Program Curriculum

Program Curriculum

 

The Post-Degree Certificate in Data Analytics is designed for working professionals who have prior data analysis experience in their related fields. This part-time program allows them to broaden their skill sets or advance further in their careers. The program focusses on the entire life-cycle of the data analytics process from acquisition through analysis to presentation of results. By handling real-life data throughout in the program, students learn to clean and analyze data, and to effectively present their findings. Students also gain skills in industry standard software applications.

Total Credits: 25 or 26

Courses Credits
All of
CPSC 4800 Computing for Data Analytics
3

Lecture Hours: 2.0 | Seminar: 0.0 | Lab: 2.0

New Course

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.

More Information »

CPSC 4810 Transformations for Data Analytics
4

Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 3.0

New Course

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.

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 4800.

More Information »

CPSC 4820 Visualization for Data Analytics
3

Lecture Hours: 2.0 | Seminar: 0.0 | Lab: 2.0

New Course

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.

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

Prerequisite(s): A minimum of "C" grade in both DANA 4800 and CPSC 4800.

More Information »

DANA 4800 Data Analysis and Statistical Inference
3

Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 1.0

New Course

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.

More Information »

DANA 4810 Predictive Analytics - Quantitative Data
3

Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 1.0

New Course

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.

Prerequisite(s): A passing mark from Data Analytics Math Assessment Test or a passing grade in MATH 4801 and a minimum "C" grade in DANA 4800

More Information »

DANA 4820 Predictive Analytics - Qualitative Data
3

Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 1.0

New Course

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.

Prerequisite(s): A passing mark from Data Analytics Math Assessment Test or a passing grade in MATH 4801 and a minimum "C" grade in DANA 4800.

More Information »

DANA 4830 Dimension Reduction & Classification I
3

Lecture Hours: 4.0 | Seminar: 0.0 | Lab: 0.0

New Course

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.

Prerequisites: A minimum "C" grade in DANA 4810.

More Information »

DANA 4840 Classification II
3

Lecture Hours: 4.0 | Seminar: 0.0 | Lab: 0.0

New Course

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-Degree Diploma in Data Analytics.

Prerequisite(s): A minimum "C" grade in DANA 4820 and 4830.

More Information »

MATH 4801 Mathematics for Data Analytics 1
1

Lecture Hours: 0.0 | Seminar: 2.0 | Lab: 0.0

New Course

Students require a solid foundation in pre-calculus algebra and linear algebra to succeed in the PDD in Data Analytics. Topics include linear equations, systems of equations, matrix operations, quadratic forms, power functions, square root functions, exponential functions, logarithmic function, and reciprocal functions.

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

More Information »

Notes:
1 A mathematics diagnostic test will be administered upon students’ arrival or during the first week of the term. Students who fail this test will be required to complete MATH 4801.
1

The Post-Degree Diploma in Data Analytics focuses on the entire lifecycle of the data analytics process from acquisition through analysis to presentation of results. Through collaboration with industry partners, students gain experience handling real-life data from such fields as telecommunications, finance, and health care. In lab-oriented courses, they learn to clean and analyze data and to effectively present their findings from the analysis of structured and unstructured data. Students will gain skills in industry standard software and applications.

Total Credits: 46 or 47

Term 1

Courses Credits
All of
BUSM 4805 Professional Business Practice
3

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, and Marketing Management.

More Information »

CPSC 4800 Computing for Data Analytics
3

Lecture Hours: 2.0 | Seminar: 0.0 | Lab: 2.0

New Course

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.

More Information »

DANA 4800 Data Analysis and Statistical Inference
3

Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 1.0

New Course

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.

More Information »

EXPE 4801 Career Goals, Resumes, and Cover Letters
1

Lecture Hours: 1.5 | Seminar: 0.0 | Lab: 0.0

Formerly COOP 4801

This course is meant to focus and prepare students to effectively create targeted job applications to utilize when applying for career positions. In EXPE 4801 students will learn what employers look for when screening job applications, how to read and interpret job postings, and how to create relevant, job specific targeted resumes and cover letters that will impress employers and increase their odds of getting interviews. This course is the first in a series of three that is ultimately aimed at preparing students with job ready employability skills that they can utilize to advance their careers.

Students will receive credit for only one of COOP 4801 and EXPE 4801.

Registration in this course is restricted to students admitted to the Post-Degree Diplomas in Accounting, Applied Planning, Business Administration, Marketing Management, and Web and Mobile App Design and Development.

More Information »

MATH 4801 Mathematics for Data Analytics 1
1

Lecture Hours: 0.0 | Seminar: 2.0 | Lab: 0.0

New Course

Students require a solid foundation in pre-calculus algebra and linear algebra to succeed in the PDD in Data Analytics. Topics include linear equations, systems of equations, matrix operations, quadratic forms, power functions, square root functions, exponential functions, logarithmic function, and reciprocal functions.

