Studying Data Science: Data Ethics (Final review)

Following my preview post, here is my review of the edX course on Data Science Ethics which ended on June 8th. It covered:

  • What are Ethics?
  • History, Concept of Informed Consent
  • Data Ownership
  • Privacy
  • Anonymity
  • Data Validity
  • Algorithmic Fairness
  • Societal Consequences
  • Code of Ethics

Without going into depth on the above I will simply note that the course was almost all lectures with exercises at the end of each module. I found the lectures to be a bit too detailed and cumbersome to follow. So what I did was download the text of each lecture, saved it in a document and read through it.

The material is softer meaning it is not hard core programming or statistics, it is all about the ethical side of data science, manipulating data, ownership of data, privacy and issues relating to these. Personally I would say that although the subject is interesting it is not presented in the best way. Simply talking to an audience is not very captivating with all of the presentation methods available nowadays.

However, the topics are thoroughly covered and the material is straightforward and easy to go over. I would recommend the course to anyone with real interest in the topics but you might want to read through it instead of listening to the lectures as noted.

Michael Lazarou
Michael Lazarou
Michael Lazarou manages revenue assurance and fraud at Epic, a Cypriot telco, having joined their RA function in March 2011. His background includes a double major in Computer Science and Economics, as well as an MBA. Before being lured into the exciting world of telecoms he worked as a software developer.

Michael is interested to gain a better understanding of different aspects of RA and data analysis. He shares his insights on training courses he participates in with Commsrisk. Michael's accumulated experience of online training also led him to volunteer for the role of Coordinator of the RAG Learning online education platform.