University of Niagara Falls application for consent to offer an Master of Science in Data Analytics program

MASTER OF SCIENCE IN DATA ANALYTICS

Application for Ministerial Consent under the Post-

secondary Education Choice and Excellence Act, 2000

Submitted by

University of Niagara Falls Canada

May 31, 2021

New Program Review

Application for Ministerial Consent under the

Post-secondary Education Choice and Excellence Act, 2000

Proposed Nomenclature:

Master of Science in Data Analytics

Location:

Niagara Falls, Ontario

Primary Contact:

Victoria Martin

Chief Commercial & Compliance Officer

Global University Systems Canada

626 W Pender St #100, Vancouver, BC – V6B 1V9

Email: vi[email protected]

Submission Date:

May 31, 2021

Table of Contents

Section I. Introduction .................................................................................................................... 1

1.1. Academic Vision.............................................................................................................. 1

1.1.1. UNF’s Unique Emphasis...................................................................................................... 1

1.1.2. Educational Model.............................................................................................................. 2

1.2. Program Abstract............................................................................................................ 5

Section II. Degree Level Standard.................................................................................................... 7

2.1 Depth and Breadth of Knowledge...................................................................................... 7

2.2 Conceptual & Methodological Awareness ........................................................................ 7

2.3 Application of Knowledge .................................................................................................. 7

2.4 Communication Skills ......................................................................................................... 7

2.5 Awareness of Limits of Knowledge.................................................................................... 8

2.6 Autonomy and Professional Capacity................................................................................ 8

Section III. Admission, Promotion and Graduation ......................................................................... 9

3.1 Admission Requirement for Direct Entry........................................................................... 9

3.1.1. Graduate Admissions ......................................................................................................... 9

3.1.2. English Language Proficiency ........................................................................................... 10

3.2. Advanced Standing and Degree Completion................................................................... 11

3.3. Prior Learning Assessment ............................................................................................... 12

3.4. Promotion and Graduation .............................................................................................. 13

3.4.1. Promotion ......................................................................................................................... 13

3.4.2. Graduation........................................................................................................................ 13

3.4.3. Academic Standing ........................................................................................................... 13

3.4.4. Grades Scale Calculations and Evaluation ....................................................................... 14

Section IV. Program Content .......................................................................................................... 16

4.1. Overview........................................................................................................................... 16

4.2. Program Learning Outcomes............................................................................................ 16

4.3. Schedule............................................................................................................................ 20

4.4. Core Courses ..................................................................................................................... 21

4.5. Work-integrated learning................................................................................................. 24

4.6. Program Advisory Committee.......................................................................................... 26

4.6.1. Program Advisory Committee Functions......................................................................... 26

4.6.2. Membership...................................................................................................................... 27

4.6.3. Pre-PAC Consultations...................................................................................................... 27

Section V. Program Delivery .......................................................................................................... 29

5.1. Program Delivery Modes.................................................................................................. 29

5.2. Student Evaluation of Instruction .................................................................................... 31

Section VI. Capacity to Deliver........................................................................................................ 32

6.1. Facilities ............................................................................................................................ 32

6.2. Library and Learning Resources ....................................................................................... 32

6.3. Student Services ............................................................................................................... 33

6.4. Faculty Model ................................................................................................................... 34

6.5. Faculty Qualifications ....................................................................................................... 35

6.6. Research............................................................................................................................ 36

6.7. Faculty policies.................................................................................................................. 36

6.8. Enrolment & Staffing Projections .................................................................................... 36

Section VII. Credential Recognition Standard ................................................................................. 38

Section VIII.Regulation and Accreditation....................................................................................... 39

Section IX. Nomenclature ............................................................................................................... 40

Section X. Internal Quality Assurance and Development ............................................................ 41

10.1 Program Review Policy..................................................................................................... 41

Section XI. References .................................................................................................................... 44

APPENDIX A - Course Outlines ......................................................................................................... 45

APPENDIX B – Research Report ....................................................................................................... 64

APPENDIX C – IBM Data Science Competency Model..................................................................... 70

APPENDIX D – Letters of Endorsement & Support.......................................................................... 87

APPENDIX E – UNF Academic Plan.................................................................................................... 97

Section I. Introduction

1.1. Academic Vision

University of Niagara Falls Canada (UNF) will be a new private university in the City of Niagara Falls

operated by Global University Systems Canada (GUS Canada). The University will offer a suite of

innovative and relevant programs at the graduate and undergraduate level and will locate in

downtown Niagara Falls to support the city’s revitalization efforts by bringing a vibrant community

of students to the region.

