This course is designed to give non-data analysts an overview of big data, the status of practice in analytics, the role of the data scientists, big data analytics in industry verticals, and data analytics lifecycle as an end-to-end process. This course aims at providing the ability to make effective decisions in a data-driven manner.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00IND 110 - Data Organization for Data AnalystsCourse DescriptionThis course provides a foundation in data management for data analysts. Topics include database architectures, formation of queries, queries themselves, data warehousing, relational database systems, NoSQL, and responsibilities of data management professionals.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00IND 119 - Introduction to Big DataCourse DescriptionThis course is designed to give students an overview of big data, state of the practice in analytics, the role of the data scientist, big data analytics in industry verticals, and analytics life-cycle as an end-to-end process. It focuses on key roles for a successful analytic project, main phases of the life-cycle, developing core deliverables for stakeholders, team work skills, and problem solving skills.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00IND 123 - Data Analytics: Basic MethodsCourse DescriptionThis course is an introduction to R, analyzing and exploring data with R, and using R with a database. It focuses on statistics for model building and evaluation. Topics cover experimental research, correlation analysis, regression, confidence intervals, and group comparisons, and parametric and non-parametric models.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00IND 300 - Introduction to ManagementCourse DescriptionThe study of theories and practices of management will be introduced with an understanding of the environment in which they operate. The task of the worker, industrial organizations and their culture, the formation and operation of a union and the Canadian industrial relations will be discussed; the dominant North American management theories and applications will be examined, and the Japanese industrial organization and the new role of management will be studied.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:MTH 141, MTH 240, CPS 188, CMN 432, CEN 199IND 303 - Work Measurement, Analysis and DesignCourse DescriptionGeneral IE functions are introduced. Operation process chart, flow process chart, flow diagram, worker and machine process chart, and gang process chart are considered as recording and analysis tools. Principles of motion economy and motion study are discussed for manual work design. Work measurement tools covered include predetermined time systems: MTM-1, MTM-2, MTM-3, Maynard Operation Sequence Technique (MOST) and introduction to computer-based MOST; time-study systems: fundamentals of continuous and snap-back techniques for stop-watch, datamyte and palm-pilots; and analytical systems: work sampling and standard data development. Student teams undertake an open-ended work-system design project that requires the integration and analysis of the topics covered.Weekly Contact:Lecture 3 hrs. Lab 2 hrs.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CEN 199, CMN 432, CPS 188, ECN 801, MTH 410, PCS 211IND 400 - Facilities DesignCourse DescriptionPrinciples and practices in layout and material handling for design of industrial and service facilities. Analytical treatment of facilities location, physical layout, material flow and handling. Integration of product, process and functional design of facilities. Fundamental concepts applied through a sequence of design projects.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CEN 199, CMN 432, ECN 801, IND 303, MEC 322, MTH 425IND 405 - Introduction to Data Science and AnalyticsCourse DescriptionOverview of big data, data science and analytics, the role of the data scientist, analytics lifecycle as an end-to-end process. The course focuses on the main phases of the lifecycle, developing core deliverables for stakeholders, teamwork skills, and problem solving skills. The course aims to provide a strong foundation in Analytics, Tools, and Statistics involving in-class lectures, individual assignments, and team projects to solve a problem, and design a solution.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CEN 199, CMN 432, CPS 188, MTH 410IND 508 - Operations Research ICourse DescriptionThis course will introduce students to the basic principles of Operations Research with special emphasis on the paradigms associated with linear programming and simplex method. These include generic modelling; mathematical modelling; the "max", "min", and "mixed case" simplex algorithms; sensitivity analysis; duality; dual simplex algorithm; the revised simplex method; and "assignment", "transportation" and "transhipment" models. These subjects will be studied from both theoretical and practical perspectives.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, CPS 188, ECN 801, MTH 410IND 600 - Systems Modeling and SimulationCourse DescriptionSimulation models of systems in terms of procedural behaviours, both discrete and continuous, deterministic and stochastic, with an emphasis on stochastic, dynamic simulation models will be studied. These include formulating and implementing simulation models, verification and validation of models, analysis of input and output data, statistical techniques for comparing alternative systems. Computer simulation languages and simulators will be introduced.Weekly Contact:Lecture 3 hrs. Lab 2 hrs.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:MEC 325 and IND 508IND 604 - Operations Research IICourse DescriptionThis course will build upon the principles learned in IND 508. Topics covered in this course include integer programming, dynamic programming, queuing theory, and stochastic processes with practical applications to operational research problems. Non-linear optimality concepts will also be introduced in this course.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:MEC 325, IND 508, MTH 510IND 605 - Experimental Design and Quality AssuranceCourse DescriptionThe objective of this course is to introduce students to the design of experiments as well as statistical quality control. Topics on experimental design include single-factor experiments, block designs, factorial designs, 2-factor experiments and Taguchi's approach to parameter design. Topics on quality control include product flow chart, cause-effect diagram, Pareto Analysis, statistical process control, acceptance sampling and Taguchi's approach to quality.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, CPS 188, PCS 211, MTH 410IND 708 - Information SystemsCourse DescriptionIntroduction to information systems covering essential new technologies, information systems applications, and their impact on business models and managerial decision making. Students will learn how new technologies and innovations enable firms to create new products and services, develop new business models, and transform the day-to-day conduct of the business. The Information Systems development life cycle (i.e. requirements analysis, systems design, and implementation) will be reviewed. Students learn concepts through lectures, hands-on assignments, and case studies.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, IND 300, IND 400, IND 405, PSY 209IND 70A/B - Industrial Systems DesignCourse DescriptionThis course, conducted in the graduating year, brings together the knowledge gained in many previous courses. The engineering design process and the impact of design on society and the environment are presented. Working in small teams, students will complete major team projects in which they will be expected to integrate the knowledge and skills acquired on various aspects of industrial engineering. Each student will complete a series of individual design projects as well. Students will be required to submit final reports and conduct oral presentations.Weekly Contact:Lecture 1 hr. Lab 3 hrs.GPA Weight:2.00Billing Units:1/1Count:2.00Prerequisites:CMN 432, EES 512 IND 300, IND 400, IND 405, IND 600, IND 604, IND 605, IND 712, MEC 323, MEC 309, MEC 516, PSY 209IND 710 - Production and Inventory SystemsCourse DescriptionThe first part will deal with features of production/service systems, methods of modelling their operation and their control system. Topics include aggregate planning, forecasting techniques, work-force and operations scheduling and material requirement planning. The second part will cover the models and techniques for managing inventory systems. The deterministic and stochastic inventory models and lot sizing in continuous and periodic review systems will be included. Emphasis will be placed on the modelling aspect as well as the use of analytical approaches in the solution of system problems.Weekly Contact:Lecture 4 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, IND 400, IND 604, IND 300, PSY 209IND 712 - Industrial ErgonomicsCourse DescriptionThe course deals with anatomical and physiological factors of the human operator for the design and use of machines, and work facilities. Work physiology and biomechanical aspects of industrial workload, shift work, fatigue, cumulative trauma are analyzed. Techniques for optimizing human/machine system availability, and organization of workstations are considered. The reduction of factors such as visual problems, noise, and heat and cold stress are studied for workplace environmental design. Postural analysis techniques are introduced for making ergonomic work designs. Projects in industrial ergonomics are carried out by students in groups.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, ECN 801, MEC 325, PCS 211IND 713 - Project ManagementCourse DescriptionThe objective of this course is to examine the fundamentals of project management within a life-cycle approach, i.e., from idea generation to termination/close phase. It treats human, mathematical, engineering and managerial issues surrounding project management to equip students with tools to effectively manage engineering projects. This course will cover topics such as: project screening and selection, evaluation methods of projects, project structures, management and control, project scheduling, resource management, life-cycle costing, research and development projects, computer support for project management, and project termination. (Equivalent to MEC 713)Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, ECN 801, MTH 510IND 719 - Big Data Analytics ToolsCourse DescriptionThis course is an introduction to learning big data tools with a focus on the Hadoop Ecosystem. Students will gain a clear understanding of Hadoop concepts, technology landscapes, and market trends. They will learn how to construct queries of moderate to high complexity using Pig/Hive to work with big data. Students will get hands-on experience working with interesting real-world datasets as geo-tagged social media posts, check-in datasets, and business tracking platform review data. Various use cases will be utilized such that students can apply what they learned in their daily work. See teaching department for consent criteria.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00Consent:Departmental consent requiredIND 810 - Flexible Manufacturing SystemsCourse DescriptionThis course provides students with an overview of the planning, design, implementation, and control of flexible manufacturing systems. It discusses the concept of flexible manufacturing and types of manufacturing systems such as cellular manufacturing and the application of various artificial intelligence techniques to the design of cellular manufacturing systems. It also includes an overview of the basic components of flexible manufacturing systems: selection of automated material handling systems, part type selection and tool allocation models, workpieces and tools routing, capacity planning, and scheduling for flexible manufacturing systems. (Equivalent to MEC 813)Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, ECN 801, MEC 322, MTH 510IND 816 - Service Operations ManagementCourse DescriptionThe objective of this course is to develop an understanding of the elements of service organizations and relations among the operations, human resources, information system and marketing functions in a service industry. The course explores the challenges faced by managers in various types of service organizations with a focus on operational issues in such organizations. Topics include the nature of services, the role of services in economy, designing service organizations, service quality, E-service, managing service operations, quality and productivity improvement in service organizations, and growth and globalization of services.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, IND 300, IND 400, IND 405, IND 600, IND 604, IND 605, PSY 209IND 820 - Big Data Analytics ProjectCourse DescriptionThis course is intended to engage students in hands-on applications of big data analytics methods and techniques in a project setting. Working with a faculty supervisor, students will apply big data analytics in respect to their real-world area of interest by using open source data.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:IND 405 or MTH 642IND 830 - Python Programming Data ScienceCourse DescriptionThis course is an introduction to Python, analysing and exploring data and doing machine learning with Python. Topics cover basics in Python programming language as well as visualization and exploratory data analysis in Python. Python libraries such as NumPy, Pandas, Matplotlib and Seaborn which are essential for data science will be introduced.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00IND 832 - Reliability and Decision AnalysisCourse DescriptionThe purpose of this course is to present analytical approaches to reliability engineering, decision analysis and risk assessment. In the first part of the course, students will be introduced to reliability functions, reliability distributions, analysis of failure data, reliability of systems, design for reliability, maintenance, reliability testing. The focus of the second part of the course is placed on the methodology to model, construct, solve and interpret various decision problems. Decision tree, value of information, risk assessment, utility theory, and multiple objective decision-making will be presented.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:MEC 325, MTH 410, MTH 510IND 833 - Financial EngineeringCourse DescriptionThis course explores concepts and methods of financial engineering and its applications with special emphasis on fixed income mathematics, introduction to derivatives, valuation of forward contracts and future contracts, hedging strategies using futures, properties of stock options, no-arbitrage pricing, continuous models (the Black-Scholes theory), and discrete models (lattice approach, Monte Carlo simulation, and finite difference method).Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:ECN 801, IND 604, MTH 410, MTH 425, MTH 510IND 840 - Practical Approaches in Machine LearningCourse DescriptionThis course covers the major ideas and principles underlying current practices of data mining. It starts with what machine learning is, where it can be used and different kinds of knowledge representations that are involved. Then, the course spans advanced techniques of data mining, at the lowest and most detailed levels. Finally, the course is wrapped up by covering techniques of “ensemble learning,” which combine the output from different learning techniques. On each topic, examples in Python are provided as lab materials.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:MTH 642IND 850 - Practical Deep LearningCourse DescriptionThis course covers an introduction to deep learning, building foundations of neural networks and doing practical deep learning with AI Platform. Topics cover supervised and unsupervised machine learning as well as basics in neural networks, convolutional and recurrent neural networks. It focuses on building deep learning applications with AI Platform.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:IND 840IND 860 - Advanced Data Analytics ProjectCourse DescriptionThis course will be based on a hands-on application of data mining and deep learning techniques in a project setting. The advanced capstone course is intended to bring advanced data analytics methods, techniques and approaches and apply them to a real-world area of interest. 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This course is designed to give non-data analysts an overview of big data, the status of practice in analytics, the role of the data scientists, big data analytics in industry verticals, and data analytics lifecycle as an end-to-end process. This course aims at providing the ability to make effective decisions in a data-driven manner.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00IND 110 - Data Organization for Data AnalystsCourse DescriptionThis course provides a foundation in data management for data analysts. Topics include database architectures, formation of queries, queries themselves, data warehousing, relational database systems, NoSQL, and responsibilities of data management professionals.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00IND 119 - Introduction to Big DataCourse DescriptionThis course is designed to give students an overview of big data, state of the practice in analytics, the role of the data scientist, big data analytics in industry verticals, and analytics life-cycle as an end-to-end process. It focuses on key roles for a successful analytic project, main phases of the life-cycle, developing core deliverables for stakeholders, team work skills, and problem solving skills.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00IND 123 - Data Analytics: Basic MethodsCourse DescriptionThis course is an introduction to R, analyzing and exploring data with R, and using R with a database. It focuses on statistics for model building and evaluation. Topics cover experimental research, correlation analysis, regression, confidence intervals, and group comparisons, and parametric and non-parametric models.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00IND 300 - Introduction to ManagementCourse DescriptionThe study of theories and practices of management will be introduced with an understanding of the environment in which they operate. The task of the worker, industrial organizations and their culture, the formation and operation of a union and the Canadian industrial relations will be discussed; the dominant North American management theories and applications will be examined, and the Japanese industrial organization and the new role of management will be studied.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:MTH 141, MTH 240, CPS 188, CMN 432, CEN 199IND 303 - Work Measurement, Analysis and DesignCourse DescriptionGeneral IE functions are introduced. Operation process chart, flow process chart, flow diagram, worker and machine process chart, and gang process chart are considered as recording and analysis tools. Principles of motion economy and motion study are discussed for manual work design. Work measurement tools covered include predetermined time systems: MTM-1, MTM-2, MTM-3, Maynard Operation Sequence Technique (MOST) and introduction to computer-based MOST; time-study systems: fundamentals of continuous and snap-back techniques for stop-watch, datamyte and palm-pilots; and analytical systems: work sampling and standard data development. Student teams undertake an open-ended work-system design project that requires the integration and analysis of the topics covered.Weekly Contact:Lecture 3 hrs. Lab 2 hrs.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CEN 199, CMN 432, CPS 188, ECN 801, MTH 410, PCS 211IND 400 - Facilities DesignCourse DescriptionPrinciples and practices in layout and material handling for design of industrial and service facilities. Analytical treatment of facilities location, physical layout, material flow and handling. Integration of product, process and functional design of facilities. Fundamental concepts applied through a sequence of design projects.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CEN 199, CMN 432, ECN 801, IND 303, MEC 322, MTH 425IND 405 - Introduction to Data Science and AnalyticsCourse DescriptionOverview of big data, data science and analytics, the role of the data scientist, analytics lifecycle as an end-to-end process. The course focuses on the main phases of the lifecycle, developing core deliverables for stakeholders, teamwork skills, and problem solving skills. The course aims to provide a strong foundation in Analytics, Tools, and Statistics involving in-class lectures, individual assignments, and team projects to solve a problem, and design a solution.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CEN 199, CMN 432, CPS 188, MTH 410IND 508 - Operations Research ICourse DescriptionThis course will introduce students to the basic principles of Operations Research with special emphasis on the paradigms associated with linear programming and simplex method. These include generic modelling; mathematical modelling; the "max", "min", and "mixed case" simplex algorithms; sensitivity analysis; duality; dual simplex algorithm; the revised simplex method; and "assignment", "transportation" and "transhipment" models. These subjects will be studied from both theoretical and practical perspectives.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, CPS 188, ECN 801, MTH 410IND 600 - Systems Modeling and SimulationCourse DescriptionSimulation models of systems in terms of procedural behaviours, both discrete and continuous, deterministic and stochastic, with an emphasis on stochastic, dynamic simulation models will be studied. These include formulating and implementing simulation models, verification and validation of models, analysis of input and output data, statistical techniques for comparing alternative systems. Computer simulation languages and simulators will be introduced.Weekly Contact:Lecture 3 hrs. Lab 2 hrs.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:MEC 325 and IND 508IND 604 - Operations Research IICourse DescriptionThis course will build upon the principles learned in IND 508. Topics covered in this course include integer programming, dynamic programming, queuing theory, and stochastic processes with practical applications to operational research problems. Non-linear optimality concepts will also be introduced in this course.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:MEC 325, IND 508, MTH 510IND 605 - Experimental Design and Quality AssuranceCourse DescriptionThe objective of this course is to introduce students to the design of experiments as well as statistical quality control. Topics on experimental design include single-factor experiments, block designs, factorial designs, 2-factor experiments and Taguchi's approach to parameter design. Topics on quality control include product flow chart, cause-effect diagram, Pareto Analysis, statistical process control, acceptance sampling and Taguchi's approach to quality.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, CPS 188, PCS 211, MTH 410IND 708 - Information SystemsCourse DescriptionIntroduction to information systems covering essential new technologies, information systems applications, and their impact on business models and managerial decision making. Students will learn how new technologies and innovations enable firms to create new products and services, develop new business models, and transform the day-to-day conduct of the business. The Information Systems development life cycle (i.e. requirements analysis, systems design, and implementation) will be reviewed. Students learn concepts through lectures, hands-on assignments, and case studies.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, IND 300, IND 400, IND 405, PSY 209IND 70A/B - Industrial Systems DesignCourse DescriptionThis course, conducted in the graduating year, brings together the knowledge gained in many previous courses. The engineering design process and the impact of design on society and the environment are presented. Working in small teams, students will complete major team projects in which they will be expected to integrate the knowledge and skills acquired on various aspects of industrial engineering. Each student will complete a series of individual design projects as well. Students will be required to submit final reports and conduct oral presentations.Weekly Contact:Lecture 1 hr. Lab 3 hrs.GPA Weight:2.00Billing Units:1/1Count:2.00Prerequisites:CMN 432, EES 512 IND 300, IND 400, IND 405, IND 600, IND 604, IND 605, IND 712, MEC 323, MEC 309, MEC 516, PSY 209IND 710 - Production and Inventory SystemsCourse DescriptionThe first part will deal with features of production/service systems, methods of modelling their operation and their control system. Topics include aggregate planning, forecasting techniques, work-force and operations scheduling and material requirement planning. The second part will cover the models and techniques for managing inventory systems. The deterministic and stochastic inventory models and lot sizing in continuous and periodic review systems will be included. Emphasis will be placed on the modelling aspect as well as the use of analytical approaches in the solution of system problems.Weekly Contact:Lecture 4 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, IND 400, IND 604, IND 300, PSY 209IND 712 - Industrial ErgonomicsCourse DescriptionThe course deals with anatomical and physiological factors of the human operator for the design and use of machines, and work facilities. Work physiology and biomechanical aspects of industrial workload, shift work, fatigue, cumulative trauma are analyzed. Techniques for optimizing human/machine system availability, and organization of workstations are considered. The reduction of factors such as visual problems, noise, and heat and cold stress are studied for workplace environmental design. Postural analysis techniques are introduced for making ergonomic work designs. Projects in industrial ergonomics are carried out by students in groups.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, ECN 801, MEC 325, PCS 211IND 713 - Project ManagementCourse DescriptionThe objective of this course is to examine the fundamentals of project management within a life-cycle approach, i.e., from idea generation to termination/close phase. It treats human, mathematical, engineering and managerial issues surrounding project management to equip students with tools to effectively manage engineering projects. This course will cover topics such as: project screening and selection, evaluation methods of projects, project structures, management and control, project scheduling, resource management, life-cycle costing, research and development projects, computer support for project management, and project termination. (Equivalent to MEC 713)Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, ECN 801, MTH 510IND 719 - Big Data Analytics ToolsCourse DescriptionThis course is an introduction to learning big data tools with a focus on the Hadoop Ecosystem. Students will gain a clear understanding of Hadoop concepts, technology landscapes, and market trends. They will learn how to construct queries of moderate to high complexity using Pig/Hive to work with big data. Students will get hands-on experience working with interesting real-world datasets as geo-tagged social media posts, check-in datasets, and business tracking platform review data. Various use cases will be utilized such that students can apply what they learned in their daily work. See teaching department for consent criteria.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00Consent:Departmental consent requiredIND 810 - Flexible Manufacturing SystemsCourse DescriptionThis course provides students with an overview of the planning, design, implementation, and control of flexible manufacturing systems. It discusses the concept of flexible manufacturing and types of manufacturing systems such as cellular manufacturing and the application of various artificial intelligence techniques to the design of cellular manufacturing systems. It also includes an overview of the basic components of flexible manufacturing systems: selection of automated material handling systems, part type selection and tool allocation models, workpieces and tools routing, capacity planning, and scheduling for flexible manufacturing systems. (Equivalent to MEC 813)Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, ECN 801, MEC 322, MTH 510IND 816 - Service Operations ManagementCourse DescriptionThe objective of this course is to develop an understanding of the elements of service organizations and relations among the operations, human resources, information system and marketing functions in a service industry. The course explores the challenges faced by managers in various types of service organizations with a focus on operational issues in such organizations. Topics include the nature of services, the role of services in economy, designing service organizations, service quality, E-service, managing service operations, quality and productivity improvement in service organizations, and growth and globalization of services.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:CMN 432, IND 300, IND 400, IND 405, IND 600, IND 604, IND 605, PSY 209IND 820 - Big Data Analytics ProjectCourse DescriptionThis course is intended to engage students in hands-on applications of big data analytics methods and techniques in a project setting. Working with a faculty supervisor, students will apply big data analytics in respect to their real-world area of interest by using open source data.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:IND 405 or MTH 642IND 830 - Python Programming Data ScienceCourse DescriptionThis course is an introduction to Python, analysing and exploring data and doing machine learning with Python. Topics cover basics in Python programming language as well as visualization and exploratory data analysis in Python. Python libraries such as NumPy, Pandas, Matplotlib and Seaborn which are essential for data science will be introduced.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00IND 832 - Reliability and Decision AnalysisCourse DescriptionThe purpose of this course is to present analytical approaches to reliability engineering, decision analysis and risk assessment. In the first part of the course, students will be introduced to reliability functions, reliability distributions, analysis of failure data, reliability of systems, design for reliability, maintenance, reliability testing. The focus of the second part of the course is placed on the methodology to model, construct, solve and interpret various decision problems. Decision tree, value of information, risk assessment, utility theory, and multiple objective decision-making will be presented.Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:MEC 325, MTH 410, MTH 510IND 833 - Financial EngineeringCourse DescriptionThis course explores concepts and methods of financial engineering and its applications with special emphasis on fixed income mathematics, introduction to derivatives, valuation of forward contracts and future contracts, hedging strategies using futures, properties of stock options, no-arbitrage pricing, continuous models (the Black-Scholes theory), and discrete models (lattice approach, Monte Carlo simulation, and finite difference method).Weekly Contact:Lecture 3 hrs. Lab 1 hr.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:ECN 801, IND 604, MTH 410, MTH 425, MTH 510IND 840 - Practical Approaches in Machine LearningCourse DescriptionThis course covers the major ideas and principles underlying current practices of data mining. It starts with what machine learning is, where it can be used and different kinds of knowledge representations that are involved. Then, the course spans advanced techniques of data mining, at the lowest and most detailed levels. Finally, the course is wrapped up by covering techniques of “ensemble learning,” which combine the output from different learning techniques. On each topic, examples in Python are provided as lab materials.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:MTH 642IND 850 - Practical Deep LearningCourse DescriptionThis course covers an introduction to deep learning, building foundations of neural networks and doing practical deep learning with AI Platform. Topics cover supervised and unsupervised machine learning as well as basics in neural networks, convolutional and recurrent neural networks. It focuses on building deep learning applications with AI Platform.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:IND 840IND 860 - Advanced Data Analytics ProjectCourse DescriptionThis course will be based on a hands-on application of data mining and deep learning techniques in a project setting. The advanced capstone course is intended to bring advanced data analytics methods, techniques and approaches and apply them to a real-world area of interest. Working with a faculty supervisor, participants will apply what they have learned in the two courses and implement their analysis through Python and AI Platform.Weekly Contact:Lecture 3 hrs.GPA Weight:1.00Billing Units:1Count:1.00Prerequisites:IND 850 jQuery(document).ready(function() { jQuery(".173798a6-c413-43ad-b5ab-1c0be31f1d24 .stackContentMain").initStackComponent({ type:"course", uuid:"173798a6-c413-43ad-b5ab-1c0be31f1d24", url:"/content/ryerson/calendar/2025-2026/courses/industrial-engineering/jcr:content/content/rescalendarcoursestack", limit:"2000", resultsPerPage:0, offset:"0", paginationType:"numbered", displayAs:"list", displayItems:{ displayTitle:true, displayDescription:true, displayLength:true, displayWeight:true, displayUnits:true, displayCourseCount:true, displayAttribute:true, displayConsent:true, displayPrerequisites:true, displayAntirequisites:true, displayCorequisites:true, displayCustomrequisites:true, openInNewWindow:false, splitCodeTitle:false, enableCourseLinking:false } }) });
