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Provided by: The Modeling Agency Predictive Analytics and Data Mining - Strategic ImplementationData Mining |
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ABOUT THIS COURSE
Data mining is essentially a discovery process -- a process riddled with common yet elusive strategic pitfalls. Project failure is rarely due to poor model development. Rather, data mining projects often fall short of their potential due to flawed or overlooked assessment, business understanding, project definition and strategic planning specifically for information discovery.
If you are looking for an intensive vendor-neutral and non-promotional introduction to data mining best practices and an approach to predictive analytics which is critical to modeling success, then this course is designed for you. There are no prerequisites for this course. However, participants will benefit by reviewing the CRISP-DM guide ahead of the training.
"Predictive Analytics & Data Mining II: Strategic Implementation" offers a concentrated presentation of capabilities, limitations, risks, rewards, use cases, best practices, strategy and lifecycle management. Those in attendance will actively step through the industry standard process for data mining and realize why an advanced degree in statistics, mathematics or computer science is no longer needed to succeed in predictive analytics. Live working sessions reveal real-world obstacles and breakthroughs from which to interpret, learn and apply.
Practitioners seeking to drill down into the tactical implementation of predictive analytics methods may also attend TMA's "Predictive Analytics & Data Mining I: Model Development course. The "Model Development" course is the counterpart to this production within the series, two days immediately preceding this course at the same public venue.
Make sure to view the course series overview page to compare the two primary orientations and target the most fitting agenda for your experience, situation and objectives.
WHO SHOULD ATTEND
IT/ IS EXECUTIVES AND MANAGERS: CIOs, CKOs, CTOs, Stakeholders, Functional Officers, Technical Directors and Project Managers
LINE-OF-BUSINESS EXECUTIVES AND FUNCTIONAL MANAGERS: Risk Managers, Customer Relationship Managers, Business Forecasters, Inventory Flow Analysts, Financial Forecasters, Direct Marketing Analysts, Medical Diagnostic Analysts, eCommerce Company Executives
TECHNOLOGY PLANNERS: Who survey emerging technologies in order to prioritize corporate investment
CONSULTANTS: Whose competitive environment is intensifying and whose success requires competency with data mining and related emerging information technologies
BENEFITS OF ATTENDING
- Make better business decisions based on information hidden within your data
- Develop a strong vocabulary and understanding of data mining terminology
- Communicate with confidence among your developers and consultants
- Plan and manage your data mining projects effectively from the start
- Experience firsthand that actual model-building is not as complicated as it might have seemed through the lecture segments
- Leave with resources, contacts and actionable plans to substantially reduce your project preparation time, costs and risks
THE BUSINESS CHALLENGE
Traditionally, organizations use data tactically - to manage operations. For competitive edge, leading organizations use data strategically - to expand the business, to improve profitability, to reduce costs, anticipate behavior, and market more effectively. The mining of data for predictive indicators creates information assets that an organization can leverage to achieve these strategic objectives.
Predictive analytics is a data-driven extension to an enterprise's decision support system (DSS) architecture. It complements and interlocks with other DSS capabilities such as query and reporting, on-line analytical processing (OLAP), data visualization, and traditional statistical analysis. These other DSS technologies are generally retrospective.
The predictive aspect of data mining may be defined as "the data-driven discovery and modeling of hidden patterns in large volumes of data." Predictive analytics differs from the retrospective technologies above because it produces models -- models that capture and represent hidden patterns and interactions in the data. Via data mining, a user can discover patterns and build models automatically, without knowing exactly what s/ he's looking for.
The resulting models are both descriptive and prospective. They address why things happened and what is likely to happen next. A user can pose "what-if" questions to a data-mining model that cannot be queried directly from the database or warehouse. Examples include: "What is the expected lifetime value of every customer account," "Which customers are likely to open a money market account," or "How will production quality be affected if various resources are adjusted?"
WHAT YOU WILL LEARN
- Basic principles and terminology for predictive analytics
- Who is utilizing predictive analytics, and why
- What are common project pitfalls and how to avoid them
- How to define business objectives for a discovery process
- Project deployment, performance and maintenance issues
- Building confidence through hands-on participation
- How to get started
WHAT MAKES THIS COURSE UNIQUE
This course offers a balanced and non-promotional presentation of data mining topics and its role in enterprise decision support. The instructor has been deeply involved with the design, development and deployment of real-world data mining solutions.
This course does not drill deeply into specific algorithms or technical implementation issues. For a comprehensive presentation of model development methodology and techniques, refer to the "Predictive Analytics & Data Mining I: Model Development" course which directly precedes this event at public venues. This level in the series presents strategic and process challenges that are critical to the success of deploying applied models in real world business environments.
