Predictive analytics utilizes a set of techniques to derive knowledge from large amounts of raw data. Techniques adapted from statistics, probability, data mining, and machine learning are used to create models that enable predictive capabilities. These models allow companies to shift their focus from insight (knowing why things happen) to foresight (knowing what is likely to happen in the future).
Predictive models learn from observed patterns in operational and historical data to quantify probabilities of future conditions, events, and risks. Virtually every industry—insurance, telecommunications, financial services, retail, healthcare, pharmaceuticals, and many more—uses predictive analytics to become more proactive in their management style and drive improved performance.
Application areas are diverse and examples include marketing, customer management, product development, operations monitoring, fraud detection, collections management, maintenance planning, risk management, price discovery, and inventory planning.
This course introduces the building blocks needed to implement predictive capabilities within an organization. It also helps develop the necessary understanding about how models, people, and decision processes must interact to drive actual business impact. Techniques based on statistics, probability, linear regression, logistic regression, and decision trees are described as key enablers for creating predictive models. Additional topics related to problem framing, data profiling, data preparation, model evaluation, human factors, leadership, and organizational culture are presented as additional and necessary ingredients for success.
You Will Learn
- Definitions, concepts, and terminology of predictive analytics
- What data science is and how it relates to predictive analytics and BI programs
- Purpose, structure, and categories of models
- Methods adapted from statistics, data mining, and machine learning
- Functionality of predictive models and related development approaches
- Common applications and use cases for predictive analytics
- How successful predictive capabilities are enabled by human and organizational factors
- Essential team composition, skills development, and organization models including roles, responsibilities, and accountabilities
- Why business, technical, and management skills are essential for success
- Practical guidance for getting started with predictive analytics
- BI and analytics executives, program managers, architects, and project managers
- Data-driven business professionals who want to learn how to implement the “power to predict”
- Technology professionals who want to develop their understanding of predictive analytics
- Business analysts who want to use predictive techniques in their analytics studies
- Business managers who want to develop a proactive and predictive decision-making style in their operations
- Anyone interested in learning the basics of predictive analytics and how it can drive business improvement