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



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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

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