Pre-requisite: COSC 242.
This course takes a practical, hands-on approach to making games. We will design, prototype, implement, polish and complete games over the 6-week course. How do we implement a game in a set time frame? How do we ensure its quality? What sets successful developers apart from everyone else with a good idea? Topics will include, but are not limited to: programming, project management, game design, visual design, and case studies from the industry.
This course is designed to introduce students to the multidisciplinary nature of computer game design, with the emphasis on technical skills and group work.
Assessment
Labs: 7%
First game: 11%
Game design: 11%
Game prototyping: 11%
Final presentation:: 6%
Final game 34%
Final examination (2 hours): 20%
Note that the internal assessments may change.
Please note: students wishing to take this course will need to have a strong background in computer programming. If you are interested in taking this paper it is important to apply as early as possible.
Pre-requisite: COSC242.
This paper aims to improve and develop programming skills by setting a series of exercises that require an analytical and creative approach to problem solving. Most – but not all – of these exercises will involve programming tasks. Some will not use computers at all; some will use them only for ancillary tasks. Each solution will be assessed against the requirements, and students will be expected to go back and rework each problem until it is completed satisfactorily. Students will be required to test and debug their programmes fully as well as learn to identify inefficiencies.
The main objectives of this course are to develop and foster general skills concerned with computer-related techniques, understanding a problem, problem-solving strategies and working with people. Most of the exercises will require working in pairs or in teams, although some exercises are individual.
Assessment
There are 15 assignments, called etudes, all of which must be successfully completed to pass the course.
This paper involves a considerable commitment of time and energy. Students are therefore discouraged from enrolling in a second paper.
An introduction to the methods and technologies used to build the information systems that run our modern world. You will learn how data is encoded for computer processing, the basics of algorithms and how machines execute algorithms to process data. In addition, you will learn the fundamental concepts of storing and managing data using relational databases, and how to manipulate these databases using query language. Finally, you will examine contemporary issues in Information and Communication Technology (ICT) and discuss how use of ICT impacts on our daily life.
This course should be of interest to any students wanting to gain a stronger understanding of how information is stored and manipulated in computer-based systems.
Assessment
In-class tests (2 × 5% and 1 × 10%): 20%
Assignments (2 × 10%): 20%
Final examination: 60%
Recommended Preparation: COMP150.
An introduction to the art and craft of computer programming and object-oriented design using Java. A first look at building graphical applications.
If you’re serious about computing, then COMP 160 is the key paper for you. It forms a base from which you can learn other programming languages and techniques. While it is suitable for students enrolled for any degree, it is particularly designed for students taking a BSc, BA or BCom degree.
Assessment
Mid-school test: 15%
Laboratory-based exercises: 25%
Final examination (2 hours): 60%
Students must pass the final examination in order to pass the paper
This course aims to enhance the capacity of students to benefit from information and communication technologies now and in the future. Students will explore the foundations and applications of Information and Communication Technology and examine its current and future impact on individuals, organisations and society. Students will apply widely used software applications to perform real-world business activities.
Assessment
Blackboard tests: 20%
Practical test: 30%
Final examination (2 hours): 50%
Prereq: (QST IS 223). Junior standing.
Designed to provide students with an overview of Agile Development methodologies. Introduces the various methods currently used in the industry and then focuses on the primary methodologies used today, SCRUM and Kanban. Students learn the tools of these software development approaches that produce deliverables to end users every two to four weeks, and analyze the value each of these methodologies brings into the development process and the reasoning behind a corporation selecting one method over the other (or a combination of both). In addition, students are introduced to CA Project Management software, the leader in the industry for SCRUM. Students learn to analyze requirements, create backlogs, schedule “stories” to be developed, hold Standup meetings, Reviews and Retrospectives.
Prereq: (CAS CS 112 & CAS CS 330) and familiarity with linear algebra, probability, and statistics.
Introduction to data mining concepts and techniques. Topics include association and correlation discovery, classification and clustering of large datasets, outlier detection. Emphasis on the algorithmic aspects as well as the application of mining in real-world problems.
