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Provided by: The Modeling Agency Predictive Analytics and Data Mining - Model DevelopmentData Mining |
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ABOUT THIS COURSE
The Modeling Agency's "Model Development" course presents a deep dive into the data mining process at a tactical level. Attendees will observe demonstrations of machine learning methods and computer-guided analytical techniques for extracting and interpreting complex patterns and relationships from large volumes of data. If you desire an intensive tactical orientation to data mining concepts, tools, techniques and supporting methods, then this event is designed for you.
This vendor-neutral course broadly covers data-driven information discovery techniques and model-building tactics without restriction to any particular modeling tool. Popular open-source and commercial packages are leveraged to illustrate methods, but not to showcase the tools.
There are no prerequisites for this course. However, participants will benefit by reviewing the CRISP-DM guide ahead of the training.
Each course in the series is designed to be taken independently or as a natural progression from tactics to strategy and practice. View the course series overview page to compare the two primary orientations and target the most fitting agenda for your experience, situation and objectives.
http://www. the-modeling-agency. com/ training/ series. html
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
- Vendor-neutral exposure to tools and techniques that will place you months ahead in method planning and product surveying
- Examine which methods and tools are most effective for your needs
- Avoid pitfalls in data preparation, modeling, and results interpretation
- Leave with resources, contacts and actionable plans to substantially increase your analysis capabilities while minimizing dead ends
THE BUSINESS CHALLENGE
The rapid emergence of electronic data processing and collection methods has lead some to call recent times as the "Information Age." However, it may be more accurately termed as "The Age of the Data Glut." Most businesses either posses a large database or have access to one. These databases contain so much data that it becomes very difficult to understand just what that data is telling us.
There is hardly a transaction that does not generate a computer record somewhere. All this data has meaning with respect to making better business decisions or understanding customer needs and preferences. But how do you discover those needs and preferences in a database that contains gigabits of seemingly incomprehensible numbers and facts? Data mining and predictive analytics does just that.
The intent of this course is to offer attendees a stronger grasp of data mining techniques, a solid understanding of how various methods and tools apply to different kinds of data intensive problems, and how to overcome limitations that cause predictive models to underperform.
WHAT YOU WILL LEARN
- The data mining process and general implementation
- How to prepare raw data and benefit from visualization
- Various data mining methods and how they compare
- Advanced model building techniques
- Results analysis and validation
- Technology and product selection
- Solution integration, ongoing performance and maintenance
- Where to begin and how to obtain resources and support
WHAT MAKES THIS COURSE UNIQUE
This course does not restrict or skew the presentation of data mining methods through a single product. Rather, the course gives consideration to all resources from a vendor-neutral position. The instructor possesses a wealth of pragmatic experience in applying data mining technology across industries in real-world applications. This course insists upon making predictive analytics constructive and interpretable in a business or organizational setting.
In addition, live modeling demonstrations projected from the presenter's machine will support the instructional sessions. The demonstrations will highlight superior performance as well as pitfalls. The instructor will show how to evaluate various packages based on strengths, limitations, value and general performance.
The Modeling Agency's "Model Development" course presents a deep dive into the data mining process at a tactical level. Attendees will observe demonstrations of machine learning methods and computer-guided analytical techniques for extracting and interpreting complex patterns and relationships from large volumes of data. If you desire an intensive tactical orientation to data mining concepts, tools, techniques and supporting methods, then this event is designed for you.
This vendor-neutral course broadly covers data-driven information discovery techniques and model-building tactics without restriction to any particular modeling tool. Popular open-source and commercial packages are leveraged to illustrate methods, but not to showcase the tools.
There are no prerequisites for this course. However, participants will benefit by reviewing the CRISP-DM guide ahead of the training.
