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Lecture Hours: 4.0 | Seminar: 0.0 | Lab: 0.0
This course is designed to provide fundamental skills necessary for success in the Canadian business environment. Successful students will develop the skills and competencies required to present themselves and their work in a professional manner according to business ethics and societal norms. The course will allow students to develop skills and strategies to manage office politics, social situations, and professional communication.Registration in this course is restricted to students admitted to the Post-Degree Diplomas in Accounting, Business Administration, Marketing Management, and Data Analytics.
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Lecture Hours: 4.0 | Seminar: 0.0 | Lab: 0.0
This course introduces project management concepts, skills, and tools that allow managers to coordinate and lead projects towards successful completion. A variety of techniques are used to manage the budget, schedule, and quality of projects. This applied course also introduces software tools specifically designed for the task. Effective project management ensures that a project is completed on time, within budget, and with high quality.Students will receive credit for only one of BUSM 4100 or 4830.Registration in this course is restricted to students admitted to the Post-Degree Diploma in Business Management or Post-Degree Diploma in Data Analytics.Prerequisite(s): A minimum "C" grade in BUSM 4805, and BUSM 4810 or DANA 4810.
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Lecture Hours: 3.0 | Seminar: 1.0 | Lab: 0.0
Formerly CMNS 4810 and WMDD 4860Succeeding in today's industries requires strong professional and interpersonal communication skills. Students are introduced to fundamental principles of communications including audience analysis, purpose identification, and elements of tone and style. Students prepare to be successful professionals by learning how to promote themselves, how to give meaningful feedback on others' work, and how to work effectively on a team. Students practice a variety of strategies for interpersonal, oral, and written communication, including conveying confident body language, demonstrating dynamic presentation skills, and using persuasive writing techniques.Students will receive credit for only one of CMNS 4800, 4810, or WMDD 4860.Registration in this course is restricted to students admitted to the Post-Degree Diploma or Certificate in Data Analytics, or Post-Degree Diploma in Web and Mobile App Design and Development.
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Lecture Hours: 2.0 | Seminar: 0.0 | Lab: 2.0
Computers provide the power and platform for any significant work in data analytics. Students learn about an organization's information systems and business processes as well as its multiple data sources. Students issue database commands to examine the data's structure and organization and retrieve appropriate sized datasets. Students also learn programming using the Python programming language.Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics and Post-Degree Certificate in Data Analytics.
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Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 3.0
Data analysts need to integrate heterogeneous data from a variety of sources and in a variety of formats to provide workable homogeneous data sets for further analysis or processing. Students write programs using a scripting language to extract, transform, merge, and clean data to generate datasets that can be loaded into an appropriate analysis or visualization tool.Students will receive credit for only one of CPSC 3260 or 4810.Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics and Post-Degree Certificate in Data Analytics.Prerequisite(s): A minimum of a "C" grade in CPSC 4800.
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Lecture Hours: 2.0 | Seminar: 0.0 | Lab: 2.0
Data analysts uncover trends and patterns in data by creating and using effective visual formats. Students learn techniques for effectively communicating both qualitative and quantitative data in tables, charts, infographics, and interactive elements. They learn the role and importance of colour theory, visual perception, and cognition, as well as design principles in the development of appropriate data visualizations.Students will receive credit for only one of CPSC 4260 or 4820.Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics and Post-Degree Certificate in Data Analytics.Prerequisite(s): A minimum "C" grade in CPSC 4800 and DANA 4800.
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Lecture Hours: 2.0 | Seminar: 0.0 | Lab: 2.0
Once data has been gathered, it must be cleaned, processed, and analyzed in order to find the most appropriate model to describe the underlying data. Using text classification and anomaly detection, students identify and implement the phases of a data mining process to mine and extract frequent pattern and outliers within data.Students will receive credit for only one of CPSC 4160 or 4830.Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics.Prerequisite(s): A minimum "C" grade in one of CPSC 4810 or 4820; and both DANA 4810, and 4820.
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Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 1.0
Statistical inference is the process of drawing conclusions from data. Students gain a foundation in probability, descriptive statistics, sampling methods, normal distributions, Poisson distributions, and sampling distributions, as well as one-sample and two-sample statistical inference procedures on both proportions and means (including z and t).Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics and Post-Degree Certificate in Data Analytics.
