Course Details

Predictive Analytics and Recommender Systems

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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:

  • Understand algorithms/techniques used in predictive analytics and recommender systems
  • Select appropriate tools for predictive analytics tasks
  • Apply the methods covered in this course to implement solutions

Target audience: Professionals, Researchers, Academicians, Students, and Data Analytics Enthusiasts

Topics to be covered:

  • • Predictive Analytics
    • • Regression
    • • Classification Techniques
    • • Clustering Techniques
    • • Time Series Analysis
  • • Recommender Systems
    • • Association Rules
    • • Collaborative Filtering
    • • Contentbased Filtering
    • • Knowledge Bbased Approach
    • • Hybrid Approaches
Mode Duration Dates
Classroom and Lab 3 days, 12-15 Hours (Classroom) and 6-8 Hours (Lab) July 14 - 16, 2016