- HBA · 4 years
- Work experience details
- ~High 80s to low 90s



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Work experience
Optional paid Science Internship Program: 4, 8, 12 or 16 months, subject to programme eligibility.
University · exact programme work-study requirementsEntering average
Published approximate guidance: Published three-cycle historical guidance: Faculty of Science: “low 80s to mid 90s”. These are the university’s literal approximate words, not exact numeric endpoints or a forecast for the next cycle. The Faculty of Science separately publishes 70% for BSc Major and 80% for BSc Honors applications.. Three admission cycles preceding the current page checked 2026-10-30; specific cycle years are not named. The central page states a general 70% floor across five programme-required subjects, with Grade 11 marks at least 60% and Grade 12 marks at least 50%. Individual programme requirements, capacity and actual offer conditions still govern. A broad faculty trend is not a major-specific admitted-average distribution. Major and Honors are separate routes, so no combined minimum is assigned.
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2027 entry
Admission, entrance awards and residence applications open on the same date.
ApplyAlberta publishes NO deadline dates at all on its public pages. Its How to Apply page describes the process only: verbatim, "Now you will 'Start a New Application' at the institution of your choice. You will be directed to the institution's application portal where you will apply and pay any application fees", and "All application fees are paid directly to the institution you are applying to. Each institution has its own unique application fees". Deadlines must be read from each of Alberta's 26 publicly-funded institutions.
Entry terms · Fall
Fall 2027 University of Alberta undergraduate entry at North Campus.
University of Alberta Science admissions and application minimaAlberta high-school applicants, 2026/27 published course requirements for this degree route
British Columbia high-school applicants, 2026/27 published course requirements for this degree route
Manitoba high-school applicants, 2026/27 published course requirements for this degree route
New Brunswick high-school applicants, 2026/27 published course requirements for this degree route
Newfoundland and Labrador high-school applicants, 2026/27 published course requirements for this degree route
Northwest Territories high-school applicants, 2026/27 published course requirements for this degree route
Nova Scotia high-school applicants, 2026/27 published course requirements for this degree route
Nunavut high-school applicants, 2026/27 published course requirements for this degree route
Ontario high-school applicants, 2026/27 published course requirements for this degree route
Prince Edward Island high-school applicants, 2026/27 published course requirements for this degree route
Quebec high-school applicants, 2026/27 published course requirements for this degree route
Saskatchewan high-school applicants, 2026/27 published course requirements for this degree route
Yukon high-school applicants, 2026/27 published course requirements for this degree route
International applicants high-school applicants, 2026/27 published course requirements for this degree route
Applicants with postsecondary coursework; each faculty controls actual entry conditions
Alberta direct high-school applicants
Applicants to the Honors degree
The central page states a general 70% floor across five programme-required subjects, with Grade 11 marks at least 60% and Grade 12 marks at least 50%. Individual programme requirements, capacity and actual offer conditions still govern. A broad faculty trend is not a major-specific admitted-average distribution. Major and Honors are separate routes, so no combined minimum is assigned.
Alberta current general minimum and literal three-cycle competitive trendsAlberta current general minimum and literal three-cycle competitive trendsPrevious Computing Science internship hosts named by the department include IBM, BioWare, Electronic Arts, SMART Technologies, Intuit and Red Hat.
University work-study informationCurrent entrants choose Major or Honors. Artificial Intelligence and Software Practice are separately named options in each degree.
BSc
Focus on artificial intelligence within Computing Science.
Current 2026/27 degree requirements and named option banks. Alternatives are included as a menu; each degree and major variant has its own requirements. This is not a class timetable. Standard introductory sequence for the BSc Computing Science major.
The department also publishes a separate advanced introductory sequence.
