10 Half-Day Sessions

May 13 - 16, 20 - 23, and 29 - 30, 2019

PREPARING AI FOR ORGANIZATIONAL ADOPTION

This comprehensive active exposure to the full Modeling Practice Framework™ provides leaders and practitioners with an integrated strategic and tactical orientation to predictive analytics, AI and machine learning.

Participants will build an AI-driven operation in stages to experience the natural messiness of AI and predictive analytics. 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 accelerate your organization’s digital transformation and put data science to work for measurable gain, then this course is designed for you. You do not need to know how code or have stats experience to track and benefit.

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 science, AI, machine learning and related emerging information technologies

KEY SKILLS YOUR TEAM WILL TAKE AWAY IMMEDIATELY

• Plan and manage your AI projects effectively from the start
• Identify, qualify and prioritize viable and actionable opportunities
• Shift from a limited technology mindset to one of organizational transformation
• Avoid approaches that waste time and expense on doomed AI projects
• Construct a valid data set and transform data for superior machine learning model performance
• Select appropriate methods for each of the four core analytic project types
• Assess the degree to which a decision model meets a predefined performance objective
• Take a low-risk / high-impact approach to model development with vendor-neutral tool exposure
• Apply a formal roadmap for data preparation, model development and validation of results
• Build an analytics sandbox for rapid model development and reduced IT dependency
• Develop the rare leadership skills to identify assess, design and oversee viable projects
• Leave with the resources, contacts and plans to reduce preparation time, costs and risks

THE ORGANIZATIONAL CHALLENGE

Traditionally, organizations use data retrospectively – to view what has already happened. Leading organizations use data strategically to anticipate behavior to maximize returns and minimize risk. The mining of data for predictive indicators with machine learning creates invaluable information assets.

Predictive analytics differs from traditional analytics because it produces models —models that capture and represent hidden patterns and interactions in the data. The models that result from such a proactive approach address both why things happened and what is likely to happen next. A user can pose “what-if” questions to an analytic model that cannot be queried directly from a data store. Examples include:

• What is the expected lifetime value of every customer account?
• How can we better target the allocation of organizational resources?
• Which cases should be audited first for the highest propensity of fraud?
• How will production quality be affected if resources and controls change?

The organizations that transform their data into information assets and automate decision-making for measurable gains will realize substantial returns on their data science and big data investments.

WHAT MAKES THIS COURSE UNIQUE

This course provides a methodical, holistic and strategic approach to predictive analytics. Most organizations jump directly into data and tools that tend to produce good models… and their projects fail. They fail because they have not anticipated how to integrate the project in the organization.

Those who make the investment to fully assess their environment, situation, resources and objectives across all team members will produce project results that are measurable, accountable, actionable and impactful. Unlike any other course on the market, the Comprehensive course steps through the full build of a predictive modeling operation within the realistic environment of a large organization.

Leaders who take this 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 goal-driven focus.In the end, the organization will be greatly strengthened with team members who operate within a common platform that makes AI, predictive analytics and machine learning purposeful and impactful.

This course is intended for those willing to invest in developing skills for superior project design and incremental decision modeling platform development. Those who complete this course will be capable of guiding their organization to stand up a thriving internal AI-driven decisioning practice with measurable and residual gains.

PREREQUISITES

Registrants will be required to view a three-hour online video “Core Concepts” orientation prior to attending this event. Access details to the Core Concepts video modules will be shared with participants prior to the start of the course. Modules are easily consumable in 10 to 15-minute blocks of instruction.

Prior education or experience in data analytics or statistics is helpful, but not required. Instructions on how to download exercise 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.