Leading commercial and open-source products will be used from a vendor-neutral perspective to illustrate and compare methods -- not to showcase tools. Results are drawn from actual data mining applications and interpreted in the context of business impact. Attendees will depart with a binder full of slides, supporting notes, hands-on experience, a valuable index of data mining resources and certification upon attending the full series and passing an on-line exam.
Data mining is essentially a discovery process -- a process riddled with common yet elusive strategic pitfalls. Project failure is rarely due to poor model development. Rather, data mining projects often fall short of their potential due to flawed or overlooked assessment, business understanding, project definition and strategic planning specifically for information discovery.
If you are looking for an intensive vendor-neutral and non-promotional introduction to data mining best practices and an approach to predictive analytics which is critical to modeling success, then this course is designed for you. There are no prerequisites for this course. However, participants will benefit by reviewing the CRISP-DM guide ahead of the training.
"Predictive Analytics & Data Mining II: Strategic Implementation" offers a concentrated presentation of capabilities, limitations, risks, rewards, use cases, best practices, strategy and lifecycle management. Those in attendance will actively step through the industry standard process for data mining and realize why an advanced degree in statistics, mathematics or computer science is no longer needed to succeed in predictive analytics. Live working sessions reveal real-world obstacles and breakthroughs from which to interpret, learn and apply.
Practitioners seeking to drill down into the tactical implementation of predictive analytics methods may also attend TMA's "Predictive Analytics & Data Mining I: Model Development course. The "Model Development" course is the counterpart to this production within the series, two days immediately preceding this course at the same public venue.
Make sure to view the course series overview page to compare the two primary orientations and target the most fitting agenda for your experience, situation and objectives.
WHO SHOULD ATTEND
IT/ IS EXECUTIVES AND MANAGERS: CIOs, CKOs, CTOs, Stakeholders, Functional Officers, Technical Directors and Project Managers
LINE-OF-BUSINESS EXECUTIVES AND FUNCTIONAL MANAGERS: Risk Managers, Customer Relationship Managers, Business Forecasters, Inventory Flow Analysts, Financial Forecasters, Direct Marketing Analysts, Medical Diagnostic Analysts, eCommerce Company Executives
TECHNOLOGY PLANNERS: Who survey emerging technologies in order to prioritize corporate investment
CONSULTANTS: Whose competitive environment is intensifying and whose success requires competency with data mining and related emerging information technologies
BENEFITS OF ATTENDING
- Make better business decisions based on information hidden within your data
- Develop a strong vocabulary and understanding of data mining terminology
- Communicate with confidence among your developers and consultants
- Plan and manage your data mining projects effectively from the start
- Experience firsthand that actual model-building is not as complicated as it might have seemed through the lecture segments
- Leave with resources, contacts and actionable plans to substantially reduce your project preparation time, costs and risks
THE BUSINESS CHALLENGE
Traditionally, organizations use data tactically - to manage operations. For competitive edge, leading organizations use data strategically - to expand the business, to improve profitability, to reduce costs, anticipate behavior, and market more effectively. The mining of data for predictive indicators creates information assets that an organization can leverage to achieve these strategic objectives.
Predictive analytics is a data-driven extension to an enterprise's decision support system (DSS) architecture. It complements and interlocks with other DSS capabilities such as query and reporting, on-line analytical processing (OLAP), data visualization, and traditional statistical analysis. These other DSS technologies are generally retrospective.
The predictive aspect of data mining may be defined as "the data-driven discovery and modeling of hidden patterns in large volumes of data." Predictive analytics differs from the retrospective technologies above because it produces models -- models that capture and represent hidden patterns and interactions in the data. Via data mining, a user can discover patterns and build models automatically, without knowing exactly what s/ he's looking for.
The resulting models are both descriptive and prospective. They address why things happened and what is likely to happen next. A user can pose "what-if" questions to a data-mining model that cannot be queried directly from the database or warehouse. Examples include: "What is the expected lifetime value of every customer account," "Which customers are likely to open a money market account," or "How will production quality be affected if various resources are adjusted?"
WHAT YOU WILL LEARN
- Basic principles and terminology for predictive analytics
- Who is utilizing predictive analytics, and why
- What are common project pitfalls and how to avoid them
- How to define business objectives for a discovery process
- Project deployment, performance and maintenance issues
- Building confidence through hands-on participation
- How to get started
WHAT MAKES THIS COURSE UNIQUE
This course offers a balanced and non-promotional presentation of data mining topics and its role in enterprise decision support. The instructor has been deeply involved with the design, development and deployment of real-world data mining solutions.
