? Why not?
• The Cost Effectiveness of Data Analytics
• Big Data can be for Small Businesses Too
• Where can Big Data improve the Cost Effectiveness of Small Businesses?
• What to consider when preparing for a New Big Data Solution
• Chapter 6: Important training for the management of big data
• Present level of skill in managing data
• Where big data training is necessary
• The Finance department
• The Human Resources department
• The supply and logistics department
• The Operations department
• The Marketing department
• The Data Integrity, Integration and Data Warehouse department
• The Legal and Compliance department
• Chapter 7: Steps Taken in Data Analysis
• Defining Data Analysis
• Actions Taken in the Data Analysis Process
• Phase 1: Setting of Goals
• Phase 2: Clearly Setting Priorities for Measurement
• Determine What You’re Going to be Measuring
• Choose a Measurement Method
• Phase 3: Data Gathering
• Phase 4: Data Scrubbing
• Phase 5: Analysis of Data
• Phase 6: Result Interpretation
• Interpret the Data Precisely
• Chapter 8: Descriptive Analytics
• Descriptive Analytics- What is It?
• How Can Descriptive Analysis Be Used?
• Measures in Descriptive Statistics
• Inferential Statistics
• Chapter 9: Predictive Analytics
• Defining Predictive Analytics
• Different Kinds of Predictive Analytics
• Predictive Models
• Descriptive Modeling
• Decision Modeling
• Chapter 10: Predictive Analysis Methods
• Machine Learning Techniques
• Regression Techniques
• Linear Regression
• Logistic Regression
• The Probit Model
• Neural Networks
• Radial Basis Function Networks
• Support Vector Machines
• Naive Bayes
• Instance-Based Learning
• Geospatial Predictive Modeling
• Hitachi’s Predictive Analytic Model
• Predictive Analytics in the Insurance Industry
• Chapter 11: R - The Future In Data Analysis Software
• Is R A Good Choice?
• Types of Data Analysis Available with R
• Is There Other Programming Language Available?
• Chapter 12: Predictive Analytics & Who Uses It
• Analytical Customer Relationship Management (CRM)
• The Use Of Predictive Analytics In Healthcare
• The Use Of Predictive Analytics In The Financial Sector
• Predictive Analytics & Business
• Keeping Customers Happy
• Marketing Strategies
• *Fraud Detection
• Processes
• Insurance Industry
• Shipping Business
• Controlling Risk Factors
• Staff Risk
• Underwriting and Accepting Liability
• Freedom Specialty Insurance: An Observation of Predictive Analytics Used in Underwriting
• Positive Results from the Model
• The Effects of Predictive Analytics on Real Estate
• The National Association of Realtors (NAR) and Its Use of Predictive Analytics
• The Revolution of Predictive Analysis across a Variety of Industries
• Chapter 13: Descriptive and predictive analysis
• Chapter 14: Crucial factors for data analysis
• Support by top management
• Resources and flexible technical structure
• Change management and effective involvement
• Strong IT and BI governance
• Alignment of BI with business strategy
• Chapter 15: Expectations of business intelligence
• Advances in technologies
• Hyper targeting
• The possibility of big data getting out of hand
• Making forecasts without enough information
• Sources of information for data management
• Chapter 16: What is Data Science?
• Skills Required for Data Science
• Mathematics
• Technology and Hacking
• Business Acumen
• What does it take to be a data scientist?
• Data Science, Analytics, and Machine Learning
• Data Munging
• Chapter 17: Deeper Insights about a Data Scientist’s Skills
• Demystifying Data Science
• Data Scientists in the Future
• Chapter 18: Big Data and the Future
• Online Activities and Big Data
• The Value of Big Data
• Security Risks Today
• Big Data and Impacts on Everyday Life
• Chapter 19: Finance and Big Data
• How a Data Scientist Works
• Understanding More Than Numbers
• Applying Sentiment Analysis
• Risk Evaluation and the Data Scientist
• Reduced Online Lending Risk
• The Finance Industry and Real-Time Analytics
• How Big Data is Beneficial to the Customer
• Customer Segmentation is Good for Business
• Chapter 20: Marketers profit by using data science
• Reducing costs to increasing revenue
• Chapter 21: Use of big data benefits in marketing
• Google Trends does all the hard work
• The profile of a perfect customer
• Ascertaining correct big data content
• Lead scoring in predictive analysis
• Geolocations are no longer an issue
• Evaluating the worth of lifetime value
• Big data advantages and disadvantages
• Making comparisons with competitors
• Patience is important when using big data
• Chapter 22: The Way That Data Science Improves Travel
• Data Science in the Travel Sector
• Travel Offers Can be personalized because of Big Data
• Safety Enhancements Thanks to Big Data
• How Up-Selling and Cross-Selling Use Big Data
• Chapter 23: How Big Data and Agriculture Feed People
• How to Improve the Value of Every Acre
• One of the Best Uses of Big Data
• How Trustworthy is Big Data?
• Can the Colombian Rice Fields be saved by Big Data?
• Up-Scaling
• Chapter 24: Big Data and Law Enforcement
• Data Analytics, Software Companies, and Police Departments: A solution?
• Analytics Decrypting Criminal Activities
• Enabling Rapid Police Response to Terrorist Attacks
• Chapter 25: The Use of Big Data in the Public Sector
• United States Government Applications of Big Data
• Data Security Issues
• The Data Problems of the Public Sector
• Chapter 26: Big Data and Gaming
• Big Data and Improving Gaming Experience
• Big Data in the Gambling Industry
• Gaming the System
• The Expansion of Gaming
• Chapter 27: Prescriptive Analytics
• Prescriptive Analytics- What is It?
• What Are its Benefits?
• What is its Future?
• Google’s “Self-Driving Car”
• Prescriptive Analytics in the Oil and Gas Industry
• Prescriptive Analytics and the Travel Industry
• Prescriptive Analytics in the Healthcare Industry
• Data Analysis and Big Data Glossary
• A
• B
• C
• D
• E
• F
• G
• H
• I
• K
• L
• M
• N
• O
• P
• Q
• R
• S
• T
• U
• V
• Conclusion