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

More Information »

Notes:
1 A mathematics diagnostic test will be administered upon students’ arrival or during the first week of the term. Students who fail this test will be required to complete MATH 4801.
 
11 Credits

Term 2

Courses Credits
All of
CMNS 4810 Communications for Data Professions
3

Lecture Hours: 3.0 | Seminar: 1.0 | Lab: 0.0

New Course

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.

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

More Information »

CPSC 4810 Transformations for Data Analytics
4

Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 3.0

New Course

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.

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 4800.

More Information »

DANA 4810 Predictive Analytics - Quantitative Data
3

Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 1.0

New Course

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.

Prerequisite(s): A passing mark from Data Analytics Math Assessment Test or a passing grade in MATH 4801 and a minimum "C" grade in DANA 4800

More Information »

EXPE 4802 Interviews and Your Professional Image
1

Lecture Hours: 1.5 | Seminar: 0.0 | Lab: 0.0

Formerly COOP 4802

This course is the second in a series of three, and will continue where EXPE 4801 left off, by transitioning students from targeted job applications, to the next phase of the job application process. In this course students will learn of the significant impact that their professional image can have on attracting potential employers and on obtaining career employment. It will also train students how to effectively engage in interviews, and networking from a Canadian business context. This course will be very practical, and students will be able to apply the content and activities from classes directly to their personal job search; increasing their chances of gaining employment.

Students will receive credit for only one of COOP 4802 and EXPE 4802.

Registration in this course is restricted to students admitted to the Post-Degree Diplomas in Accounting, Applied Planning, Business Administration, Marketing Management, and Web and Mobile App Design and Development.

Prerequisite(s): EXPE 4801 with a minimum "C" grade.

More Information »

11 Credits

Term 3

Courses Credits
All of
BUSM 4830 Project Management
3

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 may receive credit for only one of BUSM 4100 and 4830. BUSM 4100 may not be used to satisfy the BUSM 4830 requirement.

Registration in this course is restricted to students admitted to the Post-Degree Diploma in Business Management.

Prerequisite(s): A minimum "C" grade in BUSM 4805 and BUSM 4810.

More Information »

CPSC 4820 Visualization for Data Analytics
3

Lecture Hours: 2.0 | Seminar: 0.0 | Lab: 2.0

New Course

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.

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

Prerequisite(s): A minimum of "C" grade in both DANA 4800 and CPSC 4800.

More Information »

DANA 4820 Predictive Analytics - Qualitative Data
3

Lecture Hours: 3.0 | Seminar: 0.0 | Lab: 1.0

New Course

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.

Prerequisite(s): A passing mark from Data Analytics Math Assessment Test or a passing grade in MATH 4801 and a minimum "C" grade in DANA 4800.

More Information »

DANA 4830 Dimension Reduction & Classification I
3

Lecture Hours: 4.0 | Seminar: 0.0 | Lab: 0.0

New Course

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.

Prerequisites: A minimum "C" grade in DANA 4810.

More Information »

EXPE 4803 Employment Search Strategies
1

Lecture Hours: 1.5 | Seminar: 0.0 | Lab: 0.0

Formerly COOP 4803

This course is the third course in a series of three, and will transition students from searching for employment to successfully entering the workforce. Having covered targeted job applications in EXPE 4801, and interviewing and networking in EXPE 4802, this course will focus on job search strategies as well as workplace expectations and typical standards and policies of Canadian businesses. Additionally, this course will include workplace etiquette and behaviour, as well as managing and respecting cultural diversity. Lastly students will incorporate what they have learned from the first two courses and formulate an individual job search plan targeting specific employers or sectors.

Students will receive credit for only one of COOP 4803 and EXPE 4803.

Registration in this course is restricted to students admitted to the Post Degree Diplomas in Accounting, Applied Planning, Business Administration, Marketing Management, and Web and Mobile App Design and Development.

Prerequisite(s): A minimum "C" grade in EXPE 4801 and 4802.

More Information »

13 Credits

Term 4

Courses Credits
All of
CPSC 4830 Data Mining for Data Analytics
3

Lecture Hours: 2.0 | Seminar: 0.0 | Lab: 2.0

New Course

Once data has been gathered, it must be cleaned, processed, and analyzed in order to find the most appropriate model to descirbe 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.

Students will receive credit for only one of CPSC 4160 and 4830.

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

More Information »

DANA 4840 Classification II
3

Lecture Hours: 4.0 | Seminar: 0.0 | Lab: 0.0

New Course

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-Degree Diploma in Data Analytics.

Prerequisite(s): A minimum "C" grade in DANA 4820 and 4830.

More Information »

DANA 4850 Capstone Project
6

Lecture Hours: 4.0 | Seminar: 4.0 | Lab: 0.0

New Course

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 all of the following: CPSC 4810, CPSC 4820, DANA 4820, and DANA 4830.

Corequisite(s): CPSC 4830 and DANA 4840.

More Information »

12 Credits