Academic programs will be aligned to meet the needs of business and industry in the region while

contributing to the economic diversification and stability of Southern Ontario. UNF will work

closely with industry using Program Advisory Boards to ensure that our programming provides the

skills and knowledge for now and the future.

UNF’s innovative multi-modal educational model will be built on the foundation of digital mindset,

allowing graduates to be successful in the new economy.

UNF’s vision is “Innovative education and research for a digital world.”

1.1.1. UNF’s Unique Emphasis

Technology is driving societal transformation at an unprecedented rate whether it be artificial

intelligence, machine learning, robotics, or communication. Artificial intelligence capability is

currently doubling at a staggering rate of 3.4 months (Raymond Perrault, 2019).

UNF will focus on a foundational set of skills, knowledge and aptitudes for all graduates that will

enable them to compete in this new economy. We are calling this foundational education “Digital

Mindset”. A mindset is defined as a person’s way of thinking and their opinions. A person’s

mindset defines their abilities to be open to new methods, techniques, analysis, and actions.

In a growth mindset (as opposed to a fixed mindset) students see challenging assignments as an

exciting growth opportunity instead of one that will defeat them (Dweck, 2006). “In a growth

mindset, students understand that their talents and abilities can be developed through effort,

good teaching and persistence. They don’t necessarily think everyone’s the same or anyone can be

Einstein, but they believe everyone can get smarter if they work at it.” (Dweck, The Growth

Mindset and Education, 2012). A growth mindset can be encouraged by the way we teach by

setting difficult challenges and collaboratively building solutions using, for example, appreciative

inquiry (Cooperrider & Whitney, 2005), design thinking (Liedtka, 2018), challenge-based learning

(Nichols, Cator, & Torres, 2016) and new forms of authentic assessment in the curriculum.

Digital Transformation is a term coined to encompass the massive efforts underway in society and

business to replace analog or manual processes with digital processes or upgrading current digital

processes. It is not just technology, but the cultural, organizational, and operational changes

brought about by the integration of smart technologies. It is seen as a key to sustaining innovation

and requires specific skills and mindset.

UNF believes that for graduates to succeed and be adaptable and resilient, they need to achieve a

digital mindset, a combination of growth mindset and digital fluency.

A digital mindset therefore requires:

1. Digital fluency – a combination of technical proficiency, literacy, ethics and communication.

2. A growth mindset – a future focussed, collaborative, change oriented, learning disposition.

Examples of learning outcomes embedded in UNF programs that support the digital mindset

include:

• Embrace new approaches to doing things and discard older ineffective approaches.

• Work effectively in a team.

• Value diversity of ideas.

• Identify data insights that would be useful.

• Obtain, manipulate and clean data.

• Create useful visualizations by the analysis of complex data and tell a story.

• Identify, select and use technology appropriately.

1.1.2. Educational Model

The UNF educational model is designed around the following principles:

1. Accessible

2. Digitally Focussed

3. Relevant

1.1.2.1. Accessible

Term System: UNF’s programs will be built around a quarter system rather than the traditional 2

or 3 semester system so each academic year will consist of four terms – Fall (Oct – Dec), Winter

(Jan – Mar), Spring (Apr – Jun), Summer (Jul – Sep). While the terms are slightly shorter than

traditional terms, the level of effort will be the same and students will be able to take a maximum

of four courses rather than the five in the semester system.