CMPUT 174 and CMPUT 175 use a problem-driven approach to introduce the fundamental ideas of Computing Science. Emphasis is on the underlying process behind the solution, independent of programming language or style. Basic notions of state, control flow, data structures, recursion, modularization, and testing are introduced through solving simple problems in a variety of domains such as text analysis, map navigation, game search, simulation, and cryptography. Students learn to program by reading and modifying existing programs as well as writing new ones. No prior programming experience is necessary.
Alberta 2026–27 current official course definitionA continuation of CMPUT 174, revisiting topics of greater depth and complexity. More sophisticated notions such as objects, functional programming, and Abstract Data Types are explored. Various algorithms, including popular searching and sorting algorithms, are studied and compared in terms of time and space efficiency. Upon completion of this two course sequence, students from any discipline should be able to build programs to solve basic problems in their area, and will be prepared to take more advanced Computing Science courses.
Alberta 2026–27 current official course definitionFunctions, continuity, and the derivative. Applications of the derivative. Extended limits and L'Hospital's rule.
Alberta 2026–27 current official course definitionThe derivative as a rate of change. Differentiation of elementary, trigonometric, exponential, and logarithmic functions. The definite integral as a summation. Integration. The Fundamental Theorem of Calculus. Applications in the context of the life sciences.
Published note: Credit can be obtained in at most one of MATH 100, 113, 114, 117, 134, 144, 154 or SCI 100.
Alberta 2026–27 current official course definitionThe derivative as a rate of change. Differentiation of elementary, trigonometric, exponential, and logarithmic functions. The definite integral as a summation. Integration. The Fundamental Theorem of Calculus. Taylor polynomials. Applications in the context of the physical sciences.
Published note: Credit can be obtained in at most one MATH 100, 113, 114, 117, 134, 144, 154 or SCI 100.
Alberta 2026–27 current official course definitionThe derivative as a rate of change. Differentiation of elementary, trigonometric, exponential, and logarithmic functions. The definite integral as a summation. Integration. The Fundamental Theorem of Calculus. Optimization. Applications in the context of business and economics.
Published note: Credit can be obtained in at most one of MATH 100, 113, 114, 117, 134, 144, 154 or SCI 100.
Alberta 2026–27 current official course definitionIntegration and the Fundamental Theorem. Techniques and applications of integration. Derivatives and integrals of the exponential, and trigonometric functions. Introduction to infinite series. Introduction to partial derivatives.
Alberta 2026–27 current official course definitionTechniques and applications of integration. Improper integrals. Differential equations and mathematical modelling. Partial differentiation. Applications in the context of the life sciences.
Published note: Credit can be obtained in at most one of MATH 101, 115, 118, 136, 146, 156 or SCI 100.
Alberta 2026–27 current official course definitionTechniques and applications of integration. Improper integrals. Introduction to differential equations. Partial differentiation. Applications in the context of the physical sciences.
Published note: Credit can be obtained in at most one of MATH 101, 115, 118, 136, 146, 156 or SCI 100.
Alberta 2026–27 current official course definitionTechniques and applications of integration. Improper integrals. Partial differentiation. Multivariate optimization. Probability and calculus. Applications in the context of business and economics.
Published note: Credit can be obtained in at most one of MATH 101, 115, 118, 136, 146, 156 or SCI 100.
Alberta 2026–27 current official course definitionSystems of linear equations. Vectors in n-space, vector equations of lines and planes. Matrix algebra, inverses and invertibility. Introduction to linear transformations. Subspaces of n-space. Determinants. Introduction to eigenvalues and eigenvectors. Complex numbers. Dot product, cross product and orthogonality. Applications in a variety of fields.
Published note: Credit can be obtained in at most one of MATH 102, 125 or 127.
Alberta 2026–27 current official course definitionLinear equations; Euclidean spaces, matrices. Complex numbers and fields. Vector spaces : basis, dimension, linear transformations. Introductions to groups and rings; permutation groups. Determinants. Eigenvalues and Eigenvectors.
Published note: Credit can be obtained in at most one of Math 102, 125 or 127.
