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Guidance and results for the data-rich, yet information-poor.



A Practitioner’s View of Predictive Modeling

Methods, Tactics, Tools and Techniques

Classroom: Two Days
Online: Four Half-Day Live Sessions
$1,795 USD
INFORMS Professional Development Units: 18


This is the only class of its kind that places analytic model development directly in the context of a broader enterprise-level implementation framework. TMA’s Model Development course is not just about the mechanics of building a predictive model – it’s about building models that drive tangible, measurable results for the direct benefit of the organization.

Attendees will directly experience the natural messiness and realities of organizational analytic implementation through work-along labs and guided discussions that build upon a highly realistic overall project. No other course conveys this level of knowledge transfer, experiential engagement and skill reinforcement.

This course is designed to be taken independently, yet is part of a larger series that covers The Modeling Agency’s entire Modeling Practice Framework™ (see illustration) for low-risk, high-impact implementation. This event covers the Fourth Phase of the Seven-Phase Modeling Practice Framework: Model.


      • Data Scientists: who desire to extend their analytical toolbox with formal process and methodological practice at the organizational level
      • Functional Analytic Practitioners: Customer Relationship Managers, Risk Analysts, Business Forecasters, Statistical Analysts, Social Media and Web Data Analysts, Fraud Detection Analysts, Audit Selection Managers, Direct Marketing Analysts, Medical Diagnostic Analysts, Market Timers who desire to lead their teams and initiatives with greater functional confidence
      • Big Data Analysts: who are under increasing pressure to transform their deluge of data from a liability to an asset
      • Project Leaders: who want to gain a stronger command of predictive modeling methods and techniques to better manage and interact with practitioners
      • Business Analysts: who must develop and interpret the models, communicate the results and make actionable recommendations
      • IT Professionals: who wish to gain a better understanding of the data preparation, analytics and analytic sandbox development requirements to more fully support the growing demand for analytic IT support
      • Anyone Overwhelmed with Data and Starved for Actionable Insights


      • Apply a formal process for data preparation, model development and validation of results
      • Recognize and avoid common, costly pitfalls in data preparation, method selection and results interpretation
      • Ensure that your model is adequately generalized and has not memorized the training set
      • Take an incremental low-risk / high-impact approach to model development with vendor-neutral tool exposure
      • Better understand trade-offs between model accuracy and explainability when selecting modeling methods
      • Guide your IT staff to build an analytics sandbox for rapid model development and minimal IT dependency
      • Proceed confidently with the formal process, session files, and direct experience gained through follow-along labs, guided discussion and team engagement


Organizations now contain so much data that it has become very difficult to understand just what that data can tell us. All this data has meaning for making better prospective organizational decisions and anticipating customer needs and preferences. But how do you discover those needs and preferences within data stores that contain endless amounts of seemingly incomprehensible numbers and facts? Predictive analytics can do just that.

Only predictive analytics can transform the rapidly growing mass of inert data into actionable information assets. These assets can be leveraged for measurable gain through improved and automated decision-making. The practitioners who develop these transformative skills will be the leaders in their field.

The intent of this course is to present participants with

    • an understanding of how various methods and tools apply to different kinds of data-intensive problems
    • techniques to overcome limitations that frequently cause predictive models to under-perform
    • a roadmap for model-building that addresses the unique dynamics inherent in larger organizations
    • effective ways to translate model accuracy into performance metrics valued by the organization


    • Produce goal-driven prescriptive models with a view toward organizational production and impact
    • Select appropriate methods for each of the primary analytic project types
    • Optimize for organizational performance metrics, as opposed to artificial model accuracy metrics
    • Assess and report the degree to which a model meets a predefined performance objective
    • Leave with resources, contacts and actionable plans to substantially increase your analytic capabilities while minimizing dead ends


This course does not restrict or skew the presentation of machine learning methods through a single product. Rather, the Model Development course gives broad consideration of predictive analytics from a purely vendor-neutral perspective.

Live modeling demonstrations will precede follow-along exercises. Participants will directly experience the natural messiness of data mining to discover what really works, as well as what doesn’t and why. The instructor will show how to evaluate various features and available products based upon strengths, limitations, function, value and general performance.

As active consultants on large organization analytic implementations, each member of TMA’s seasoned faculty possesses a wealth of practical experience in applying predictive analytics across industries. This course, like no other, insists upon making predictive analytics purposeful, measurable and actionable in a larger and more complex organizational setting.


While this course is designed to be taken independently, it is helpful to understand its place and function within the overall ‘The Predictive Analytics Operation’ comprehensive course. 

Prior education or experience in analytics or statistics is helpful, but not required. Those seeking a deep drill-down into the mathematical or theoretical underpinnings of machine learning algorithms should refer to available academic or on-demand online offerings. The analytic algorithms in this course are actively demonstrated and conveyed from a functional perspective.

Registrants will be required to view a three-hour online “Core Concepts” orientation prior to attending this event. Access details for the Core Concepts orientation will be shared with participants prior to the start of the course.

Participants need only supply a laptop computer with Microsoft Excel. Instructions on how to download lab data and any analytic tools will be provided in a preparatory email. The instructor can assist attendees with any preparation during breaks, and before or after class.