A traditional university semester in Ontario is 13 weeks minus a one-week reading break, giving 12

weeks instructional time (not counting examination time). A full student load would be considered

five courses (of 3-credits each) typically involving 3 lectures per week over 12 weeks for a total of

180 lectures. A quarter system consists of a 10-week term plus an additional 1 or 2-week

examination and assessment period. A full student load would be considered four courses (of 3-

credits each) typically involving 4 contact hours in 2 contact hour blocks (structured learning, not

necessarily lectures) per week over 10 weeks for a total of 160 hours of contact. If the student

spends an additional 2 hours of effort per contact hour, then the student workload per week

would be 36 hours. To summarise, in a semester system, a course typically involves 3 classroom

hours per week for 12 weeks. In a quarter system, a course involves 4 classroom hours per week

for 10 weeks. So, a quarter system is as rigorous as a semester system and actually offers slightly

more classroom time than a semester system. The students' workload is nevertheless manageable

because a full-time student typically takes 3- 4 courses per quarter, rather than 5 courses as is

typical in a semester system.

The advantages are:

a. Efficiency, the university operates year-round, maximizing use of facilities and staff.

b. Multiple options for student completion and graduation. Given four terms per year and a

maximum course load of four courses gives greater flexibility for students when planning

their degree. A four-year program to graduation could be constructed on a 4:3:3:0 structure

i.e., 4 courses in the Fall, 3 courses in Winter, 3 courses in Spring and no courses in Summer.

This, if repeated for 4-years would give 40 courses and 120 credits to graduate. At the other

extreme an extremely capable and motivated student could complete 120 credits using a

4:4:4:4 structure in as little as 2.5 years taking a full course load with no breaks. While this

would be uncommon, it makes it clear that there are opportunities for students to complete

in shorter periods. Fewer than half of undergraduate degree students in Canada completed

their degree within four years (Statistics Canada, 2019).

c. More Opportunities for Working and Work Integrated Learning. Most students need some

additional income to avoid high student debt on graduation. The 4-term, system allows for

more flexibility in student work periods. Often an employer may have an opening for six-

months which can be accommodated by this term system. The simple fact is that the quarter

system lines-up with the way that business and government work and the traditional

semester system does not.

d. More entry points. A September intake while convenient for many is often inconvenient for

international students as late marks and visa delays often hamper their entry into the

Canadian post-secondary system. An October intake and up to three additional yearly intakes

will help all potential students.

e. Since the credits per course are the same as the they would be in a semester system, the

typical transfer of credits in and out will not be an issue. Potential disadvantages are limited

to transfer in and out via partnerships such as study abroad where a student can take a

semester at another international institution and transfer the credit back. These type of

student opportunities can be accommodated within the quarter system by combining two

quarters or by special arrangement with the partner institution.

Admissions and Transfer: UNF admissions are based on generally accepted Canadian university

standards with an added degree of flexibility that recognizes learning outside of the formal

education system. A rigorous prior learning assessment process will help more mature students

access programs and allow for the assessment of micro-credentials for the purposes of admission

and transfer as these become more widely accepted. The holistic admissions process will look at

the whole person to make sure that they will be successful and engaged in their program and will

emphasize evidence of enthusiastic enquiry.

UNF will accept credit from other institutions and not make students needlessly repeat courses.

This will mean recognizing baccalaureate courses completed at other universities and colleges

with similar academic standards. This will be facilitated by signing block transfer agreements with

Canadian colleges so that diploma holders have a clear pathway to degree completion, mirroring a

process that has been very successful in British Columbia (Merner & Bennett, 2020). The objective

here is for the complete articulation of suitable 60-credit diplomas into undergraduate programs

such that students are not required to take additional courses at the 100 and 200 level. At the

graduate level, UNF will look to admit college undergraduate honours degree holders straight into

graduate school with no additional courses where there is an appropriate match.

Delivery Model: Initially, UNF will deliver all programs in an on-campus format. However, the

University anticipates demand in other formats, particularly blended or hybrid. For working

students, the on-campus model is not attractive and part-time evening and weekend models can

take a long time to complete. UNF will offer a hybrid model that would consist of one weekend

every six weeks on-campus with the remaining components of the course delivered in an on-line

format.

Subject to student demand, UNF would introduce the hybrid model in year 1 for a single cohort in

each of the Masters programs. Future expansion to the undergraduate level will be considered as

warranted by student interest. In addition, the University intends to build out full online delivery

capabilities for each program area by year 3 of operation. This will allow students with the

flexibility to choose between on-campus, online or hybrid course delivery, including the ability to

enroll in a combination of delivery modes, to best suit their personal needs and learning style. This

will also provide the university with operational resilience to quickly pivot the instructional model

if needed in response to significant changes in its operating environment, such as restrictions

placed on in-class learning in 2020-21 because of the COVID-19 pandemic.

