Data Analytics

Data Analytics Front Cover
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2017-03-08
280 pages

Book Description

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

The Ultimate Guide to Data Science and Analytics

This practical guide is accessible for the reader who is relatively new to the field of data analytics, while still remaining robust and detailed enough to function as a helpful guide to those already experienced in the field. Data science is expanding in breadth and growing rapidly in importance as rapidly integrates ever deeper into business and our daily lives. The need for a succinct and informal guide to this important field has never been greater.

RIGHT NOW you can get ahead of the pack!

This coherent guide covers everything you need to know on the subject of data science, with numerous concrete examples, and invites the reader to dive further into this exciting field. Students from a variety of academic backgrounds, including , business, engineering, statistics, anyone interested in discovering new ideas and insights derived from data can use this as a textbook. At the same time, professionals such as managers, executives, professors, analysts, doctors, developers, computer scientists, accountants, and others can use this book to make a quantum leap in their knowledge of big data in a matter of only a few hours. Learn how to understand this field and uncover actionable insights from data through analytics.

UNDERSTAND the following key insights when you grab your copy today:

  • WHY DATA IS IMPORTANT TO YOUR BUSINESS
  • DATA SOURCES
  • HOW DATA CAN IMPROVE YOUR BUSINESS
  • HOW BIG DATA CREATES VALUE
  • DEVELOPMENT OF BIG DATA
  • CONSIDERING THE PROS AND CONS OF BIG DATA
  • BIG DATA FOR SMALL BUSINESSES
  • THE COST EFFECTIVENESS OF DATA ANALYTICS
  • WHAT TO CONSIDER WHEN PREPARING FOR A NEW BIG DATA SOLUTION
  • DATA GATHERING
  • DATA SCRUBBING
  • DESCRIPTIVE ANALYTICS
  • INFERENTIAL STATISTICS
  • PREDICTIVE ANALYTICS
  • PREDICTIVE MODELS
  • DESCRIPTIVE
  • DECISION MODELING
  • PREDICTIVE ANALYSIS METHODS
  • MACHINE LEARNING TECHNIQUES
  • DATA ANALYSIS WITH "R"
  • ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT (CRM)
  • THE USE OF PREDICTIVE ANALYTICS IN HEALTHCARE
  • THE USE OF PREDICTIVE ANALYTICS IN THE FINANCIAL SECTOR
  • PREDICTIVE ANALYTICS & BUSINESS
  • MARKETING STRATEGIES
  • FRAUD DETECTION
  • SHIPPING BUSINESS
  • CONTROLLING RISK FACTORS
  • THE REVOLUTION OF PREDICTIVE ANALYSIS ACROSS A VARIETY OF INDUSTRIES
  • DESCRIPTIVE AND PREDICTIVE ANALYSIS
  • CRUCIAL FACTORS FOR DATA ANALYSIS
  • RESOURCES AND FLEXIBLE TECHNICAL STRUCTURE
  • HYPER TARGETING
  • WHAT IS DATA SCIENCE?
  • DATA MUNGING
  • DEMYSTIFYING DATA SCIENCE
  • SECURITY RISKS TODAY
  • BIG DATA AND IMPACTS ON EVERYDAY LIFE
  • FINANCE AND BIG DATA
  • APPLYING SENTIMENT ANALYSIS
  • RISK EVALUATION AND THE DATA SCIENTIST
  • THE FINANCE INDUSTRY AND REAL-TIME ANALYTICS
  • HOW BIG DATA IS BENEFICIAL TO THE CUSTOMER
  • CUSTOMER SEGMENTATION IS GOOD FOR BUSINESS
  • USE OF BIG DATA BENEFITS IN MARKETING
  • TRENDS
  • THE PROFILE OF A PERFECT CUSTOMER
  • LEAD SCORING IN PREDICTIVE ANALYSIS
  • EVALUATING THE WORTH OF LIFETIME VALUE
  • BIG DATA ADVANTAGES AND DISADVANTAGES
  • MAKING COMPARISONS WITH COMPETITORS
  • DATA SCIENCE IN THE TRAVEL SECTOR
  • SAFETY ENHANCEMENTS THANKS TO BIG DATA
  • BIG DATA AND AGRICULTURE
  • BIG DATA AND LAW ENFORCEMENT
  • THE USE OF BIG DATA IN THE PUBLIC SECTOR
  • BIG DATA AND GAMING
  • PRESCRIPTIVE ANALYTICS
  • GOOGLE’S “SELF-DRIVING CAR”
  • AND MUCH MORE!

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Table of Contents

Chapter 1: Why Data is Important to Your Business
Chapter 2: Big Data
Chapter 3: Development of Big Data
Chapter 4: Considering the Pros and Cons of Big Data
Chapter 5: Big Data for Small Businesses? Why not?
Chapter 6: Important training for the management of big data
Chapter 7: Steps Taken in Data Analysis
Chapter 8: Descriptive Analytics
Chapter 9: Predictive Analytics
Chapter 10: Predictive Analysis Methods
Chapter 11: R - The Future In Data Analysis Software
Chapter 12: Predictive Analytics & Who Uses It
Chapter 13: Descriptive and predictive analysis
Chapter 14: Crucial factors for data analysis
Chapter 15: Expectations of business intelligence
Chapter 16: What is Data Science?
Chapter 17: Deeper Insights about a Data Scientist’s Skills
Chapter 18: Big Data and the Future
Chapter 19: Finance and Big Data
Chapter 20: Marketers profit by using data science
Chapter 21: Use of big data benefits in marketing
Chapter 22: The Way That Data Science Improves Travel
Chapter 23: How Big Data and Agriculture Feed People
Chapter 24: Big Data and Law Enforcement
Chapter 25: The Use of Big Data in the Public Sector
Chapter 26: Big Data and Gaming
Chapter 27: Prescriptive Analytics

Book Details

  • Title: Data Analytics
  • Author:
  • Length: 280 pages
  • Edition: 1
  • Language: English
  • Publication Date: 2017-03-08
  • ISBN-10: B06XHR9TJC
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