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Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 1.0
Predictive analytics is a process of using and applying statistical analysis techniques for estimation and forecasting. Students learn standard methodology for analyzing quantitative data, including analysis of variance, design of experiments, simple regression, multiple regression, data transformation, and generalized linear models.Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics and Post-Degree Certificate in Data Analytics.Prerequisite(s): A passing mark from Data Analytics Math Assessment Test or an "S" grade in MATH 4801; and a minimum "C" grade in DANA 4800.
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Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 1.0
Predictive Analytics is a process of using and applying statistical analysis techniques for estimation and forecasting. Students learn standard methodology for analyzing categorical data including chi-square tests for two-way and multi-way contingency tables, logistic regression, and Poisson regression.Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics and Post-Degree Certificate in Data Analytics.Prerequisite(s): A passing mark from Data Analytics Math Assessment Test or an "S" grade in MATH 4801; and a minimum "C" grade in DANA 4800.
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Lecture Hours: 4.0 | Seminar: 0.0 | Lab: 0.0
A core requirement in data analytics is the classification of a large group of records (items or objects) into different subgroups based on statistical criteria. The classification can be made easier if the number of dimensions of the data used is reduced. Students learn a number of techniques in reducing the number of dimensions in a data set without losing its latent structure. They also learn how to perform statistical classification into pre-defined groups. Topics include principal component analysis, factor analysis, multiple correspondence analysis, multivariate discriminant analysis, as well as stepwise techniques in regressions.Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics and Post-Degree Certificate in Data Analytics.Prerequisites: A minimum "C" grade in DANA 4810 and 4820.
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Lecture Hours: 4.0 | Seminar: 0.0 | Lab: 0.0
Following dimension reduction and standard techniques of classification, situations arise where more advanced techniques are called for. Students learn the various multivariate techniques for classifying objects or cases into several groups. Density-based and centroid-based clustering, hierarchical techniques, as well as other clustering techniques such as fuzzy clustering will be discussed in detail.Registration in this course is restricted to students admitted to the Post-DegreeDiploma in Data Analytics and Post-Degree Certificate in Data Analytics.Prerequisite(s): A minimum "C" grade in DANA 4810 and 4820.
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Lecture Hours: 4.0 | Seminar: 4.0 | Lab: 0.0
Students are guided to apply their skills in a capstone project. Depending on the nature of the project, students demonstrate their ability to handle data by taking it through the life cycle of the data analytics process from acquisition through analysis to presentation of results.Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics. Prerequisite(s): A minimum "C" grade in CPSC 4830, DANA 4830, and 4840.
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Lecture Hours: 4.0 | Seminar: 0.0 | Lab: 0.0
Finding meaningful work takes time, research, and planning. Students explore diverse perspectives and strategies that will lead to personal success in career planning. Through direct contact with industry professionals and career educators, students investigate practical strategies and tools to successfully job search and participate in the Canadian workforce. They develop tools and techniques to help them pursue employment in their chosen career. Relevant subjects include job search mindset, personal branding, targeted job applications, social media, networking, interview skills, workplace expectations and typical employment standards, and policies of Canadian businesses.Registration in this course is restricted to students admitted to Post-Degree Diploma programs with a work term option.
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Lecture Hours: 0.0 | Seminar: 26.0 | Lab: 0.0
During a term of full-time employment (minimum 300 hours over a 16 week term), students will have the opportunity for the practical application of theoretical knowledge gained in academic studies to enhance their skills and provide professional and personal development. Evaluation will consist of employer feedback, workplace reflection, and a final work term report. Students are responsible for finding suitable work placements.Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics.Prerequisite(s): Completion of a minimum of 40 credits including a minimum "C" grade in EXPE 4800; or a minimum "C-" grade in EXPE 4801 and 4802, and a minimum "C" grade in EXPE 4803.
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Lecture Hours: 0.0 | Seminar: 2.0 | Lab: 0.0
Intended for students who require additional practice in pre-calculus algebra and linear algebra to succeed in data analytics programs. Topics include linear equations, systems of equations, matrix operations, quadratic forms, power functions, square root functions, exponential functions, logarithmic function, and reciprocal functions. Students with a passing score on the Data Analytics Mathematics Assessment (DAMA) Test are not required to complete this course. Graded S/U.Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics and Post-Degree Certificate in Data Analytics.Prerequisite(s): A minimum score of 1 on the Data Analytics Mathematics Assessment (DAMA).
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1 EXPE 4800 must be taken in term three of the program.
2 EXPE 4844 must be taken in term five.
3 The Data Analytics Mathematics Assessment (DAMA) will be administered upon students’ arrival or during the first week of the term. Students who meet minimum standards will be exempt from MATH 4801. |
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