Core Concepts
Model Development Introduction
    • Current Trends in Analytic Modeling, Data Mining and Machine Learning
          • Algorithms in the News: Deep Learning
          • The Modeling Software Landscape
          • The Rise of R and Python: The Impact on Modeling and Deployment
          • Do I Need to Know About Statistics to Build Predictive Models?
      • Strategic and Tactical Considerations in Choosing a Modeling Algorithm
            • What is an Algorithm?
            • Is a “Black Box” Algorithm an Option for Me?
    The Tasks of the Model Phase
        • Generate Test Design
              • Train-Test Validation
              • Accept or Reject Modeling Parameters
              • Test / Test / Validate
        • Optimizing Data for Different Algorithms
        • Build Models
              • Classification
                    • Issues Unique to Classification Problems
                    • Why Classification Projects are So Common
                    • An Overview of Classification Algorithms
                          • Logistic Regression
                          • Neural Networks
                          • Naïve Bayes Classification
                          • Support Vector Machines
                          • Decision Trees
                          • Ensemble Methods
              • Value Estimation and Regression
              • Clustering
              • Association Rules
              • Other Modeling Techniques
                    • Times Series
                    • Text Mining
                    • Factor Analysis
        • Model Assessment
              • Evaluate Model Results
                    • Check Plausibility
                    • Check Reliability
              • Model Accuracy and Stability
              • Lift and Gains Charts
        • Modeling Demonstration
              • Assess Model Viability
              • Select Final Models
        • Why Accuracy and Stability are Not Enough
        • What to Look for in Model Performance
        • Exercise Breakout Session
              • Create & Document Modeling Plan
              • Determine Readiness for Deployment
        • What are Potential Deployment Challenges for Each Candidate Model?
        • Exercise Breakout Session and Guided Project Discussion
Wrap-up and Next Steps
      • Supplementary Materials and Resources
      • Conferences and Communities
      • Get Started on a Project!
      • Options for Implementation Oversight and Collaborative Development

The Modeling Agency, LLC, is registered with INFORMS (the INstitute For Operations Research and the Management Sciences) as a Recognized Analytics Continuing Educational Provider for the CAP® (Certified Analytics Professional) program. The CAP® credential provides analytics professionals with a means to distinguish themselves and demonstrate to employers, colleagues, and the public that they are knowledgeable analytics professionals. Courses provided by The Modeling Agency, LLC, are automatically accepted by INFORMS when claimed by credential holders as evidence of continuing education. For more information about the CAP® program, including requirements, eligibility, benefits, preparation, and exam dates, please visit the INFORMS website at www.informs.org/certification.


January 8 – 11, 2018
12:00p – 5p US EST
April 25 & 26, 2018
June 25 – 28, 2018
12:00p – 5p US EDT

Sign Up Early and Save

Events Limited to 20 Seats

On-Site Available

Inquire by Phone or Email

Predictive Analytics Webinar

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Tuesday, February 20, 2018
11am EST / 8am PST
90-Minute Live Interactive Event

Why Train With TMA?

Determine whether TMA training is right for you, and learn why TMA is truly the best option for live classroom analytics training.

Attendee Comments

“The instructor is knowledgeable, well organized, and interacts extremely well with participants. If you have only two days to learn about data mining, TMA’s Model Development course is the class you should attend.”

Yiguang Qiu, PhD
Marketing Department
Amica Insurance

“This course is a great overview of available predictive analytics methods and techniques that provides an organizing framework for your model development efforts.”

April Smith
Lead Workforce Analytics

“Both instructors in the series did a fantastic job of getting me up to speed in predictive analytics much faster than any book (or probably any other training class or conference) available.”

Raymond G. Henderson
Knowledge-Based Systems Engineer
Compliance Technologies, Inc.

“The scope and pace of the class were spot on for me, exactly what I was looking for. The real world examples from industry veterans were both insightful and beneficial. The class ultimately helped to strip away the buzz words and hype surrounding the industry, and revealed the truly valuable foundation of this remarkable combination of art and science.”

Eric Vajentic
Senior Business Analyst
Cerner Corporation

“This is a must-attend course for those who would like to get started in the amazing world of Predictive Analytics & Data Mining. Without going into theoretical details, it covers every important step in the development of predictive models.”

Ivan Mendoza
Database Marketing & Predictive Analytics Expert
Cogeco Cable

“The practicality of predictive analytics was effectively presented during the Development course. Keith demonstrated keys to developing good predictive models, which helped to move the focus beyond the algorithms to the foundation for the model to work – effective data preparation. Content well delivered.”

Alwayne Geddes
RTC Quality Coordinator
Tredegar Corporation

“This course was very enlightening. I was suprised to see the many ways predictive analytics can be applied!”

Roxanne Ramos
Business Operations Analyst
Toshiba America Medical Systems

“The wealth of information covered in these courses, as well as the in-depth demonstrations of multiple software packages, made the sessions valuable from a wide range of perspectives. I will certainly recommend that others attend.”

Brent King
AVP, Managed Care
Analytics / Business Development
Health Smart Preferred Care

“The instructor was great and the instructor was very knowledgeable. It was just at the right level, not to general and not too technical.”

Doug Rosenberg
Technical Consultant

“The instructor and course material are first rate. Any organization that believes data mining should be a part of their business operations portfolio would be making a wise investment by attending this course.”

Eric Rickard
Information Computing Sciences
SRI International

“The instructor was really great. Tim not only had a very clear understanding of what he was presenting, he was also a very good teacher who solicited a lot of audience participation. Most people with a highly technical background can’t always communicate effectively. Tim had no such problem.”

Bryan Arnold
Current Employment Statistics
Bureau of Labor Statistics

“This course is extremely helpful and should be attended by any and all new Data Analysts. It outlines the process completely and thoroughly.”

Gagan Sekhon
Data Scientist
Xoom Corporation

“The Modeling Agency takes a daunting subject and brings it to a very understandable level to attack any problem you may be facing as an organization.”

Eric Barger
Personal Lines Underwriting Manager
Brethren Mutual Insurance Company