Alberta 2026–27 current official course definitionData collection and presentation, descriptive statistics. Probability distributions, sampling distributions and the central limit theorem. Point estimation and hypothesis testing. Correlation and regression analysis. Goodness of fit and contingency table.
Alberta 2026–27 current official course definitionDescriptive data analysis. Calculus of Probability. Binomial, multinomial, Poisson, normal, beta, exponential, gamma, hypergeometric, and Weibull distributions. Sampling distributions. Estimation, testing hypotheses, goodness-of-fit tests, and one-way analysis of variance. Linear correlation and regression. Sampling. Quality control. Use of a microcomputer software package for statistical analyses in engineering applications.
Alberta 2026–27 current official course definitionSample space, events, combinatorial probability, conditional probability, independent events, Bayes Theorem, random variables, discrete random variables, expected values, moment generating function, inequalities, continuous distributions, multivariate distributions, independence.
Published note: Credit can be obtained in at most two of STAT 181, STAT 281, or STAT 265.
Alberta 2026–27 current official course definitionIntroduction to the principles, methods, tools, and practices of the professional programmer. The lectures focus on the fundamental principles of software engineering based on abstract data types and their implementations. The laboratories offer an intensive apprenticeship to the aspiring software developer. Students use C and software development tools of the Unix environment.
Alberta 2026–27 current official course definitionThe first of two courses on algorithm design and analysis, with emphasis on fundamentals of searching, sorting, and graph algorithms. Examples include divide and conquer, dynamic programming, greedy methods, backtracking, and local search methods, together with analysis techniques to estimate program efficiency.
Alberta 2026–27 current official course definitionNumber representation, computer architecture and organization, instruction-set architecture, assembly-level programming, procedures, stack frames, memory access through pointers, exception handling, computer arithmetic, floating-point representation, datapath, control logic, pipelining, memory hierarchy, virtual memory.
Alberta 2026–27 current official course definitionAn introduction to the tools of set theory, logic, and induction, and their use in the practice of reasoning about algorithms and programs. Basic set theory; the notion of a function; counting; propositional and predicate logic and their proof systems; inductive definitions and proofs by induction; program specification and correctness.
Alberta 2026–27 current official course definitionBasic concepts in computer data organization and information processing; entity-relationship model; relational model; SQL and other relational query languages; storage architecture; physical organization of data; access methods for relational data. Programming experience (e.g., Python) is required for the course project.
Alberta 2026–27 current official course definitionThis course covers the issues of ethics, privacy, algorithmic fairness, explainability and transparency of data and algorithms, and the legal and regulatory frameworks for these issues. The course also includes a module on Indigenous principles in data governance. The objectives are to learn how to identify and measure these aspects in outputs of algorithms, and how to build algorithms that correct for these issues. The course introduces these concepts with real case studies, followed by a technical treatment of the topics. Students will learn and implement basic data science and machine learning methods, and tools and techniques for privacy and mitigation of algorithmic unfairness.
Alberta 2026–27 current official course definitionSocial, ethical, professional, economic, and legal issues in the development and deployment of computer technology in society.
Alberta 2026–27 current official course definitionThis topics course is designed for a one on one individual study course between a student and an instructor. Prerequisites for each section may differ and are defined by the instructor in the course outline.
Alberta 2026–27 current official course definitionAll phases of software development are reviewed from a process perspective. Best practices in software project and product development and management are introduced. Architectural and technological impacts on management. Group projects require specification and initial design or redesign of a software system.
Alberta 2026–27 current official course definitionStudents will experience the challenges, and rewards, of working in a team to address a real-world task, related to artificial intelligence or machine learning. This will involve first identifying the task itself, then iteratively addressing relevant issues (typically with feedback from a domain expert), leading to an implementation and culminating in evaluating that system. Students will also learn about best practices in organizing team projects, as well as important information about effective communication.
Alberta 2026–27 current official course definitionThis topics course is designed for a one on one individual study course between a student and an instructor. Prerequisites for each section may differ and are defined by the instructor in the course outline.
