Theory and Practice of Real-time Data Analytics

Traditional approaches cannot be used for real-time data analysis because they rely on historical data. Companies today are heavily interested in using external sources of data including social media, blogs, and sensors. These external data are critical in performing predictive analytics efficiently  which gives companies advantages over their competitors. This course is about the concepts, techniques, and technologies of data analytics in realtime. It covers theoretical foundation of batch style data analytics. It covers data analytics tools include machine learning techniques and algorithms, and inferential statistics. Also, during the course a real-world end-to-end real-time data analytics scenario will be simulated.

Course Objectives

  • Provide a solid background on fundamental concepts of real-time data analytics.
  • Provide a theoretical foundation of real-time data analysis.
  • Provide strong understanding on machine learning techniques and algorithms.
  • Provide strong understanding on inferential statistics.
  • Explain how to use machine learning and statistics to design and develop descriptive and predictive models.
  • Provide hands-on knowledge how to implement a predictive model, deploy in in Apache Storm cluster.
  • Present and describe the simulation of a real-world data analysis scenario.

What is in it for the Participants?

  • Learning foundational concepts of real-time data analysis.
  • Learning the theoretical aspects of real-time data analysis
  • Being able to design descriptive and predictive models using machine learning and statistical methods.
  • Being able to implement descriptive and predictive models which can extract knowledge in real-time.
  • Being able to deploy, run, and manage analytics jobs real-time in a single-node or in a distributed Apache Storm cluster.