This course does not drill deeply into specific algorithms or technical implementation issues. For a comprehensive presentation of model development methodology and techniques, refer to the "Predictive Analytics & Data Mining I: Model Development" course which directly precedes this event at public venues. This level in the series presents strategic and process challenges that are critical to the success of deploying applied models in real world business environments.
Leading commercial and open-source products will be used from a vendor-neutral perspective to illustrate and compare methods -- not to showcase tools. Results are drawn from actual data mining applications and interpreted in the context of business impact. Attendees will depart with a binder full of slides, supporting notes, hands-on experience, a valuable index of data mining resources and certification upon attending the full series and passing an on-line exam.
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Certificate Program
Provided by The Modeling Agency
Predictive Analytics and Data Mining - Strategic Implementation Seminar Schedule
| Location | ||||
|---|---|---|---|---|
August, 2011 | ||||
| 17th Aug | Minneapolis | [Register] | ||
September, 2011 | ||||
| 21st Sep | Washington, DC | [Register] | ||
November, 2011 | ||||
| 16th Nov | Dallas | [Register] | ||
December, 2011 | ||||
| 7th Dec | Los Angeles, CA | [Register] | ||
February, 2012 | ||||
| 8th Feb | Atlanta, GA | [Register] | ||
Predictive Analytics and Data Mining - Strategic Implementation
COURSE OUTLINE
INTRODUCTION
What is predictive analytics?
Goal driven analysis of large data sets...
to identify an approach for allocating organizational resources
that enhances performance on the organization's self-defined
performance metrics
to better achieve the organization's business objectives
using a repeatable, consistent strategy
Beyond traditional statistics
Shift your thinking
The goal of modeling
Physical systems
Human behavior
Behaviors of interest
Setting up the game
Project team
Phased development cycle
Definitions
Data sandbox
Formulas vs. Model development
The conflict between algorithm objectives and business objectives
Why use predictive analytics?
Definition of data mining
What data mining is not
Why mine data?
The key question is "so what?"
Successful data mining is goal-directed analysis
Traditional statistics are insufficient in today's world
What can data mining do?
Data mining opportunities
Data mining business goals
Data mining analytic goals
Why the majority of data mining projects fail
How much data is needed to develop a model?
How many variables?
Rules of thumb
Types of sampling
Experimental design
Data sets used
Types of data distribution
Types of decision
Predictive analytics key technologies overview*
* Methods and techniques are detailed in the Model Development course
Who needs brains when you've got software?
Low-Risk / High-ROI project design
The business justification for predictive analytics: Goal driven analytics
Organizational predictive analytics opportunity identification
Incremental project design
Single-tailed model development: Identify positive impacts
Single-tailed model development: Identify negative impacts
Two-tailed model development: Conflict resolution
Ranking across the continuum: Adding resolution
Subdividing dimensions: Adding detail
Forecasting model development
A 'real world' standardized development process:
The CRoss-Industry Standard Process for Data Mining (CRISP-DM)
USE CASE WORKSHOP #1
Implement CRISP-DM for a single-tailed model
Business Understanding (CRISP 1)
Determine business objective
Background and business objectives
Identify decision process
Business success criteria
Identify performance metrics
Calculate current baseline levels of performance
Determine modeling objectives
Requirements
Assumptions
Constraints
Risks and contingencies
Terminology
Costs and benefits
Modeling goals
Modeling success criteria
Assess resource availability
Hardware resources
Sources of data and knowledge
Personnel sources
Produce project plan
Prepare Business Understanding Deliverables
Data Understanding (CRISP 2)
Review data availability
Collect initial data
Data requirements planning
Selection criteria
Insertion of data
Construction of Output variable
Describe data
Volumetric analysis of data
Attribute types and values
Keys
Review assumptions and goals
Explore data
Statistical analysis
Data exploration
Suppositions for future analysis
Verify data quality
Data Understanding Deliverables
Initial data collection report
Data description report
Data exploration report
Data quality report
( Note: CRISP-DM Parts 3, 4 and 5 are detailed in the "Model Development"
course and extended into practice in this course. It is helpful but not necessary
to have had the tactical drill-down into these Parts prior to their implementation. )
Data Preparation (CRISP-DM 3)
Modeling (CRISP-DM 4)
Evaluation (CRISP-DM 5)
Deployment (CRISP 6)
Plan deployment
Develop monitoring and maintenance plan
Produce final report
Project review
Deliverables
Deployment plan
Monitoring and maintenance plan
Final report
USE CASE WORKSHOP #2
Second CRISP-DM pass for a two-tailed model implementation
Business Understanding (CRISP-DM 1)
Data Understanding (CRISP-DM 2)
Data Preparation (CRISP-DM 3)
Modeling (CRISP-DM 4)
Evaluation (CRISP-DM 5)
Deployment (CRISP 6)
EXTENDED MODELING TOPICS
WRAP-UP AND NEXT STEPS
PA/ DM Level I Course: "Model Development"
Certification Exam (for those who complete the series)
Product training courses
Keep learning
Supplementary materials and resources
Conferences and communities
Get started on a project
INTRODUCTION
What is predictive analytics?
