Bioinformatics is the interdisciplinary meeting point for computer science and molecular biology. It requires understanding of the knowledge domains of biology, chemistry, computer science, mathematics, and probability and statistics.

Bioinformatics focuses on the role of DNA and its associated biomolecules in encoding and regulating cellular processes which eventually manifest as heritable traits. Bioinformatics uses sophisticated computing techniques to model genetic behavior. Because the processes encoded in DNA are fundamentally information processes, bioinformatics also covers aspects of biological processes as computing mechanisms.

One major or ultimate goal of bioinformatics is to accurately predict organism-level characteristics from genomic information. This will require the characterization of very complex systems, from atomic to cellular and organism levels.

Featured classes

CMPT 270: Developing Object-Oriented Systems
Object-oriented programming. The use of modeling, abstractions, patterns, and GUIs to design and build a good OO system. Unit testing to ensure that it works. Application of the techniques to interactive systems.

BINF 351: Introduction to Bioinformatics
An introduction to the main concepts, techniques, data resources and terminology in bioinformatics. Topics include algorithms for sequence alignment, sequence assembly, phylogenetics, structure prediction, functional genomics, sequence motifs, proteomics, and genome annotation. Students will also learn to use major proteomic and genomic databases, to utilize bioinformatics software toolboxes, and to write simple bioinformatics programs in a scripting language.

BIOC 311: Introductory Molecular Biology
Basic principles and techniques of nucleic acid manipulations used in molecular biology and biotechnology are presented. Information and practical experience with plasmids, restriction endonucleases, PCR, DNA sequencing, site-directed mutagenesis, cloning, hybridization, analysis of RNA and gene promoters, and protein over-expression are presented. The laboratory component will also include an Internet exercise.

Information technology (IT) is one of the fastest growing sectors in Saskatchewan and around the world, and needs not only skilled computer programmers, but people who understand both coding and business practice. Leading software project teams, managing relationships with clients, and making strategic decisions about product development are all best undertaken by experts who understand both the potential and limitations of software and the imperatives and processes of business practice.

This degree program focuses on skills primarily in computer science with substantial contributions from the Edwards School of Business to provide knowledge and skills in several critical areas: fundamentals of computer programming and practice, the fundamentals of business practice, marketing, and software development principles.

Featured classes

CMPT 371: Software Management
Covers software management topics in the context of a significant group project. Includes: software process; process improvement; project tracking and metrics; project planning; project and group management; IT enterprise strategy and planning; software configuration management; deployment and maintenance; inspection; testing; verification and validation; and quality assurance.

COMM 349: Introduction to Entrepreneurship
Designed to provide both knowledge and evaluation skills needed to add value in the new venture sector of the economy. Students taking this course will acquire knowledge in respect to current concepts in entrepreneurship, primarily as it concerns the evaluation of entrepreneurs, their ventures, and the venturing environment.

CMPT 384: Information Visualization
This course will introduce visualization process for different datasets, design principals, techniques for developing effective visualizations, visualization algorithms and interaction techniques. The course is targeted to students interested in using visualization in their own work, as well as to those who are interested in developing visualization systems. Topics include: data abstractions, visualization process, design principles, visualizations of tabular data, geo-visualizations, cartographic representation, visualization for sets, temporal and hierarchical data (treemaps, radial layouts), network visualizations, visualization algorithms and software, interactions with large datasets, and a brief overview of visual analytics.

Data analytics has become a major growth area of IT, penetrating many more traditional industries with the promise of increased efficiency. Whether it is modeling customers for business, crops for agriculture, or voting intensions for politicians, data analytics has changed the way that we measure, model and understand the world we live in. Data analytics requires significant cross training in computer science, mathematics and statistics, as well as knowledge in the domain of application.

This program trains students in the mathematical theory and computational tools and techniques of data analysis. Data is now a core business commodity used to analyze everything from stock market performance to the voting intentions of particular groups, to the conservation status of protected species. Underlying these complex analyses are mathematical and computational tools that allow the manipulation of large amounts of data to extract meaning.

The data analytics concentration combines courses in computer science and mathematics and statistics to provide knowledge and skills in several critical areas: fundamentals of computer programming and practice, the fundamentals of data analytics, mathematical fundamentals for modelling, statistical measurement and reasoning, and machine learning.

Featured classes

CMPT 318: Data Analytics
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CMPT 423: Machine Learning
A survey of Machine Learning techniques, their underlying theory, and their application to realistic data. Machine learning techniques may include Neural Networks, Support Vector Machines, Bayesian networks, Hidden Markov Models, Particle Filtering; Expectation-Maximization; Sampling; Evaluation methodologies; Over-fitting and Regularization. Software tools will be introduced for practical application.