Prereq: (CAS CS 112) or consent of instructor.
Topics in Computer Science. Introduction to application creation, written in Javascript, using the MEAN stack as examined from theoretical and practical perspectives. Culminating in a final session-long programming project.
Representation, analysis, techniques, and principles for manipulation of basic combinatoric structures used in computer science. Rigorous reasoning is emphasized.
This course fulfills a single unit in the following BU Hub area: Quantitative Reasoning II.
Prereq: (CAS CS 112).
Fundamental concepts of computer systems and systems programming. Hardware fundamentals including digital logic, memory systems, processor design, buses, I/O subsystems, data representations, computer arithmetic, and instruction-set architecture. Software concepts including assembly language programming, operating systems, assemblers, linkers, and systems programming in C. Students must register for two sections: lecture and discussion.
This course fulfills a single unit in the following BU Hub area: Quantitative Reasoning II.
For students with no previous experience with computers. Organization and function of computer systems; application of computers in today’s society; social impact of computers. Introduction to algorithms, various types of application packages, and the Internet. Not for computer science majors.
Prereq: (CAS CS 131 and CAS CS 210).
Concepts involved in the design of programming languages. Bindings, argument transmission, and control structures. Environments: compile-time, load-time, and run-time. Interpreters.
Prereq: (MET CS 544), equivalent knowledge, or instructor’s consent.
Provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples.
Prereq: (MET CS 232) or instructor’s consent.
Covers data structures using the Java programming language. Topics include data abstraction, encapsulation, information hiding, and the use of recursion, creation, and manipulation of various data structures: lists, queues, tables, trees, heaps, and graphs, and searching and sorting algorithms.
Fundamentals of logic (the laws of logic, rules of inferences, quantifiers, proofs of theorems). Fundamental principles of counting (permutations, combinations), set theory, relations and functions, graphs, trees and sorting.
Prereq: (MET CS546 and (MET CS520 or MET CS521)) or equivalent knowledge, or instructor’s consent.
Provides students with the mathematical and practical background required in the field of data analytics. Starting with an introduction to probability and statistics, the R tool is introduced for statistical computing and graphics. Different types of data are investigated along with data summarization techniques and plotting. Data populations using discrete, continuous, and multivariate distributions are explored. Errors during measurements and computations are analyzed in the course. Confidence intervals and hypothesis testing topics are also examined. The concepts covered in the course are demonstrated using R. Laboratory Course.
Prereq: (CAS CS 111; CAS MA 123 recommended).
Basic concepts, data structures, and algorithms for geometric objects. Examples of topics: Cartesian geometry, transformations and their representation, queries and sampling, triangulations. Emphasis on rigorous reasoning and analysis, advancing algorithmic maturity and expertise in its application.
Prereq: (CAS CS 112) or consent of instructor.
Topics in Computer Science. This course investigates new tools and mathematics that aid in understanding real-world graphs. The course begins with a quick review of basic graph theory (various definitions, some key concepts, theorems, and algorithms). It then introduces some more advanced concepts such as random graph theory and spectral graph theory. Using such tools, students investigate various models and concepts that attempt to describe real-world networks – scale-free networks, preferential attachment, sparse and inhomogenous models, etc. We also discuss some key recent results such as Szeremedi’s regularity lemma. The course also studies some past and future applications of these concepts that are relevant to students’ current interests. At least the half of the grade will be based on term projects to be done in small groups.
A comprehensive overview of the principles, processes, and practices of software project management. Students learn techniques for planning, organizing, scheduling, and controlling software projects. There is substantial focus on software cost estimation and software risk management. Students obtain practical project management skills and competencies related to the definition of a software project, establishment of project communications, managing project changes, and managing distributed software teams and projects.
Prereq: (QST IS 223).
Surveys the organizational implementation, uses, and impacts of advanced information technology including decision support systems, management support systems, and expert systems. Includes a group project to design and develop a decision support system.
Prereq: (CAS CS 112, CAS CS 131, and CAS CS 132) or (CAS CS 235) or (CAS CS 237).