Alberta 2026–27 current official course definitionMethods in applied statistics including regression techniques, analysis of variance and covariance, and methods of data analysis. Applications are taken from Biological, Physical and Social Sciences, and Business.
Alberta 2026–27 current official course definitionFunctions of random variables, sampling distributions, Central Limit Theorem, law of large numbers, statistical models for the data, likelihood, parameters and their interpretation, objectives of statistical inference, point and interval estimation, method of moments, basic notions of testing of hypotheses, errors of the first and second kind, significance level, power, p-value.
Alberta 2026–27 current official course definitionIntroduction to artificial intelligence focusing on techniques for building intelligent software systems and agents. Topics include search and problem-solving techniques, knowledge representation and reasoning, reasoning and acting under uncertainty, machine learning and neural networks.
Alberta 2026–27 current official course definitionThis course introduces the fundamental statistical, mathematical, and computational concepts in analyzing data. The goal for this introductory course is to provide a solid foundation in the mathematics of machine learning, in preparation for more advanced machine learning concepts. The course focuses on univariate models, to simplify some of the mathematics and emphasize some of the underlying concepts in machine learning, including: how should one think about data, how can data be summarized, how models can be estimated from data, what sound estimation principles look like, how generalization is achieved, and how to evaluate the performance of learned models.
Alberta 2026–27 current official course definitionThis course provides an introduction to reinforcement learning, which focuses on the study and design of learning agents that interact with a complex, uncertain world to achieve a goal. The course will cover multi- armed bandits, Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information-processing approach to the study of intelligence and briefly touch on perspectives from psychology, neuroscience, and philosophy. The course will use the University of Alberta MOOC on Reinforcement Learning. Any student who understands the material in this course will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses, or to apply AI tools and ideas to real-world problems.
Alberta 2026–27 current official course definitionThis course provides an introduction to search and planning in artificial intelligence. The course covers deterministic single-agent and multi-agent problems. Students will learn how to model real-world problems as state-space search problems and how to solve such problems. The course covers algorithms for solving deterministic shortest path problems with factored and non-factored states, combinatorial optimization problems, constraint satisfaction problems, and multi- agent problems.
Alberta 2026–27 current official course definitionThis is the second course of a two-course sequence on machine learning, with a focus on extending to nonlinear modeling with neural networks and higher-dimensional data. Topics include: optimization approaches (constrained optimization, hessians, matrix solutions), deep learning and neural networks, generative models, more advanced methods for assessing generalization (cross-validation, bootstrapping), introduction to non-iid data and missing data.
Alberta 2026–27 current official course definitionAlgorithms and software paradigms for robot programming; mathematical modeling of robot arms and rovers including kinematics, and an introduction to dynamics and control; sensors, motors and their modeling; basics of image processing and machine vision; vision-guided motion control.
Alberta 2026–27 current official course definitionIntroduction to visual recognition to recognize objects and classify scenes or images automatically by a computer. Supervised and unsupervised machine learning principles and deep learning techniques will be utilized for visual recognition. Successful commercial systems based on visual recognition range from entertainment to serious scientific research: face detection and recognition on personal devices, social media.
Alberta 2026–27 current official course definitionComputer arithmetic and errors. The study of computational methods for solving problems in linear algebra, non-linear equations, optimization, interpolation and approximation, and integration. This course will provide a basic foundation in numerical methods that supports further study in machine learning; computer graphics, vision and multimedia; robotics; and other topics in Science and Engineering.
Alberta 2026–27 current official course definitionThis course focuses on building efficient video game engines using C++. Programming-specific topics include object-oriented programming, memory management, data efficiency, and the Standard Template Library. These topics are applied to design 2D game engines with object-oriented and/or entity-component system (ECS) methods. These engines are built upon concepts such as sprites, cameras, object collisions, and shaders. Student projects give hands-on experience directly applicable to the video games industry.