STAT 346: Multivariate Analysis
The multivariate normal distribution, multivariate analysis of variance, discriminant analysis, classification procedures, multiple covariance analysis, factor analysis, computer applications.

Geomatics is the quantitative study of relationships pertaining to the Earth’s surface, particularly focused on the analysis of satellite or drone images of the Earth or in GPS tracking across the Earth’s surface. Satellite, drone, and GPS data have revolutionized how we capture, analyze, and interpret spatially anchored data, such as the distribution and health of crops or the weekly shopping habits of individuals.

This program trains students in the theory, tools, and techniques of spatial data analysis. It combines courses in computer science and geography and planning to provide knowledge and skills in several critical areas: fundamentals of computer programming and practice, the acquisition and analysis of spatial data using geographic information systems, the interpretation of spatial data given existing theory, the fundamentals of data analytics, and the fundamentals of image processing.

The computer science component of the concentration in geomatics adds data analytics, visualization and image processing courses to the fundamentals listed above, providing you with the tools you need to analyze and present spatial data. This field has applications in civic planning, land and water management, agriculture, and business. Increasingly, firms are making decisions about investments based on satellite telemetry. In the field of geomatics, computer science provides the tools for analyzing data and geography provides the social and physical theoretical constructs which provide meaning to the data and analysis.

In upper years of this program, senior computer science courses are grouped into a software engineering option for students who wish to build geomatic software systems, an analytics option focused on AI and machine learning, and a user interface and visualization option for student who want to create interactive spatial tools. The options are voluntary, and students can mix and match as they deem appropriate.

Featured classes

CMPT 487: Image Processing and Computer Vision
Presents the fundamentals of theory and practice of image processing and computer vision. A range of topics are presented covering the phases of a typical image processing and computer vision pipeline: image preprocessing, image segmentation, region description, and classification/decision-making. Theory is practiced through computer programming assignments using a modern image processing library. Students completing this course can expect to be able to solve image processing and computer vision problems of up to moderate difficulty that increasingly arise across a wide range of disciplines and application areas.

GEOG 322: Introduction to Geographic Information Systems
Introduces students to the use of computer-based Geographic Information Systems for the management and analysis of spatial data for map production. Topics include vector and raster data structures, spatial data acquisition, geo-referencing, spatial interpolation, overlay analysis, and modelling. Students obtain practical experience with Geographical Information Systems through a series of exercises.

PLAN 360: Urban Data Analysis and Visualization
Several forms of urban data exist that pertain to the residents’ demographics and travel behaviours, neighbourhoods urban form and land uses, and cities transportation and infrastructure systems. In this course, students will focus on integrating, analyzing and mapping several types of most common urban datasets, developing their quantitative reasoning and visualization skills, within the scope of the planning profession.

The interactive systems design concentration trains students in all aspects of the design and development of interactive systems. Interactive systems are now a ubiquitous part of people’s lives – from web applications to games to embedded devices – and the design and usability of these systems are having an increasingly large effect on the quality of people’s relationship to technology.

Interactive systems design combines courses in art and art history, psychology, and computer science, and these courses provide knowledge and skills in several critical areas: principles of visual communication; critical approaches to visual systems; fundamentals of human perception, memory, and cognition; and the principles of computation and programming needed to design, build, and evaluate games and interactive systems.

Featured classes

CMPT 281: Website Design and Development
Introduction to design concepts and issues in the development of usable applications on the World Wide Web, including visual design concepts, the user-centered iterative design process, and development technologies that enable application development for the Web.

CMPT 381: Implementation of Graphical User Interfaces
Advanced introduction to concepts and structures used to develop GUIs in software, focusing on building user interfaces. Covers the fundamentals of GUI toolkits including input, widgets, layout, events, model-view-controller architectures, and two-dimensional graphics.

CMPT 481: Human Computer Interaction
Fundamental theory and practice in the design, implementation, and evaluation of human-computer interfaces. Topics include: principles of design, usability engineering, methods for evaluating interfaces with or without user involvement, techniques for prototyping and implementing graphical user interfaces.

Faculty
Our faculty are excited about the dynamic disciplines of computer science and applied computing, and exploring new ways in which computing can change the lives of people everywhere. They provide solid classroom instruction and offer laboratory experience in state-of-the-art facilities.

Interdisciplinary learning
The applied computing program offers a unique interdisciplinary experience at USask. Depending on your chosen concentration, you will have the opportunity to learn from instructors in the following fields: mathematics and statistics, geography and planning, art, psychology, and biology, as well as from Edwards School of Business.

The Computer Science Professional Internship Program allows undergraduate students to obtain extended periods of practical, "on-the-job" experience with sponsoring companies across Canada. Students can apply to participate prior to completing the final year of the undergraduate degree program.