New Courses (Effective 201920):

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New Courses (Effective 201930):

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CMNS 4810, 3 credits

Communications for Data Professions

New Course as of Summer Semester 2019

Communication skills are essential to clearly express complex ideas and information to a variety of audiences. Students learn to present and explain technical procedures and analysis findings verbally and in writing, adapting their work to different audiences while maintaining professionalism in format, tone, and style. In addition, students work individually and in groups, and provide each other with constructive feedback.

Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics Program. 

Prerequisite(s): LET 3 (or LPI equivalent); a minimum 80% in one of BC English 12, or BC Engish Literature 12, or BC English First Peoples 12; or a "C" in ENGL 1120; or an "S" in one of ENGL 1107, or 1108, or 1110.


CPSC 2350, 3 credits

Software Practices

New Course as of Summer Semester 2019

Students learn how to apply software development best-practices to create the right product (validated), done right (verified), and managed right (through a responsive and responsible process). They will learn the "what", "how", and "why" of agile practices, and the consequences for not following them, while working through the Software Development Cycle (SDLC) on multi-developer projects.

Priority registration for students admitted to the Citation in Full Stack Web Development Program.

Students will receive credit for only one of CPSC 2301, CPSC 2350, or CSIS 2302

Prerequisite(s): A minimum grade of "B" in both CPSC 1030 and CPSC 1045


CPSC 2600, 3 credits

Full Stack Web Development I

New Course as of Summer Semester 2019

Students explore web application development concepts and practices, including common full-stack architectures, server-side scripting languages and frameworks, and databases. Students learn the basics of server-side scripting and build a basic web application using contemporary languages, libraries, and frameworks.

Priority registration for students admitted to the Citation in Full Stack Web Development Program.

Students will receive credit for only one of CPSC 2261 or CPSC 2600

Prerequisite(s): A minimum grade of "B" in both CPSC 1030 and CPSC 1045


CPSC 2650, 3 credits

Full Stack Web Development II

New Course as of Summer Semester 2019

Building on the knowledge and skills learned in Full Stack Web Development I, students explore intermediate web application development practices, including security and authentication, 3rd party APIs, web accessibility, advanced UI design techniques, contemporary front-end architectures and frameworks, and hosting and deployment. Students build and deploy a moderately-complex web application.

Priority registration for students admitted to the Citation in Full Stack Web Development Program.

Prerequisite(s): A minimum grade of "C" in CPSC 2600 and CPSC 2350


CPSC 4830, 3 credits

Data Mining for Data Analytics

New Course as of Summer Semester 2019

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.

Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics Program. 

Students will receive credit for only one of CPSC 4160 or CPSC 4830

Prerequisite(s): A minimum grade of "C" in CPSC 4810, CPSC 4820, and DANA 4830


DANA 4830, 3 credits

Dimension Reduction & Classification I

New Course as of Summer Semester 2019

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 Program. 

Prerequisite(s): A minimum grade of a "C" in DANA 4810


DANA 4840, 3 credits

Classification II

New Course as of Summer Semester 2019

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 centorid-based clustering, hierachical 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-Degree Diploma in Data Analytics Program. 

Prerequisite(s): A minimum grade of a "C" in both DANA 4820 and DANA 4830


DANA 4850, 6 credits

Capstone Project

New Course as of Summer Semester 2019

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 data analytics process from acquisition through analysis to presentaiton of results.

Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics Program. 

Prerequisite(s): A minimum grade of a "C" in all DANA 4820, DANA 4830, CPSC 4810, and CPSC 4820

Corequisite(s): DANA 4840 and CPSC 4830


STAT 3225, 4 credits

Statistical Methods for Biological and Health Sciences

New Course as of Fall Semester 2019

Students learn how to use statistical methods for analyzing data from the biological and health sciences. The programming language R and R commander is used for statistical computing including data manipulation, data analysis, and graphical display of data. Topics covered in this course include: observational and experimental studies, parametric and nonparametric statistical methods, analysis of contingency tables, analysis of variance, multiple linear regression, and logistic regression. Students are required to complete a term data analysis project using statistical methods and software presented in this course.

Prerequisite(s): STAT 1181 and STAT 2281