Data Analytics in Educational Institutions: Building a Predictive Model for Student Enrolment

#Data #Analytics #Predictive #Model #Student #Enrolment
Share

: Now more than ever, planning and decision-making is data, rather than gut, driven. While there are many existing models for regression, prediction and learning, choosing an appropriate model involves understanding the associated data deeply. Often, choosing a standard model is still insufficient if the analyst is not customizing their model to handle the intricacies of their data.

 

We describe our development process of a Markov chain and Dynamic Markov Chain-based model for predicting college enrolments. The model is compared and contrasted against previously-attempted models. We include a demo of using our interface into the model, which allows for roughly 600 configurations of parameters, and many additional override value options.

 The ideas presented here are applicable to many dynamical systems involving population migration in a discrete-time process.



  Date and Time

  Location

  Hosts

  Registration



  • Date: 07 Dec 2016
  • Time: 04:00 PM to 05:00 PM
  • All times are (GMT-08:00) PST8PDT
  • Add_To_Calendar_icon Add Event to Calendar
  • 1000 KLO Rd.
  • Kelowna, British Columbia
  • Canada V1Y 4X8
  • Building: E
  • Room Number: 103

  • Contact Event Host
  • Co-sponsored by Okanagan College
  • Starts 18 November 2016 12:00 AM
  • Ends 07 December 2016 04:00 PM
  • All times are (GMT-08:00) PST8PDT
  • No Admission Charge


  Speakers

McCall Milligan of Okanagan College

Topic:

Data Analytics in Educational Institutions: Building a Predictive Model for Student Enrolment

Biography:

McCall Milligan earned his bachelor of Applied Mathematics at UBC Okanagan. He will be entering an MSc program in financial modeling and risk analysis in 2017. McCall is currently working as an Analyst at Okanagan College in the Institutional Research and Planning office.

Jim Nastos Jim Nastos of Okanagan College

Topic:

Data Analytics in Educational Institutions: Building a Predictive Model for Student Enrolment

Biography:

Jim Nastos earned his PhD from UBC Okanagan in Interdisciplinary Studies. He is currently a College Professor of Computer Science at Okanagan College, and previously worked as an Institutional Research Data Analyst at Okanagan College, a math and computer science lecturer at the UBCO and a math lecturer at the UAlberta. His academic research has been published in mathematics, computer science, physics, social networks, bioinformatics and marketing venues.


McCall Milligan of Okanagan College

Topic:

Data Analytics in Educational Institutions: Building a Predictive Model for Student Enrolment

Biography:

Jim Nastos of Okanagan College

Topic:

Data Analytics in Educational Institutions: Building a Predictive Model for Student Enrolment

Biography: