Designed to prepare the student for Microsoft certification exams in PowerPoint, Excel, and Outlook, this course will guide the student in developing the skills necessary to be proficient in each of the software applications.

An introduction to an object oriented programming language, algorithm design, structured and object-oriented programming techniques. No prior programming experience is assumed. Prerequisite: higher algebra.

Intermediate data structures and techniques of object-oriented and structured programming. Discrete data types and structures, including arrays, files, sets, lists, trees, hash tables, sorting and recursion. Small to medium-scale programs are developed.

Basics of programming echniques for the World Wide Web. Provides an introduction to several web design methodologies including methodologies for data access and presentation.

Basics of software development for mobile devices. Provides an introcution to programming techniques for mobile devices including mobile web access and mobile access to databases.

An introduction to database theory and practice. Topics include relational database design, ER modeling, normalization, SQL/embedded SQL, concurrency control, data warehousing and other emerging database technologies. Practical software engineering principles are emphasized through student projects.

An introduction to the theory and practice of quantitative modeling and optimization, with applications to computer simulation and business resource management. Possible topics include linear and nonlinear programming, network analysis, game theory, deterministic and probabilistic models. Prerequisite: consent of the instructor.

An overview of the systems development process. Includes: tools/techniques for describing processes, data flows, data structures, file designs, input/output designs, program specifications and prototyping for systems. Discovery, problem-solving and communications skills as employed by the systems analyst are also covered.

This course is introduction to the fundamental concepts in the design and implementation of computer networks. Topics include network topologies, OSI and TCP/IP reference models, local area networks, Wi-Fi, routing. Examples and projects will focus primarily on TCP/IP protocols.

An opportunity to study in depth an advanced topic of current interest. Students work as teams to complete several extended research projects.

This course is intended to give a wide exposure to the history and current state of the field of Artificial Intelligence. Students will be introduced to the different Artificial Intelligence methodologies and familiarized with the relative strenghts and weaknesses of these technologies.

A study of how computers manage their resources. Highlights include concurrency, memory management, process and processor management and scheduling, device control, performance evaluation and system security. Several operating systems are compared.

An introduction to principles of programming language design. Topics include regular and context-free grammars, parsing, static and dynamic scoping, and type checking. Students will explore the dimensions of computer languages drawn from several different programming paradigms.

Provides an introduction to a variety of topics in computer security both from a technical and from a human resource point of view.

This course will allow the students to apply all their knowledge from the computer science major to implement a real world software project. Students will simultaneously learn techniques for insuring quality software and will apply these techniques among other techniques to implement a software project with direct applicability to a large problem situation.

This course provides an opportunity for individual students to conduct in-depth study of a particular topic under the supervision of a faculty member. Contact the department or program chair for more information.

A study of the mechanisms for interaction (i.e. user interfaces) between users and computing equipment whether this computing equipment comes in the form of a computer or of a computing system embedded within any other system (manufacturing machinery controllers, medical equipment, aircraft, traffic lights, home appliances...etc.) Human computer interaction focuses on user satisfaction as well as ensuring user interfaces that avoid erroneous use of computing equipment that may at times have catastrophic results.

This course provides an opportunity for individual students to conduct research in a specific area of study, completed under the direction of a faculty mentor. Specific expectations of the research experience to be determined by the faculty. Repeatable for credit. Prerequisite: consent of instructor.

This is an introductory course in using modern data analysis concepts and tools to gain insight and make decisions in a business or organizational setting. Topics include data storage, business intelligence, basic data mining and modeling, visualization, prediction/forecasting, and clustering/segmentation. Students will complete at least one data analytics project, starting from an original research question and concluding with actionable recommendations.

An introduction to the construction and analysis of least-squares models, including multiple regression, ANOVA, ANCOVA, and mixed models. Generalized linear models will also be presented, with special attention paid to logistic regression and log-linear models. Examples and applications will be drawn from various disciplines, including biology, medicine, economics, engineering, and the social sciences.

Forecasting is the science of predicting future events and outcomes. In this course students will learn how to effectively use both data and theory to create forecasts and how to quantify and communicate uncertainty in forecasts. Topics include random walks, Markov models, time series analysis, Bayesian methods and qualitative forecasting.

Data mining is the study of discovering and assessing patterns, relationships and information within large datasets. This course provides an introduction to data mining with an emphasis on predictive modeling techniques and machine learning algorithms. Examples and applications will be drawn from various disciplines.

Courses covering various topics of interest in this particular discipline are offered regularly. Contact department or program chair for more information.

This course provides an opportunity for individual students to conduct in-depth study of a particular topic under the supervision of a faculty member. Contact the department or program chair for more information.

This course provides an opportunity for individual students to conduct research in a specific area of study, completed under the direction of a faculty mentor. Specific expectations of the research experience to be determined by the faculty. Repeatable for credit. Prerequisite: consent of instructor.

This course focuses on technical and visual aspects of inspecting and presenting data. Technical topics include importing data from various sources, establishing relationships between data tables, transforming data, filtering, sorting, and aggregation. Visuals will be designed to focus attention on what the data is saying, with a special focus on visuals that respond dynamically to user manipulations. Emphasis will be placed on the design/refinement cycle for visualizations.

This course allows the student to understand and demonstrate knowledge of descriptive and inferential statistics used in research, and apply their knowledge to real-world situations and research questions. Emphasis is placed on distinguishing similarities and differences among statistical tests, and recognizing the essentiality of statistics for producing and comprehending scientific research

Forecasting is the science of predicting future events and outcomes. In this course students will learn how to effectively use both data and theory to create forecasts and how to quantify and communicate uncertainty in forecasts. Topics include random walks, Markov models, time series analysis, Bayesian methods and qualitative forecasting.

Data mining is the study of discovering and assessing patterns, relationships and information within large data sets. This course provides an introduction to data mining with an emphasis on predictive modeling techniques and machine learning algorithms. Examples and applications will be drawn from various disciplines.

Students will learn specialized applications of operations research to problems arising from business. These will include data envelope analysis, transportation/transshipment problems, goal programming, network models (including PERT-CPM), and capital budgeting. Other topics such as inventory models, facility location problems, etc. will be covered as time and student interest permit. Special attention will be paid to the development and analysis of models for realistic medium- to large-scale problems.

The main purpose of the capstone course is to provide the culminating, integrative curricular experience for students. The course consolidates students' learning to develop a project with knowledge gained from many areas in the MSQM. The focuses of the course are case analyses and professional development.

Integrative Capstone Experience II's main purpose is to provide a structured means for students to get hands-on experience in real-life business analytics practices. Students will apply skills and knowledge gained throughout the MSQM program, such as statistical techniques, models, and analytical decision-making that support the business-defined problems scoped collaboratively between companies and Concordia.