Examines the basic principles of algorithm design and analysis; graph algorithms; greedy algorithms; dynamic programming; network flows; polynomial-time reductions; NP-hard and NP-complete problems; approximation algorithms; randomized algorithms.
This course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Critical Thinking.
The first course for computer science majors and anyone seeking a rigorous introduction. Develops computational problem-solving skills by programming in the Python language and exposes students to a variety of other topics from computer science and its applications.
Carries MCS divisional credit in CAS. This course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking.
Prereq: (CAS CS 111) or equivalent.
Covers advanced programming techniques and data structures. Topics include recursion, algorithm analysis, linked lists, stacks, queues, trees, graphs, tables, searching, and sorting.
Carries MCS divisional credit in CAS. This course fulfills a single unit in the following BU Hub area: Quantitative Reasoning II, Creativity and Innovation, Critical Thinking.
Provides a comprehensive overview of IT Project Management and the key processes associated with planning, organizing, and controlling of software projects. The course focuses on various knowledge areas such as project scope management, risk management, quality management, communications management, and integration management. Students are required to submit a term paper.
Prereq: (QST SM 131).
Provides students with an understanding of the important role that information and information technology play in supporting the effective operation and management of business. Elaborates on the themes of “place to space” and the implications for business of the digital enterprise. Focuses on learning IS concepts in the context of application to real business problems.
Introduction to the basic architecture and protocols underlying the operation of the Internet with an emphasis on Web design, Web application programming, and algorithmic thinking. General familiarity with the Internet is assumed.
Carries MCS divisional credit in CAS. This course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Digital/Multimedia Expression.
Introduction to problem-solving methods and algorithm development. Includes procedural and data abstractions, program design, debugging, testing, and documentation. Covers data types, control structures, functions, parameter passing, library functions, and arrays. Laboratory exercises in Python.
Prereq: (ENG EK 127 or ENG EK 128).
Introduction to software design, programming techniques, data structures, and software engineering principles. The course is structured bottom up, beginning with basic hardware followed by an understanding of machine language that controls the hardware and the assembly language that organizes that control. It proceeds through fundamental elements of functional programming languages, using C as the case example, and continues with the principles of object-oriented programming, as principally embodied in C++ but also its daughter languages Java, C#, and objective C. The course concludes with an introduction to elementary data structures and algorithmic analysis. Throughout, the course develops core competencies in software engineering, including programming style, optimization, debugging, compilation, and program management, utilizing a variety of Integrated Development Environments and operating systems.
Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.
Prereq: (Graduate standing or permission of instructor. Fundamental knowledge of programming and experience with a high-level programming language (i.e. Java, C++, Python), data structures and basic algorithms.)
Topics in Computer Science. Introduces principles and techniques of object-oriented programming. Focuses on specification, programming, analysis of large-scale, reliable, and reusable Java software using object-oriented design. Includes object models, memory models, inheritance, exceptions, namespaces, data abstraction, design against failure, design patterns, reasoning about objects.
Prereq: (CAS CS 131).
Introduction to basic probabilistic concepts and methods used in computer science. Develops an understanding of the crucial role played by randomness in computing, both as a powerful tool and as a challenge to confront and analyze. Emphasis on rigorous reasoning, analysis, and algorithmic thinking.
This course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Critical Thinking.
Prereq: (MET CS 201) or instructor’s consent.
Covers the elements of object-oriented programming and the C++ language. Data types, control structures, functions, library functions, classes, inheritance, and multiple inheritance. Use of constructors, destructors, function and operator overloading, reference parameters and default values, friend functions, input and output streams, templates, and exceptions.
Prereq: (MET CS 201) or instructor’s consent.
Covers the elements of object-oriented programming and the Java Programming Language. Primitive data types, control structures, methods, classes, arrays and strings, inheritance and polymorphism, interfaces, creating user interfaces, applets, exceptions and streams.
Examination of information technology in society, including Internet, World Wide Web, search engines (e.g., Google, Yahoo, Lycos), retrieval systems, electronic publishing, and distribution of media, including newspapers, books, and music. Exploration of many of these technologies, social, cultural, and political context in which they exist, and how social relationships are changing.
