Data Analytics

Description

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

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 technology 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 computer science, 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 MODELING
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
BUSINESS INTELLIGENCE
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
GOOGLE 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!

WANT MORE?

Scroll up and grab this helpful guide toady!

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