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

PREDICTIVE ANALYTICS & DATA MINING

PROJECT PLANNING

Opportunity Identification and a Roadmap

for Low-Risk / High-Impact Projects

One Day
$995 USD
NASBA CPEs: 8


DESIGN FOR ACTIONABLE IMPLEMENTATION

project-planning

This fully unique course focuses on the most chronic and critical failing of the vast majority of advanced analytics implementations: project assessment, planning and design. The analytic professionals who apply the strategic principles conveyed within this course will obtain the rare soft skills to advance and stand out in this competitive practice.

This vendor-neutral course is designed to be taken independently, yet is part of a larger course series that covers an incremental 6-Phase model development methodology for low-risk, high-impact projects. The scope of this course extends to the first phase: Plan.

If you are looking for an intensive vendor-neutral strategic orientation to predictive analytics that is critical to overall project success, then this course is for you.


WHO SHOULD ATTEND

      • IT EXECUTIVES AND BIG DATA DIRECTORS: CIOs, CAOs, CTOs, Stakeholders, Functional Officers, Technical Directors and Project Managers who desire to shift their deluge of data from liability to asset
      • LINE-OF-BUSINESS EXECUTIVES AND FUNCTIONAL MANAGERS: Risk Managers, CRM Managers, Public Sector Directors, Business Forecasters, Inventory Flow Analysts, Financial Forecasters, Medical Diagnostic Analysts, Fraud and Loss Prevention Managers, eCommerce Company Executives
      • DATA SCIENTISTS: Who recognize the importance of complementing their tactical proficiency with a strategic planning and design approach to advanced analytics
      • 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

KEY SKILLS YOU’LL TAKE AWAY IMMEDIATELY

      • Plan and manage your predictive modeling projects effectively from the start
      • Identify, qualify and prioritize actionable analytic opportunities
      • Certify data resources and plan for an efficient analytic sandbox
      • Engage with confidence among your developers, analysts and consultants
      • Convey a standardized process development model to implement across your team
      • Develop the rare soft skills required to assess, design and oversee actionable analytics
      • Leave with resources, contacts and plans to substantially reduce your project preparation time, costs and risks

THE ORGANIZATIONAL CHALLENGE FOR ANALYTICS

Industry surveys underscore that most advanced analytics projects fail or fall short of their objectives. Project failure is rarely due to poor model development. Most organizations are working on the wrong end of the problem. Instead of preparing a goal-driven plan and tailored project design, they are leading with a technology focus. Most of today’s data scientists are skilled with trees, but lost in the forest.

And it’s not the fault of today’s practitioners. Commercial and academic courseware maintains a focus on analytic methods, software and tactics. They focus on optimizing for technical metrics as opposed to goal-driven performance. Most analytic professionals launch directly into data and software before assessing strategic issues that cause otherwise valid models to die on the vine at implementation time.

Beyond lecture and demonstration, this course will actively lead you through a structured and comprehensive analytic project design exercise that you can take home and apply. Those who stay for the full series will reference the completed design as the blueprints for full strategic implementation.


UPON COMPLETION, YOU’LL BE ABLE TO

      • Understand the purpose, function and impact of the 6-Phase Model Development Methodology
      • Outline the general implementation of the first phase of the methodology: Plan
      • Define and prioritize primary business objectives and detail the criteria for a successful project  results
      • Establish a Three-Step Experimental Design for predictive model development
      • Recognize pitfalls and avoid misleading approaches that cause analytic projects to fall short of their potential
      • Reinforce skills through active participation and a clear implementation roadmap
      • Evaluate this event to qualify subsequent series courses for additional team members to proceed through a common implementation process

WHAT MAKES THIS COURSE UNIQUE

The developer of this course has been deeply involved with the design, implementation and deployment of real-world predictive modeling solutions. Well before leading industry conferences started catching on to the importance of strategic design for actionable analytics, the facilitators of this course had been conveying the practice to their clients. These clients have been richly rewarded by recognizing doomed projects in advance and amplifying the impact of successful deployments.

