Certificate in Digital Business

This course requires students to apply principles of Digital Innovation (DI) holistically to a concrete problem. In the context of a multidisciplinary team, students are expected to apply Di processes, develop negotiation and collaborative skills.

This class will provide Master of Digital Innovation thesis students with an understanding of the principles of empirical science as they relate to computer science related research. The goal is for the student to determine the research methods most appropriate for their research area and to be able to design simple to moderately complicated research studies. The course covers both quantitative and qualitative research issues and will provide a practical introduction to the statistics through hand-on tutorials. In addition, this course will provide the basis for critical reading of research findings in the literature and students will gain experience with scientific writing. This course will teach students how to assess the validity of other researchers’ articles, and at the same time, enable students to validate their own research.

Digital transformation implies reinventing business processes to take advantage of automation, efficiency gains due to new technologies, or to answer new legal or competitive requirements. In this course, we will learn how to get from an existing to an improved business process, including, when relevant, automating low-added value steps. This course prepares students to complete their first digitalization process improvement projects and manage changes at various levels in the firm. This includes monitoring the existing process, evaluating its conformance, identifying improvements, and choosing an appropriate change management approach.

Data warehousing and data mining are two emerging technologies which will have a profound effect on the role information plays in organizations. A data warehouse is a repository of data taken from multiple sources that supports querying and analysis tools. Data mining, the process of knowledge discovery from data in a data warehouse, is typically used for strategic planning and has great economic potential for organizations. This course covers key issues in data warehouse architecture, design of data warehouse schemas, design of metadata repositories, the creation, development and maintenance of warehouses, as well as tools and techniques for querying, analyzing and mining the warehouse data. Data mining techniques such as statistical and non-statistical supervised and unsupervised learning methods will be applied to problems drawn from the medical and business world.

The course will cover the principles of management for Information Technology Projects. The history of project management is rooted in Civil Engineering and manufacturing. Information technology projects have several notable differences. Students will learn those differences as well as generic principles of project management. Through case studies and field investigations of actual IT projects, students will gain a real-world understanding.

Public administration rhetoric often indicates that governments are re-inventing themselves by using information technology. What is happening around the world with E-government? Using Canada as reference, this course reviews the development of management of information as it affects performance management, democracy, the nation state, accountability, network growth, productivity and access. Each student will be required to analyze an international country, state or province and its progression to e-government and relate that progress to activities in governments around the world.

This course deals with the law that governs the dissemination of information and the regulation of information providers. In this course, “media” is defined broadly to include the internet. Topics that will be addressed include: defamation; liability of service providers; privacy issues; publication bans; media regulation; copyright issues; conducting business via the internet ("e-commerce") and media ownership. The impact of the internet on the legal regulation relating to each of these topics will be explored throughout the course.

Machine Learning is the area of Artificial Intelligence concerned with the problem of building computer programs that automatically improve with experience. The intent of this course is to present a broad introduction to the principles and paradigms underlying machine learning, including discussions of each of the major approaches currently being investigated. Main topics covered in the course include a review of information theory, unsupervised learning or clustering (the K-means family, co-clustering, mixture models and the EM algorithm), supervised learning or classification (support vector machines, decision trees, rule learning, Bayesian learners, maximum entropy, ensemble methods), feature selection and feature transformations. The focus of applications that will be discussed will be text classification and clustering.

Human-Computer interaction (HCI) deals with facilitating human-computer communication. Students will learn the foundations of HCI, including the process for user-centered development, the models that inform HCI design, the social issues influencing HCI design and use, and the evaluation of interfaces and systems with users.

This course will introduce the concepts of Visual Analytics (VA). VA is a multi-disciplinary domain that combines data visualization with machine learning and other automated techniques to help people make sense of data. Students will be introduced to the design of visual representations supporting tasks to go from findings to insights based on data. Topics include basic concepts of information visualization and machine learning; visual analytics of evolving phenomena; analysis of spatial and temporal data sets; visual social media analytics; and the visual analytics of text and multimedia collections. Students will prototype visual analytics applications using existing toolkits, coupling machine learning and visualization methods. Students will gain competence in performing data analysis and visualization tasks in different application domains.

This course provides real world, hands-on learning on what it's like to actually start a scalable company or enterprise. This course is not about how to write a business plan. It's not an exercise on how smart you are in a classroom, or how well you use the research library to size markets. And the end result is not a PowerPoint slide deck for a VC presentation. This is a practical course - essentially a lab, not a theory or 'book' course. You will be getting your hands dirty talking to customers, partners, and competitors, as you encounter the chaos and uncertainty of how a Startup actually works. You'll work in teams learning how to turn a great idea into a great company. You'll learn how to use a business model to brainstorm each part of a company and customer development to get out of the classroom to see whether anyone other than you would want/use your product. Each day will be a new adventure outside the classroom as you test each part of your business model, then share you hard earned knowledge with the rest of the class.

New Venture Creation is about entrepreneurship: the process of creating new businesses. It employs cases, experiential exercises, and a major project to expose students to the issues, problems, and challenges of creating viable new businesses. The project provides students with the opportunity, within the framework of a formal course, to explore and develop business ideas they have been considering or wish to investigate. The final output of the project is a feasibility study, business plan, and financing proposal for a new venture.

Enterprise Systems are comprised of a unified database with shared analysis and reporting tools allowing for real time business intelligence across global operations. Emphasis in this course is equally on learning business processes and integration between different functional areas as it is about the technology that facilitates this. This course will be taught in the teaching labs with a combination of individual and group simulations interspersed with short lectures. An active learning approach in this course will include hands-on learning using SAP ERP, as well as ERPSim, a game-based SAP ERP simulation. Here you will learn to manage companies from end-to-end using the actual SAP ERP in a real-time simulated competitive environment and will learn the processes, gain technical skills with SAP and playfully learn how Enterprise Systems facilitate Business Intelligence which can be used to lead a company in a competitive environment.

This course provides an introduction to Business Analytics and Data Visualization. It covers the processes, methodologies and practices used to transform the large amounts of business and public data into useful information to support business decision-making. Students will learn how to extract and manipulate data from these systems. They will also acquire basic knowledge of data mining and statistical analysis, with a focus on data visualization. The students will also learn to build and use management dashboards and balanced scorecards using a variety of data design and visualization tools. The course will be made up of a combination of conceptual and applied topics with classes being held in a computer lab. Technologies to be used will be focused on end-user analytics and data visualization and will include state of the art tools for self-serve business analytics.

Natural Language Processing (NLP) is an area of Artificial Intelligence concerned with the problem of automatically analyzing and generating a natural language, such as English, French, or other, in written or spoken form. It is a relatively old area of computer science, but it is still a very active research area. This course introduces fundamental concepts and principals used in NLP with emphasis on statistical approaches to NLP and unification-based grammars. In the application part of the course, we discuss the problems of question answering, machine translation, text classification, information extraction, grammar induction, and dictionary generation and other.