Real-time Analytics Using Machine Learning and Statistical Tools

Traditional historical analytics approaches such as business intelligence were highly efficient and effective for extracting required discrete knowledge from data. These approaches served organizations for many years successfully in defining new organizational strategies or operational strategies. However, these solutions have a few limitations such as, these approaches lack the ability to predict an occurrence, incidence, disaster. Additionally, since historical analytics relies on historical data, traditional approaches cannot be used in real-time analysis which organizations today are heavily interested in. Real-time data analytics gives several advantages over batch style analytics. This course is about the concepts, techniques, and technologies of real-time data analytics. The course covers data analytics tools: machine learning and statistics. The course covers machine learning techniques, algorithms. Also it covers the methods of inferential statistics. During the course a real-world end-to-end data real-time analytics scenario will be simulated.

Course Objectives

  • Provide a solid background of fundamental concepts of real-time data analytics.
  • Provide a strong understanding of machine learning techniques and algorithms.
  • Provide a strong understanding of statistical methods.
  • Explain how to use machine learning and statistics to design and develop data analytics model.
  • Provide hands-on knowledge of how to implement an analytical model, and deploy the model in Apache Storm cluster.
  • Provide hands-on knowledge of how to administer the Apache storm cluster.
  • Present and describe the simulation of a real-world data analytics scenario.

What is in it for the Participants?

  • Learning the details of the basics of real-time data analytics technologies.
  • Being able to design the real-time predictive models using machine learning and statistical methods.
  • Being able to implement the real-time analytics model over an use case.
  • Being able deploy, run, and manage real-time  analytics jobs in single-node or in a distributed Apache Storm cluster.