MAT5153

Data Analytics MDC4153/MAT5153

Catalog entry

Prerequisite: MAT2243 Applied Linear Algebra or (MAT2233 Linear Algebra and MAT2214/MAT2213 Calculus III).

Content: This immersive Data Analytics course equips students with the essential mathematical skills and knowledge required to analyze, visualize, and interpret complex datasets. Students will be exposed to the entire life cycle of data analysis. Throughout the course, participants will explore basic operations in scripting languages, delve into advanced visualization techniques, and investigate linear discriminants, generalized regressions, time series analysis, and non-linear discriminants, and clustering. Students will program essential algorithms, instead of using toolboxes, to explore the discrete Fourier transform, generalized regressions, clustering algorithms, and artificial neural networks.

Furthermore, the course will provide an understanding of relational databases and their integration with programming environments, as well as guidance on creating effective data analysis plans. Emphasis will be placed on solution architecture, reproducibility, configuration management, and generating standardized reports.

By the end of the course, students will have a strong foundation in data analytics, allowing them to transform raw data into valuable insights for decision-making.

Course Content

Week	Source	Topic	Prerequisites	SLOs
1		Description of the course project.
2		Ethics in data analysis.
3		Scripts vs. compiled code. Setting up environments.
4		Basic numeric operations in scripting vs. compiled code.
5		Visualization (basic and advanced).
6		Relational databases
7		Linear discriminants
8		Generalized regressions
9		Clustering
10		Non-linear discriminants (i.e. artificial neural networks).
11		Solution architecture & reproducibility.
12		Management of the configuration.
13		Data analysis plans & standardized reports.
14		Project presentations