About The Course:
Today businesses can not depend only on their products or services to grow, they have to utilize and learn from historical data to better understand the end user. Predictive analytics is emerging as a strategy to achieve the same through various techniques. Predictive analytics helps improve end user experience through user action prediction and appropriate (real-time) recommendations. Predictive Analytics and Recommender Systems play a key role in many data science applications as demonstrated by applications like, Google flu trends, e-commerce portals e.g. Amazon, among others.
The course covers various methods of Predictive Analytics and Recommender Systems drawn from Statistics, Data Mining, and Machine Learning. It will discuss popular algorithms in the domain and their use in various applications. The course emphasizes hands-on approach for better understanding of the techniques used in the domain. During the course, mainly open-source tools will be used for illustrations and lab.
Objectives: On completion of this course, one should be able to:
Target audience: Professionals, Researchers, Academicians, Students, and Data Analytics Enthusiasts
Topics to be covered:
|Classroom and Lab||3 days, 12-15 Hours (Classroom) and 6-8 Hours (Lab)||July 14 - 16, 2016|