Mount Allison University Campus

Academic Calendar 2023-2024

Table of Contents

Data Science

Data Science is the emerging interdisciplinary study and application of how we capture, organize, archive, access, and use large-scale data. Data Science alters disciplinary and professional practices, enabling new conceptual approaches and categories of questions, while generating new challenges in ethics and privacy. These issues and opportunities now pervade many areas of human endeavour, from physics to health care to social policy.

To successfully engage with the scope and scale of data resources requires high throughput approaches, with new concepts and practices for effective management, distribution, and presentation of data. Our goal is to give students the concepts and tools to empower their ethical engagement with the emerging potentials and challenges of data, across disciplines and fields of enquiry.

The Minor is designed to engage and serve the interests of a wide range of students. It emphasizes interdisciplinary opportunities and challenges of data science, which supports and extends many disciplines and professional areas. Skills in data science are thus highly transferrable across many areas of practice.

Certificates in Data Management and Data Analytics are also available (see Certificate Programs in Section 12 Programs and Courses of Instruction).

Interdisciplinary B.Sc. Programs

MINOR in Data Science is 24 credits earned as follows:

6from MATH 1311, 2221
3from COMP 1631
3from ECON 2701, BIOL 2701, MATH 2321, PSYC 2011, GENS 2431, PHYS 2801
9from DATA 3001, 3101, 4001
3from ECON 4711, BIOL 4711, MATH 4311, PSYC 3001, GENS 4721

Note:  There are prerequisites for some 2000 level courses in this minor. Students are responsible for ensuring that they have the necessary prerequisites. It is recommended that students meet with the program director early on to map out the minor.


Note:  The listing of a course in the Calendar is not a guarantee that the course is offered every year.

Note:  Students must obtain a grade of at least C- in all courses used to fulfill prerequisite requirements. Otherwise, written permission of the appropriate Department Head or Program Co-ordinator must be obtained.

Data Visualization and Communication

Prereq: MATH 1311; or permission of the Department
This course will build on concepts established in MATH 1311 to cover accurate and effective visualization and communication of data to both technically trained audiences and to the wider public. Students will learn to organize diverse data types for efficient static, dynamic, and interactive visual presentations to effectively communicate key messages in multiple formats. Students will learn approaches to high throughput report generation and content updating, with principles of open data and maintaining audit trails from presentation back to source. The course will cover common pitfalls or distortions of data presentation, and principles of visual grammar and accessibility for diverse users. (Format: Integrated Tutorial and Laboratory 6 hours)

Data Acquisition and Organization

Prereq: MATH 1311; or permission of the Department
This course will build on MATH 1311 to cover high throughput acquisition and management of data. The course will use diverse data types and formats to illustrate conceptual challenges across disciplines. Technical aspects will include evolving approaches to script-based web scraping, file formats and conversions, data mergers and tidying, meta-data organization and capture. In parallel, the course will cover theoretical and ethical aspects of data curation and access policies, development of best practices for research data management and case studies in secure management of sensitive or private data.(Format: Integrated Lecture and Laboratory 3 hours)

Advanced Experiential Data Science

Prereq: MATH 1311; 3 credits from DATA 3001, 3101; or permission of the Department
This capstone course will give upper-year students the opportunity to work on data of interest to them, captured either through direct measurement or through access to open data sets, in order to develop management and analysis competencies applicable to their disciplinary area(s). In this integrated seminar and laboratory, students will regularly present their individual works in progress and discuss specialized advanced topics in data science, as relevant both to broad experiential applications, and to their specific projects. The course will allow students to deeply engage with a self-determined data-handling project within their discipline, cultivate a transferrable skill set, and work in an interdisciplinary, collaborative environment to experience a breadth of different applications and perspectives on data science.(Format: Integrated Seminar and Laboratory 3 hours)