Predictive Data Analytics and Modelling

Code: BIDA406
Apply this course towards: Business Intelligence and Data Analytics, Business Administration - Certificate, Business Administration - Diploma

Course description

Using standard, industry-accepted informatics tools, you will design and create descriptive, diagnostic and predictive models. Learn how to translate stakeholder needs into model designs and understand the ethical implications of the analytical models you use.

Learning objectives

Upon completion of this course you will be able to:

  • Use presentation tools and data mining techniques to explore datasets with different complexities
  • Think analytically: Descriptive, Diagnostic, Predictive and Prescriptive approaches
  • Apply data mining processes and unsupervised methods
  • Utilize supervised predictive methods (Regression and Classification)
  • Understand Time Series problems and model time series case studies
  • Identify the difference between machine learning and programming
  • Differentiate the ethical implications of modelling and predictive analytics

Prerequisites

​​It is recommended that students have completed the following courses:

If your first language is not English, you will be asked to provide evidence of fluency through the submission of an IELTS (Academic) score of minimum 6.0 (with no component less than 5.5), TOEFL score of 550 (79 Internet Based TOEFL), completion of the UVic English Language Centre's ELPI 570 level, or equivalent level through one of our English pathway partners.

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Info you should know:

Continuing Studies statement on use of educational technology

This course will require the use of Zoom and may use other education technology such as internet-based applications, cloud services, or social media. In order to complete this course you will be required to either consent to the disclosure of your personal information outside of Canada to enable use of these technologies, or work with the Division of Continuing Studies to explore other privacy protective options (such as using an alias or nickname).