Each course in the series is designed to be taken independently or as a natural progression from tactics to strategy and practice. View the course series overview page to compare the two primary orientations and target the most fitting agenda for your experience, situation and objectives.
http://www. the-modeling-agency. com/ training/ series. html
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
- Vendor-neutral exposure to tools and techniques that will place you months ahead in method planning and product surveying
- Examine which methods and tools are most effective for your needs
- Avoid pitfalls in data preparation, modeling, and results interpretation
- Leave with resources, contacts and actionable plans to substantially increase your analysis capabilities while minimizing dead ends
THE BUSINESS CHALLENGE
The rapid emergence of electronic data processing and collection methods has lead some to call recent times as the "Information Age." However, it may be more accurately termed as "The Age of the Data Glut." Most businesses either posses a large database or have access to one. These databases contain so much data that it becomes very difficult to understand just what that data is telling us.
There is hardly a transaction that does not generate a computer record somewhere. All this data has meaning with respect to making better business decisions or understanding customer needs and preferences. But how do you discover those needs and preferences in a database that contains gigabits of seemingly incomprehensible numbers and facts? Data mining and predictive analytics does just that.
The intent of this course is to offer attendees a stronger grasp of data mining techniques, a solid understanding of how various methods and tools apply to different kinds of data intensive problems, and how to overcome limitations that cause predictive models to underperform.
WHAT YOU WILL LEARN
- The data mining process and general implementation
- How to prepare raw data and benefit from visualization
- Various data mining methods and how they compare
- Advanced model building techniques
- Results analysis and validation
- Technology and product selection
- Solution integration, ongoing performance and maintenance
- Where to begin and how to obtain resources and support
WHAT MAKES THIS COURSE UNIQUE
This course does not restrict or skew the presentation of data mining methods through a single product. Rather, the course gives consideration to all resources from a vendor-neutral position. The instructor possesses a wealth of pragmatic experience in applying data mining technology across industries in real-world applications. This course insists upon making predictive analytics constructive and interpretable in a business or organizational setting.
In addition, live modeling demonstrations projected from the presenter's machine will support the instructional sessions. The demonstrations will highlight superior performance as well as pitfalls. The instructor will show how to evaluate various packages based on strengths, limitations, value and general performance.
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Certificate Program
Provided by The Modeling Agency
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- asked: I am presently a student of engineering I want to know about the various avenues whaer I can do my summer trining. my Branch is applied electronics & Insrumentation
- asked: I am presently a student of engineering I want to know about the various avenues whaer I can do my summer trining. my Branch is applied electronics & Insrumentation
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Predictive Analytics and Data Mining - Model Development Seminar Schedule
| Location | ||||
|---|---|---|---|---|
August, 2011 | ||||
| 15th Aug | Minneapolis | [Register] | ||
September, 2011 | ||||
| 19th Sep | Washington, DC | [Register] | ||
November, 2011 | ||||
| 14th Nov | Dallas | [Register] | ||
December, 2011 | ||||
| 5th Dec | Los Angeles, CA | [Register] | ||
February, 2012 | ||||
| 6th Feb | Atlanta, GA | [Register] | ||
Predictive Analytics and Data Mining - Model Development
COURSE OUTLINE
SESSION I - Strategic Overview
INTRODUCTION
What you will get in this course
What is PA/ DM?
Definition
Related terms and fields
Machine learning
Computer-aided pattern discovery
Business analytics and statistics
Others you have heard?
Examples
Differences
How can you develop PA/ DM opportunities
Generative questions
Examples
Nuts and bolts of a project
Big Picture: Introduction to CRISP-DM
What is it? What is it not?
Why do we care? Why use it? What is it good for?
Example: Tour of CRISP-DM in real-world context
Team Exercise
One Practitioner's View
Regarding PA/ DM: What's hype and what isn't?
How to be successful with PA/ DM
Tools and products
People matter
CRISP-DM METHODOLOGY: Parts 3, 4, 5
Highlight CRISP-DM 1, 2, 6
CRISP 1, 2, 6 are detailed in "Level II: Strategic Implementation"
Business understanding
Data understanding
Deployment
Data Preparation (CRISP 3)
Rows: Select data
How much data?
Rows: Selecting the "unit of analysis"
Determine what the record will look like
Determine how many records we have to work with
Site selection example
Rows: Defining the population / outcome of interest
Rows: Sampling methods / oversampling
Rows: Exclusions / rules of thumb
Columns: Identifying types
Need definitions (from clients or internal) so that we
understand what the data represents. Don't assume
that an element isn't important
Categorical / Nominal (what does null mean)?
Ordinal
Interval / Rational
Date / Time
Sub-Types (money, count, geo, id, etc, and why care?)
Columns: Appropriate statistics and visualizations
Univariate
Multivariate
Columns: Selection for modeling
See "Clean Data" for pre-modeling elimination of
redundant, constant, etc columns
Final selection is done during the Modeling phase
Document the above in a "Scorecard"
Modeling (CRISP 4)
Select modeling technique
Taxonomies: An overview
Supervised vs. Unsupervised
Descriptive vs. Predictive
Classification vs. Estimation
Supervised -- Constellation of methods with pros and cons
Classification
Decision Trees
Logistic Regression
Neural Networks
K-Nearest Neighbor
Prediction
Linear Regression
Neural Networks
MARS
Exercise: Scenario revisited -- What method(s) do we choose?
Unsupervised -- More methods with pros and cons
Segmentation / Clustering
Hierarchical clustering
K-Means
Decision trees
Association Rules
Team Exercise: Com up with an expert-derived decision tree to
make a selection for supervised problems
Advanced Topics
Ensembles
Bagging
Boosting
Parting remarks
Models should be as simple as possible, but no simpler
Why not both? (a low-res descriptive model and
a high-res opaque accuracy model)
Generate test design
Data segregation
Performance metrics: Whenever possible, go for the
custom metric -- "If you build it, they will come."
Build Model
Use a tool, select a method, set parameters (if any),
select candidate columns, select outcome (if supervised)
Variable selection techniques for supervised methods
Variable selection techniques for unsupervised methods
Assess Model (Tweaking)
Predictors
Manually removing or limiting
Forcing predictors
Structure
Profiles
Compared to What?
Baseline model comparison
Train/ Test/ Validation comparison
Scoring the model
What does scoring mean?
How is it different from building the model?
What are we looking for when scoring?
Final Product
Model(s)
Description(s)
Evaluation (CRISP 5)
Evaluate results (from business perspective)
Prelude to business use presentation
Informal, low-risk setting
Poke holes early, before business presentation
Does the model or segmentation make sense?
Does it contradict of reinforce the standard "lore"?
SWOT analysis: What are the strengths, weaknesses,
opportunities and threats?
Get support and buy-in from potential champions
Candidate names for segments
Present results to business users or clients
BUs need to be convinced: Models, segments and analysis
need to be marketed
Deployment will require change
To processes
To systems
To ingrained mindsets
Deployment costs (to each change area above)
Results must have business value, not technical representations
Performance results -- in business terms
Descriptions
No equations
Tell the story, paint the vision, what will life be like with
or without the model in place?
Review Process
Follow-ups to the presentation
Anticipate follow-up issues in planning and esimates
Revisions to the model(s) or segments based upon feedack
Final quality assurance
Determine next steps
Are you done?
Will the model(s) be deployed? Why or why not?
Document
Lessons learned meeting
Final Product
Consulting Exercise
WRAP-UP AND PARTING THOUGHTS
Final Q&A
Springboard exercise: "The boss in the elevator"
PA/ DM Philosophy
Understand the problem
Understand the data
Then, think about how to solve it (Einstein quote)
Work on problems with specific business goals,
specific hypotheses to be tested. Do NOT go
prospecting for "data mining nuggets."
Next Steps
PA/ DM Level II Course: "Strategic Implementation"
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
SESSION I - Strategic Overview
INTRODUCTION
What you will get in this course
What is PA/ DM?
Definition
Related terms and fields
Machine learning
Computer-aided pattern discovery
Business analytics and statistics
Others you have heard?
Examples
Differences
How can you develop PA/ DM opportunities
Generative questions
Examples
Nuts and bolts of a project
Big Picture: Introduction to CRISP-DM
What is it? What is it not?
Why do we care? Why use it? What is it good for?
Example: Tour of CRISP-DM in real-world context
Team Exercise
One Practitioner's View
Regarding PA/ DM: What's hype and what isn't?