Goal driven analysis of large data sets...
to identify an approach for allocating organizational resources
that enhances performance on the organization's self-defined
performance metrics
to better achieve the organization's business objectives
using a repeatable, consistent strategy
Beyond traditional statistics
Shift your thinking
The goal of modeling
Physical systems
Human behavior
Behaviors of interest
Setting up the game
Project team
Phased development cycle
Definitions
Data sandbox
Formulas vs. Model development
The conflict between algorithm objectives and business objectives
Why use predictive analytics?
Definition of data mining
What data mining is not
Why mine data?
The key question is "so what?"
Successful data mining is goal-directed analysis
Traditional statistics are insufficient in today's world
What can data mining do?
Data mining opportunities
Data mining business goals
Data mining analytic goals
Why the majority of data mining projects fail
How much data is needed to develop a model?
How many variables?
Rules of thumb
Types of sampling
Experimental design
Data sets used
Types of data distribution
Types of decision
Predictive analytics key technologies overview*
* Methods and techniques are detailed in the Model Development course
Who needs brains when you've got software?
Low-Risk / High-ROI project design
The business justification for predictive analytics: Goal driven analytics
Organizational predictive analytics opportunity identification
Incremental project design
Single-tailed model development: Identify positive impacts
Single-tailed model development: Identify negative impacts
Two-tailed model development: Conflict resolution
Ranking across the continuum: Adding resolution
Subdividing dimensions: Adding detail
Forecasting model development
A 'real world' standardized development process:
The CRoss-Industry Standard Process for Data Mining (CRISP-DM)
USE CASE WORKSHOP #1
Implement CRISP-DM for a single-tailed model
Business Understanding (CRISP 1)
Determine business objective
Background and business objectives
Identify decision process
Business success criteria
Identify performance metrics
Calculate current baseline levels of performance
Determine modeling objectives
Requirements
Assumptions
Constraints
Risks and contingencies
Terminology
Costs and benefits
Modeling goals
Modeling success criteria
Assess resource availability
Hardware resources
Sources of data and knowledge
Personnel sources
Produce project plan
Prepare Business Understanding Deliverables
Data Understanding (CRISP 2)
Review data availability
Collect initial data
Data requirements planning
Selection criteria
Insertion of data
Construction of Output variable
Describe data
Volumetric analysis of data
Attribute types and values
Keys
Review assumptions and goals
Explore data
Statistical analysis
Data exploration
Suppositions for future analysis
Verify data quality
Data Understanding Deliverables
Initial data collection report
Data description report
Data exploration report
Data quality report
( Note: CRISP-DM Parts 3, 4 and 5 are detailed in the "Model Development"
course and extended into practice in this course. It is helpful but not necessary
to have had the tactical drill-down into these Parts prior to their implementation. )
Data Preparation (CRISP-DM 3)
Modeling (CRISP-DM 4)
Evaluation (CRISP-DM 5)
Deployment (CRISP 6)
Plan deployment
Develop monitoring and maintenance plan
Produce final report
Project review
Deliverables
Deployment plan
Monitoring and maintenance plan
Final report
USE CASE WORKSHOP #2
Second CRISP-DM pass for a two-tailed model implementation
Business Understanding (CRISP-DM 1)
Data Understanding (CRISP-DM 2)
Data Preparation (CRISP-DM 3)
Modeling (CRISP-DM 4)
Evaluation (CRISP-DM 5)
Deployment (CRISP 6)
EXTENDED MODELING TOPICS
WRAP-UP AND NEXT STEPS
PA/ DM Level I Course: "Model Development"
Certification Exam (for those who complete the series)
Product training courses
Keep learning
Supplementary materials and resources
Conferences and communities
Get started on a project
About The Training Provider: The Modeling Agency
The Modeling Agency - The Modeling Agency (TMA) is a structured, redundant network of senior-level data mining consultants, associate modelers, application programmers, project managers and technical documentation specialists. The Modeling Agency (TMA) provides data mining guidance and results for those who are data-rich, yet information-poor through a structured network of independent consultants, or agents.
The...
