Predictive Analytics in the Real World

Predictive Analytics in the Real World

 

RELEASE DATE
23-Oct-2015
REGION
North America
Research Code: 9A37-00-23-00-00
SKU: IT00240-NA-SF_01236

$3,950.00

Special Price $2,962.50 save 25 %

In stock
SKU
IT00240-NA-SF_01236

$3,950.00

$2,962.50 save 25 %

DownloadLink

Pay by invoice

ENQUIRE NOW

Description

The field of predictive analytics is one of the most complex and challenging subsets of the Big Data and advanced analytic market. There is also a very wide gap between the practitioners of predictive analytics—most of whom are statisticians, scientific researchers and data scientists—and the people who most want to use predictive analytics for commercial and industrial applications. Bridging this gap with accurate information and clear communication is imperative if predictive applications are to satisfy business users’ expectations. The purpose of this report is to assist in building this bridge: first, by placing predictive analytics in a practical conceptual context for business people; then, by exploring several of the promising solutions that are coming to market.

Table of Contents

The Theoretical World of Models and the Real World of Data

How Theoretical Models are Linked to Real World Data Analysis

Important Differences in the Way in Which We Relate to Real World Models

Dato Targets Developers with Single Code Base from Dev to Production

Nutonian Leads with Symbolic Regression and Genetic Programming

Skytree Optimizes for Enterprise Grade Implementations

  • Dato’s Platform
  • Important Capabilities of the Dato Platform

The Predictive Analytics Process

List of Figures
  • 1. The Theoretical World of Models and the Real World of Data
  • 2. How Theoretical Models are Linked to Real World Data Analysis
  • 3. Dato’s Platform
  • 4. Comparing Eureqa’s Model Generation Approach to Traditional Algorithms
  • 5. Skytree High Level Architecture
  • 6. The Predictive Analytics Process
Related Research
The field of predictive analytics is one of the most complex and challenging subsets of the Big Data and advanced analytic market. There is also a very wide gap between the practitioners of predictive analyticsmost of whom are statisticians, scientific researchers and data scientistsand the people who most want to use predictive analytics for commercial and industrial applications. Bridging this gap with accurate information and clear communication is imperative if predictive applications are to satisfy business users expectations. The purpose of this report is to assist in building this bridge: first, by placing predictive analytics in a practical conceptual context for business people; then, by exploring several of the promising solutions that are coming to market.
More Information
No Index No
Podcast No
Table of Contents | Executive Summary~ | Introduction~ | Mapping the Theoretical World to the Real World~ || The Theoretical World of Models and the Real World of Data~ || How Theoretical Models are Linked to Real World Data Analysis~ || Important Differences in the Way in Which We Relate to Real World Models~ | Predictive Analytic Solutions~ || Dato Targets Developers with Single Code Base from Dev to Production~ ||| Dato’s Platform~ ||| Important Capabilities of the Dato Platform~ || Nutonian Leads with Symbolic Regression and Genetic Programming~ ||| Comparing Eureqa’s Model Generation Approach to Traditional Algorithms~ || Skytree Optimizes for Enterprise Grade Implementations~ ||| Important Capabilities~ ||| Skytree High Level Architecture~ | Putting Predictive Analytics into Practice~ || The Predictive Analytics Process~ | Implications for Buyers and Sellers~ | Last Word~
List of Charts and Figures 1. The Theoretical World of Models and the Real World of Data~ 2. How Theoretical Models are Linked to Real World Data Analysis~ 3. Dato’s Platform~ 4. Comparing Eureqa’s Model Generation Approach to Traditional Algorithms~ 5. Skytree High Level Architecture~ 6. The Predictive Analytics Process~
Author Sandy Borthick
Industries Information Technology
WIP Number 9A37-00-23-00-00
Keyword 1 predictive analytics
Keyword 2 advanced analytic
Keyword 3 Big Data and advanced analytics
Is Prebook No