This introductory data science course provides a comprehensive foundation in statistical reasoning, computational thinking, and mathematical modelling as it relates to data-driven decision-making. Students will learn to acquire, manage, analyze, and interpret diverse types of data using modern programming tools. Through hands-on projects involving real-world datasets from a variety of domains, students will develop skills in data wrangling, visualization, pattern discovery, and predictive modelling. Emphasis is placed on reproducible workflows, ethical considerations like privacy, and effective communication of data-driven insights. Designed for beginners, the course equips students to address complex, modern problems using the tools and concepts of data science.
This course is the culminating experience of the data science major, designed to integrate and apply the knowledge, skills, and tools students have developed throughout their coursework. Students will address a complex, real-world data science problem from start to finish, defining a research question or business challenge, acquiring and cleaning data, applying appropriate statistical and computational methods, developing models or analytical pipelines, and effectively communicating findings to both technical and non-technical audiences. Emphasis is placed on project management, ethical considerations, reproducibility, and professional presentation. The capstone provides students with an opportunity to demonstrate mastery of the data science process and to produce a portfolio-ready project that showcases their ability to generate actionable insights from data.
The student will undertake a significant project on a topic related to data science, under the supervision of a faculty member; graduate officer approval required.
The student will undertake a significant project on a topic related to data science, under the supervision of a faculty member; graduate officer approval required.