There simply is no other vendor-neutral event in the marketplace that focuses exclusively on analytic project assessment, planning and design – let alone integrating seamlessly into an overarching series for end-to-end process implementation. The design skills conveyed in this event are greatly underrepresented in the analytic field and rightfully becoming highly valued.

It is important to note that this course does not drill into specific algorithms or tactical implementation topics. For a deeper presentation of modeling methods, algorithms and techniques, refer to the Model Development course which directly follows this event. For a comprehensive orientation to the full 6-Phase process model, view the Strategic Implementation course description.


PREREQUISITES

While this course is designed to be taken independently, it is important to understand its place and function within the overall Predictive Analytics & Data Mining Course Series.

Registrants will be required to view a four-hour asynchronous “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. Prior education or experience in data analytics or statistics is helpful, but not required.

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 the preparatory email. The instructor can assist participants with any preparation during breaks, and before or after class.


TOPIC COVERAGE

What You Will Get in this Presentation

Core Concepts

Terms Used in Today’s Analytics Environment

    • Big Data Analytics
    • Predictive Analytics
    • Data Science
    • Business Intelligence
    • Data Analysis
    • Machine Learning
    • Dashboards
    • Statistics
    • Prescriptive Analytics
    • Predictive Modeling
    • The Current Landscape of Analytics Software

PLAN: Incremental Development

    • Enhanced Resource Allocation
    • The Four Core Project Types
    • Low-Risk / High-ROI Project Design
    • The Incremental Development Process
        • Stage 1P: Positive Benefit Models
        • Stage 1N: Negative Impact Models
        • Stage 2: Conflict Resolution
        • Stage 3: Modeling Across the Continuum
        • Stage 4R: Resolution Enhancement
        • Stage 4P: Precision Enhancement
    • LAB: Incremental Development

PLAN: 3-Step Project Design

    • Train
        • Constructing Candidate Models
        • Sample Size Requirements
        • Stage Considerations
        • Specifying Techniques by Project Type
    • Test
        • Decision Cycle Identification
        • Sample Size Requirements
        • Business Evaluation of Candidate Models
    • Validate
        • Business Decision Consistency
        • Strategy specification
        • Validation Study Requirements
        • Expected Performance Estimation
    • LAB: 3-Step Project Design

PLAN: Organizational Issues

    • Actionable Analytics Requires More Than Software and Data
        • Decision-Making Expertise is Decentralized: The Threat of Centralized Development
        • Roles and Responsibilities in Project Development
        • The Project Team: Levels of Effort by Phase
        • Conflicting Objectives and Their Resolution
        • Project Development in Highly Dynamic Environments
        • Current State Decision Process: When Predictive analytics is NOT the Right Option
        • Future State Decision Process: Defining Project Deliverables
        • Return on Investment: Organizational Objectives and Project Resources Mismatch
    • Opportunity Identification
        • Organizational Objectives
        • Performance Metrics
        • Behavior of Interest
        • Scarce Resources
        • Sufficient Data
    • Outcome Attribute Specification
        • The Critical Design Issue
        • Who is a ‘1’?
        • Specifying the Behavior of Interest
        • Constraint Specification
        • Omission Threats to Projects
        • Evaluating Project Potential
    • LAB: Business Issues

PLAN: Data Issues

    • Data Quality
        • Data Errors
        • Outliers
        • Missing Data
        • Query Integrity
        • As-Was Review
    • Data Representations
    • Data Types – Practical Data Representations Schemes
    • Data Transformations
        • Constructing Derived Condition Attributes
        • Linearizing Relationships
        • Removing Skewness
    • Train / Test / Validate Data Set Construction
    • Condition Attribute Specification
        • Quantitative Characteristics
        • Descriptive Data Statistics
        • Missing Value Treatment
        • Outlier Identification & Treatment
        • Record Structure
        • Condition Specification
        • Data Representation Specification
        • Data Transformation Specification
        • Domain Information Specification
    • Structuring Data for Modeling
    • LAB: Data Sandbox Construction

Wrap-up and Next Steps

    • Apply a 20% discount within six months to the Model Development or Strategic Implementation course
    • Supplementary materials and resources
    • Conferences and communities
    • Get started on a project!
    • Strategic Oversight and Collaborative Development
The Modeling Agency, LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. For more information regarding refunds, and cancellation policies, contact a training TMA training advisor at (281) 667-4200, ext 3. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.learningmarket.org


Upcoming Sessions

December 3, 2014
February 23, 2015
April 13, 2015
May 11, 2015

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Attendee Comments

“A few months ago, I attended Prediction Impact’s data mining course. It provided a good orientation to predictive analytics. But I wanted to learn how to really apply data mining at the project level. TMA’s course series provided the comprehensive and pragmatic approach I was truly seeking that will allow me to dive confidently and properly into the practice.”

Ernest Ngwa
Aspiring Data Mining Practitioner
Lanham, MD

“When the only complaint is that the course could be longer, I think you’ve got an excellent class! I very much enjoyed the instructor’s use of a real data set to demonstrate principles taught throughout the entire class. The instructor went out of his way both before and during the class to help me to translate the class material to my own work.”

Susan Glass
Senior Engineer,
Biological Technologies Analysis Solutions
Wyeth

“The instructor’s presentation was quite thoughtful and very well organized. I came away with a solid map for the ever changing data mining landscape.”

David Cousins
Divisional Scientist
BBN Technologies

“Statisticians and Analysts alike can benefit from this Data Mining course. It is interesting to view the business objective from the other side of the coin. Exploratory Data Analysis in Data Mining is fun because the causality constraint of classical Statistics is relaxed. Take this course and open up to another way of dealing with large data sets.”

Raymond D. Mooring, PhD
Wage and Investment Research
Internal Revenue Service

“If you want a thorough introduction to predictive analytics at the project level with a wealth of real world experience solving problems, then Tony’s your guy.”

Elies Koudier
Professor of Marketing
Ferris State University

“A great experience. I would recommend this course for anyone interested in Predictive Analytics.”

Maisam Salehi
Analyst, Customer Insights
Giant Food Stores

“This class, by far, is the most interesting, motivating and applicable class I have taken in a very long time. Tony provides a refreshingly different perspective on predictive modeling and approach methodology. Not only would I absolutely recommend this course to any colleague or anyone interested in the practical, yet powerful insights into predictive modeling, but I may look into additional learning and or professional services opportunities. I can’t wait to get back to work and jump right into applying the concepts and learnings.”

Anonymous

“The class was great! I was really impressed with the instructor’s knowledge, experience, and ability. He was able to answer everyone’s questions thoroughly and tailor the class to individual needs. I learned so much about the data mining process, the different methods, and available tools. I highly recommend this course to both technical and non-technical people interested in leading-edge data mining methodologies and the application of current data mining software to marketing, business, and research endeavors.”

Stephen Pearce
Preventive Medicine
Kaiser Permanente

“This course will fully help you understand what is really important for increasing performance from your data by introducing key concepts that are not taught in any business schools.”

Miguel Flores
Operations Research Analyst
Federal Aviation Administration

“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

“Attending The Modeling Agency’s series was a tremendously rewarding experience, helping me to ‘de-mystify data mining’ and interface with exceptionally intelligent people who live in the data mining world.”

Dr. Joan L. Anderson
Apparel, Merchandising, and Textiles
Washington State University
“The instructor’s effective communication and presentation skills provided us the confidence to understand the proper use of data mining in our new roles as analysts. His genuine interest and concerns for adaptation to each student level in addition to his experience and understanding enabled proactive class participation conducive to learning. This was extremely helpful for those of us with no data mining background. Our sincere thanks for an overall excellent experience!”

Ana Lemmon and Elisabetta Halkard
Office of Special Investigations
US Air Force

“This course was fabulous. It was everything I hoped it would be – technical and practical. The instructor is amazing and the resources he gave to further my studies was also helpful.”

Jenifer Underwood
Solutions Architect
Bayshore Solutions