Data science has become mainstream. Evolving predictive modeling techniques have significantly improved the ability of computers to help make valuable predictions. Across industries, predictive models are used to support strategic functions such as pricing, fraud analysis, sales targeting and segmentation. Understanding of the applications, assumptions, and strengths and weaknesses of predictive models is increasingly becoming a general management skill. During this course, participants will learn how to conceive, understand, challenge and deploy robust models. Foundations in Data Science is a recommended pre-requisite for people with limited data science exposure.

Learning Objectives:

By the end of the course, you will be able to:

  • summarize some of the key approaches to prediction
  • describe how advances in predictive technology can drive new business opportunities
  • explain selected predictive model techniques
  • distinguish between supervised and unsupervised models
  • list and describe tools to assess model quality
  • summarize the key factors when deploying predictive analytics projects

Who will Benefit:

Managers and individuals responsible for overseeing or participating in data science projects, and anyone interested in understanding how to add value to the teams using data science.