CISC451/3.0 Topics in Data Analytics
Content will vary from year to year; typical areas covered may include: tools for large scale data analytics (Hadoop, Spark), data analytics in the cloud, properties of large scale social networks, applications of data analytics in security.
Learning Hours: 120 (36L;36Lab;48P)
Potential topics include:
Tools and techniques (8 weeks)
Applications of large-scale analytics
- Conceptual and pragmatic difficulties in large-scale analytics
- Properties of clusters (for computation) and clouds (for storage)
- Tools for analytics at scale (Hadoop, Spark, Tensorflow)
(4 weeks) such as social network analysis in networks of planetary scale (e.g. Facebook); textual analytics of large corpora (e.g. forums, tweets); collaborative filtering in large-scale review data (e.g. Amazon review datasets)
Upon successful completion of this course, a student will be able to:
- Design inductive model building algorithms appropriate for of any scale and complexity, using cutting edge technologies
- Plan ways to collect data, build models, and interpret results in large distributed (cloud) datasets
- Evaluate the modelling performance of such algorithms, and the implications for real-world system that the data describes
As a topics course, the readings will vary from year to year and will consist of custom courseware and papers located through the Queen’s library databases.