Alberta 2026–27 current official course definitionA project-based course dealing with the design and implementation of mobile robots to accomplish specific tasks. Students work in groups and are introduced to concepts in sensor technologies, sensor data processing, motion control based on feedback and real-time programming.
Alberta 2026–27 current official course definitionWhen making decisions in games, computers rely on three main ideas: search, knowledge and simulations. Knowledge can be created by machine learning techniques and encoded in deep neural networks. Search and simulations help to understand the short and long-term consequences of possible actions. This course leads from basic concepts to state-of-the-art decision-making algorithms.
Alberta 2026–27 current official course definitionNatural language processing (NLP) is a subfield of artificial intelligence concerned with the interactions between computers and human languages. This course is an introduction to NLP, with the emphasis on writing programs to process and analyze texts, covering both foundational aspects and applications of NLP. The course aims at a balance between classical and statistical methods for NLP, including methods based on machine learning.
Alberta 2026–27 current official course definitionProbabilistic graphical models (PGMs; including Bayesian Belief Nets, Markov Random Fields, etc.) now contribute significantly to many areas, including expert systems, computer perception (vision and speech), natural language interpretation, automated decision making, and robotics. This course provides an introduction to this field, describing semantics, inference and learning, as well as practical applications of these systems. Programming assignments will include hands-on experiments with various reasoning systems.
Alberta 2026–27 current official course definitionObject-oriented design and analysis, with interactive applications as the primary example. Topics include: software process; revision control; Unified Modeling Language (UML); requirements; software architecture, design patterns, frameworks, design guidelines; unit testing; refactoring; software tools.
Alberta 2026–27 current official course definitionA study of the theory, run-time structure, and implementation of selected non-procedural programming languages. Languages will be selected from the domains of functional, and logic-based languages.
Alberta 2026–27 current official course definitionIntroduction to the structure, components, and concepts behind modern general-purpose operating systems. Processes: process state transitions; operations on processes; interrupt processing; multiprocessor considerations; resource allocation; synchronization; critical sections and events; semaphores; deadlock: avoidance, detection, and recovery; memory management; virtual memory; paging and segmentation; page replacement strategies; working sets; demand paging; process scheduling; scheduling algorithms; file system functions; file organization; space allocation; virtual machines.
Alberta 2026–27 current official course definitionSoftware quality issues are covered. Various types of software testing, ranging from unit testing to integration testing are discussed. Processes to ensure quality, such as reviews and continuous integration, are introduced. State-of-the-art software quality tools that analyze different artifacts within the software lifecycle are described.
Alberta 2026–27 current official course definitionThe second course of a two-course sequence on algorithm design. Emphasis on principles of algorithm design. Categories of algorithms such as divide-and-conquer, greedy algorithms, dynamic programming; analysis of algorithms; limits of algorithm design; NP-completeness; heuristic algorithms.
Alberta 2026–27 current official course definitionFormal grammars; relationship between grammars and automata; regular expressions; finite state machines; pushdown automata; Turing machines; computability; the halting problem; time and space complexity.
Alberta 2026–27 current official course definitionUses Python and Linux to practice procedural programming, sorting and searching, and foundational data structures. The studio combines lectures and labs. Math 30-1 is required and prior programming is recommended.
University of Alberta CMPUT 274 course catalogueUses C++ to introduce object-oriented programming and algorithms such as shortest paths, divide-and-conquer and dynamic programming. CMPUT 274 is the prerequisite.
University of Alberta CMPUT 275 course catalogueCurrent entrants choose Major or Honors. Artificial Intelligence and Software Practice are separately named options in each degree.
Published Computing Science career examples. The department separately reports previous internship employers.
Computing Science
2026–27 domestic · Fall 2027 international entry · Tuition basis varies by category · CAD
Canadian students example · Choose your category
Domestic example is one standard course with fee index 6. International tuition covers one Fall/Winter academic year. Student fees are separate.
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