As organizations scramble to analyze large data sets to enhance decision making, concerns verging on public outrage surrounding breaches in data privacy, have become prominently entrenched in public discourse. With the growing richness of data, organizations have more insight into user behaviour patterns and transactions than ever before. The lines defining ethics in data science, however, are gray and constantly shifting.

Today’s organizations are facing uncharted challenges pertaining to data privacy and security. Gaps in existing standards and the emergence of new ones has led to varied approaches in different jurisdictions. Business users need to be aware of key challenges and considerations to competently assess the need to deploy changes in organizational policies and processes to be able to effectively manage changing risks.

During this eLearning course, participants will become familiar with key issues and challenges surrounding the collection and analysis of personal information, and the relevant legislation. Participants will explore unique ethical issues inherent is specific areas of data science, such as working with outside parties, advanced analytics, artificial intelligence, the internet of things and big data. Participants will also explore key considerations in developing data policy, providing guidance to staff and managing risk.


By the end of this module, you will be able to:

  • Describe some of the recent privacy and data issues that have garnered a lot of attention in the media, popular culture and from legislators
  • Summarize the ethical issues inherent in various areas of data science, such as data aggregation, health information and DNA, as well as the internet of things (IoT)
  • Outline current privacy and data legislation in Canada, the United States and Europe, which can create legal issues for Canadian firms
  • Describe the components of a multi-level framework for managing data and ethics exposures


  • Ethical considerations
  • How our information is being collected, aggregated and used
  • Psychometric profiling
  • Surveillance
  • Health-related data
  • Legal and ethical standards
  • Considerations for data science professionals, boards and CEOs


  • Individuals new to the field of data science, as well as those interested in broadening their knowledge of the data science and AI landscapes
  • Individuals who would like to gain a foundational understanding of key concepts, terminology and types of problems that data science typically helps solve