PREDICTIVE ANALYTICS & DATA MINING
A Comprehensive and Active Experience of the
6-Phase Model Development Methodology
A HOLISTIC APPROACH TO ACCELERATE ANALYTIC MATURITY
This comprehensive work-along exposure to the full modeling process development methodology provides leaders and practitioners with the combined strategic and tactical orientations to predictive analytics.
Live demonstrations follow short lecture segments. Participants then proceed to build an overarching project in stages with the autonomy to directly experience the natural messiness of data mining. No other training in the market provides such an immersive, skill-reinforcing and complete view of the practice – particularly with a real-world focus and vendor-neutral perspective.
If you are a business or public sector leader or practitioner looking to propel your organization’s analytic maturity and put predictive analytics to work for measurable gain, then this course is designed 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 transform their deluge of inert data to actionable assets
- 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
- 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 viable and actionable analytic opportunities
- Convey a standardized process development model to implement across your team
- Acquire the rare combination of tactical and strategic skills to stand out in the analytics practice
- Take an incremental low-risk / high-impact approach to model development along with vendor-neutral tool exposure that will save months in product surveying
- Apply a formal process for data preparation, model development and validation of results
- Recognize and avoid common but costly pitfalls in data prep, method selection and results interpretation
- Guide your IT staff to build an analytics sandbox for rapid model development and minimal IT dependency
- Develop the rare soft skills required to assess, design and oversee actionable analytics
- Proceed confidently upon return with the formal process templates, session files, and direct hands-on experience gained in the follow-along labs through the full 6-Phase Model Development Methodology
THE BUSINESS CHALLENGE
Traditionally, organizations use data retrospectively – to view what has already happened. Leading organizations use data prospectively – to anticipate behavior and automate prescriptive decisioning that targets the allocation of resources and grows the business while minimizing risk and loss. The mining of data for predictive indicators creates information assets from big data or small, which an organization can leverage to achieve specific strategic objectives.
Predictive analytics is a data-driven extension to an enterprise’s decision support system and big data architecture. It complements and interlocks with other IT and big data capabilities such as query and reporting, on-line analytical processing (OLAP), data visualization, and traditional statistical analysis. 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.
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 a data store. Examples include: “What is the expected lifetime value of every customer account,” “Which customers are likely to open a money market account,” “Which cases should be audited first for the highest propensity of fraud”, or “How will production quality be affected if various resources are changed?”
The organizations that effectively transform their big data liability into information assets and automate decision-making for measurable gains will be the first to realize substantial returns on their big data and analytic investments.
UPON COMPLETION, YOU’LL BE ABLE TO
- Understand the purpose, function and impact of the 6-Phase Model Development Methodology
- Define and prioritize primary organizational objectives and detail the criteria for 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
- Realize that model-building for actionable production need not be highly technical or complex
- Construct a valid data set and transform data for superior model performance
- Select appropriate methods for each of the Four Core Analytic Project Types
- Assess the degree to which a model meets a predefined performance objective
- Leave with resources, contacts and plans to substantially reduce your project preparation time, costs and risks
WHAT MAKES THIS COURSE UNIQUE
For today’s organizations to transform from their current big data challenges into leveraging their growing mass of data prospectively for measurable and sustainable gains, they will have to first take a more methodical and holistic approach to predictive analytics.
This will require a purposeful and balanced approach to strategy and tactics. Most organizations jump directly into data and tools that tend to produce good models… and fail at the project level for a host of strategic reasons. Those who make the investment to fully assess their environment, situation, resources and objectives across all team members will produce project designs that result in analytic projects that are measurable, accountable, actionable and impactful.
Unlike any other course on the market, Strategic Implementation steps through a full 6-Phase Model Development Methodology giving equal emphasis to strategic and tactical issues. Leaders who take this comprehensive course will interact more effectively with their teams at the tactical level, while analytic practitioners will complement their existing algorithmic background with a more strategic focus.
In the end, the organization will be greatly strengthened with team members who run from a common platform that insists on making predictive analytics purposeful and impactful. This course is intended for those willing to invest in developing skills for superior project design and incremental development to overcome chronic analytic failings. Those who complete this course will be capable of guiding their organization to stand up a thriving internal analytic practice with measurable and residual gains.
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. Read the Series Overview to understand all that is delivered in this highly engaging course.
What You Will Get in this Presentation
- Prerequisite four-hour preparatory orientation
- View the full Core Concepts Topic Outline
- Project Planning Course
- Strategic Implementation includes the Project Planning course
- View the full Project Planning Course Topic Outline
Prepare and Build Phases
- Model Development Course
- Strategic Implementation includes the Model Development course
- View the full Model Development Course Topic Outline
- Test: Business Evaluation of Alternative Models
- Selecting a Challenger Model
- Lab: Model Selection
- Validate: Estimating Performance of Challenger Model
- Lab: Performance Estimation
- Rounding Threshold Analysis
- Lab: Rounding Threshold
- Lab: Predictive Dashboard
- Confirm Future State Decision Process
- Confirming Implementation Strategy
- Organizational Adoption Issues
- Model Monitoring Considerations
- Model Maintenance
- Identifying Domain Insights
- Threats from Environmental Change
- Estimating Future Analytic Potential
- Opportunities in Business Environment Evolution
- Estimating Future Performance Enhancement Potential
- Opportunities in Time Series
- Simplifying Big Data
- Opportunities in Emerging Data Types
- Variations on Basic Model Types
- Overview and Demonstration of Selected Software Tools
Extended Modeling Topics
Wrap-up and Next Steps
- Certification Exam
- Supplementary materials and resources
- Conferences and communities
- Get started on a project!
- Strategic Oversight and Collaborative Development
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Data Mining Webinar
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Why Train With TMA?
“There is no better way to learn Data Mining than to do it yourself. Take this course for an invaluable hands-on experience of project definition and implementation. Even if your organization does not adopt predictive analytics as standard, you will have acquired a much needed skill set.”
Wage and Investment Research
Internal Revenue Service
“This course gave me just what I needed: a clear conceptual idea of how a data mining project is designed and implemented, and the hands-on to gain confidence in the process.”
Market Information Manager
Bank of Hawaii
“This course opened my eyes to the big picture in a practical way. The content of the Project Implementation course was very clear and responsive to my needs. My questions were answered directly and clearly. Exceeded my expectations!”
ITS – Business Solutions
“I would recommend TMA’s Project Implementation course to executives weighing the costs and benefits of such projects within their organizations. Tony approaches the course from a business management perspective and presents the concepts in real-world cases making the task of visualizing use of the process in one’s own business a snap!”
AVP, Information Technology
Principal, Technology Services Group
Computer Sciences Corporation
“This course was excellent. It was technical and practical. The instructors were just amazing with their knowledge experience and their ability to answer all our inquires, also the resources they provided us with were very helpful. Well done TMA!”
Planning and Program Analyst
“This course gave me a new perspective on techniques and applications software that our federal agency had not previously seen. The course content was great, and the very knowledgeable instructor kept the students attention by using real-life examples and discussion of additional resources. I highly recommend this course!”
US Department of Education
“I was a bit apprehensive about attending and how I could apply data mining concepts to my particular industry, but the instructor put those fears to rest. Highly recommended! Thanks TMA!”
Planner / Analyst
The Mitchell Gold Company
“This class gives the Statistician a bunch of new tools to use in solving business problems. Once the limitations of statistics are reached, grab this data mining tool belt. You will be surprised how much further you can get.”
Wage and Investment Research
Internal Revenue Service