Analytics : Predictive Analysis in HR , Fraud and Marketing

Learn how different domains can apply predictive analytics .Quick guide for analytics & machine learning professionals.


What you’ll learn

  • predictive analytics.
  • HR Analytics.
  • Fraud Analytics.
  • Customer Analytics.

Course Content

  • Introduction –> 1 lecture • 13min.
  • Predictive Customer Analytics –> 9 lectures • 16min.
  • Predictive Customer Analytics Case Study –> 4 lectures • 7min.
  • Predictive Fraud Analytics –> 3 lectures • 5min.
  • Predictive Fraud Analytics Use Cases –> 7 lectures • 9min.
  • Predictive HR Analytics –> 6 lectures • 10min.
  • Predictive HR Analytics Use Cases –> 5 lectures • 8min.
  • Bonus –> 1 lecture • 2min.

Analytics : Predictive Analysis in HR , Fraud and Marketing


  • No prerequisites.


This course gives you an understanding of the application of predictive analytics in your field of interest /domain. As you learn the tools (machine learning, statistics e.t.c) its important to understand the application part. Whether you are a manager, newbie, enthusiast, data scientist or a machine learning professional, this course will bring more light on how you can apply predictive analytics in your domain.



  • Analyzing customer behaviour
  • From focusing on segments to focusing on the individual customer
  • This has been made possible through technological advancement & data mining tools
  • At the highest level of using customer data is predicting customer behaviour
  • Why? Lots of data: social media, transaction history, demographic data, e.t.c


  • Using predictive analytics in fraud detection & prevention
  • Move from detecting fraud after we have already made a loss to detecting fraud behaviour and thus prevent it from happening.
  • With tech, we’re trying to go into improving UX & UI such as fewer authentications but that comes with gaps for digital fraud hence the need for predictive fraud analytics.


  • We are moving to a data-driven HR function
  • Why? HR collects lots of data that can be used ( demographics, salary history, empl history, promotions data, churn data e.t.c )
  • Moving from depiction HR dep as a cost function to a strategic partner in the business.
  • Decisions like attracting, retaining and managing talent can be backed with data and to add more applying predictive analytics in those decisions.