MDC5153

Data Analytics

Pre-requisite: MAT2243 Applied Linear Algebra or equivalent set of courses.

This immersive Data Analytics course equips students with the essential skills and knowledge required to analyze, visualize, and interpret complex datasets. Students will learn the importance of ethics in data analysis and how to set up a suitable environment for efficient processing. Throughout the course, participants will explore basic operations in Python, delve into advanced visualization techniques, and investigate linear and non-linear discriminants, such as 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		Environment setup.
4		Basic operations in python.
5		Visualization (basic and advanced).
6		Linear discriminants & regressions.
7		Relational databases.
8		Relational databases from python.
9		Non-linear discriminants (i.e. artificial neural networks).
10		Data analysis plans.
11		Solution architecture & reproducibility.
12		Management of the configuration.
13		Standardized reports.
14		Project presentations