How to be successful with PA/ DM
Tools and products
People matter
CRISP-DM METHODOLOGY: Parts 3, 4, 5
Highlight CRISP-DM 1, 2, 6
CRISP 1, 2, 6 are detailed in "Level II: Strategic Implementation"
Business understanding
Data understanding
Deployment
Data Preparation (CRISP 3)
Rows: Select data
How much data?
Rows: Selecting the "unit of analysis"
Determine what the record will look like
Determine how many records we have to work with
Site selection example
Rows: Defining the population / outcome of interest
Rows: Sampling methods / oversampling
Rows: Exclusions / rules of thumb
Columns: Identifying types
Need definitions (from clients or internal) so that we
understand what the data represents. Don't assume
that an element isn't important
Categorical / Nominal (what does null mean)?
Ordinal
Interval / Rational
Date / Time
Sub-Types (money, count, geo, id, etc, and why care?)
Columns: Appropriate statistics and visualizations
Univariate
Multivariate
Columns: Selection for modeling
See "Clean Data" for pre-modeling elimination of
redundant, constant, etc columns
Final selection is done during the Modeling phase
Document the above in a "Scorecard"
Modeling (CRISP 4)
Select modeling technique
Taxonomies: An overview
Supervised vs. Unsupervised
Descriptive vs. Predictive
Classification vs. Estimation
Supervised -- Constellation of methods with pros and cons
Classification
Decision Trees
Logistic Regression
Neural Networks
K-Nearest Neighbor
Prediction
Linear Regression
Neural Networks
MARS
Exercise: Scenario revisited -- What method(s) do we choose?
Unsupervised -- More methods with pros and cons
Segmentation / Clustering
Hierarchical clustering
K-Means
Decision trees
Association Rules
Team Exercise: Com up with an expert-derived decision tree to
make a selection for supervised problems
Advanced Topics
Ensembles
Bagging
Boosting
Parting remarks
Models should be as simple as possible, but no simpler
Why not both? (a low-res descriptive model and
a high-res opaque accuracy model)
Generate test design
Data segregation
Performance metrics: Whenever possible, go for the
custom metric -- "If you build it, they will come."
Build Model
Use a tool, select a method, set parameters (if any),
select candidate columns, select outcome (if supervised)
Variable selection techniques for supervised methods
Variable selection techniques for unsupervised methods
Assess Model (Tweaking)
Predictors
Manually removing or limiting
Forcing predictors
Structure
Profiles
Compared to What?
Baseline model comparison
Train/ Test/ Validation comparison
Scoring the model
What does scoring mean?
How is it different from building the model?
What are we looking for when scoring?
Final Product
Model(s)
Description(s)
Evaluation (CRISP 5)
Evaluate results (from business perspective)
Prelude to business use presentation
Informal, low-risk setting
Poke holes early, before business presentation
Does the model or segmentation make sense?
Does it contradict of reinforce the standard "lore"?
SWOT analysis: What are the strengths, weaknesses,
opportunities and threats?
Get support and buy-in from potential champions
Candidate names for segments
Present results to business users or clients
BUs need to be convinced: Models, segments and analysis
need to be marketed
Deployment will require change
To processes
To systems
To ingrained mindsets
Deployment costs (to each change area above)
Results must have business value, not technical representations
Performance results -- in business terms
Descriptions
No equations
Tell the story, paint the vision, what will life be like with
or without the model in place?
Review Process
Follow-ups to the presentation
Anticipate follow-up issues in planning and esimates
Revisions to the model(s) or segments based upon feedack
Final quality assurance
Determine next steps
Are you done?
Will the model(s) be deployed? Why or why not?
Document
Lessons learned meeting
Final Product
Consulting Exercise
WRAP-UP AND PARTING THOUGHTS
Final Q&A
Springboard exercise: "The boss in the elevator"
PA/ DM Philosophy
Understand the problem
Understand the data
Then, think about how to solve it (Einstein quote)
Work on problems with specific business goals,
specific hypotheses to be tested. Do NOT go
prospecting for "data mining nuggets."
Next Steps
PA/ DM Level II Course: "Strategic Implementation"
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...