1.1.2.2. Digitally Focussed

An underlying theme of UNF programming is digital fluency. The pace of technology adoption and

change continues at an unprecedented rate leading to automation and disruption resulting in new

jobs requiring new skills. High demand occupations are already emerging with titles that did not

exist a short time ago (World Economic Forum, 2020), for example, Cloud Engineer, Content

Specialist, Digital Marketing Specialist, Talent Acquisition Specialist, Agile Coach, AI Specialist, Big

Data Analyst and many more. The teaching and assessment framework will ensure that students

have the specific and transferrable skills to succeed.

UNF will model what we teach with digital services for students. A recent study found that

textbook costs have outpaced inflation substantially and are impacting students. 54% of students

do not purchase the required text for a course. When textbooks are expensive (which they almost

all are) students can earn lower grades, not register or drop courses (Jhangiani & Jhangiani, 2017).

UNF aims to become be the first zero textbook cost (ZTC) university in Canada with all the required

and optional reading for courses ultimately supplied using academically appropriate open

education resources (OER) or resources available at no charge from on-line library resources.

While initially some courses may require textbooks, as those OER resources become available,

UNF will transition those courses to achieve the ZTC objective.

The in-class curriculum will be fully supported by on-line learning and content management

systems. Valuable class time will not be taken up with lectures but with content application and

problem solving. UNF will pilot emerging educational technologies such as virtual reality and

augmented reality (VR/AR) and make use of current social media tools and technologies to

enhance engagement and collaboration. The goal is fully engaged students who can learn anytime

and any place for life.

1.1.2.3. Relevant

The university will be situated on the traditional territory of the Haudenosaunee and Anishinaabe

peoples. UNF will partner with the Indigenous communities to ensure that our students, faculty

and staff can come to understand and respect Indigenous culture and tradition. Since, ultimately,

the University will have a significant percentage of international students from all over the world,

cultural sensitivity, appreciation and respect will be critical to success.

The new world of work requires some new skills and some old skills, including active listening,

speaking, critical thinking, reading comprehension, visual communication, monitoring, social

perceptiveness, coordination, time and self management, judgement, and more (RBC, 2018).

These are transferrable skills that will equip students with the ability to move between careers in

the gig economy. UNF will package these skills into programs as learning outcomes and embed

them into courses across the curriculum. Each student will be required to demonstrate by

authentic assessment that they have mastered these learning outcomes in order to graduate. For

each skill mastered a micro-credential or badge will be granted.

Work integrated learning (WIL) will be a vital component of the educational model but much

different than the standard coop model. UNF will offer multiple opportunities for students to work

with business, government and industry. For example:

• Applied Research Projects – Individual or groups of students partner with a community

organization or industry to solve a particular problem.

• Challenge Based Learning (Nichols, Cator, & Torres, 2016) – Current challenges faced by

society or business are brought into the classroom and students participate in an engage,

investigate, act curriculum model resulting in the development of new knowledge and skills

through deep engagement.

• Internships/Practica: A supervised and structured work experience for academic credit.

• Field Placements: Short-term, practical experience in relevant setting. May be part of a course

or independent.

• Service Learning: Integrating community service with classroom instruction and reflection for

a challenge identified by the community.

To enhance WIL, UNF will partner with the city to open a collaboration and innovation hub based

on the model of City Studio Vancouver (CityStudio, 2020). This centre will connect the City, Not-

for-Profit groups and businesses with students and faculty and generate new ideas and solutions

to enhance the local economy and quality of life. In addition, UNF will partner with Spark Niagara

to support business incubation (Spark Educational Innovation Centre, 2020).

1.2. Program Abstract

Data analysis is one of the fastest emerging profession in Canada. The Job Bank forecasts a

shortage of data analyst across Canada through 2029, especially in Ontario

. The purpose of the

Master of Science Data Analytics (MSDA) program is to develop big data professionals who can

grow in this high-demand career. Workplace problems frame and guide all learning in this program

https://www.jobbank.gc.ca/marketreport/summary-occupation/17882/ON

- from real-world case studies, to Internships, to the capstone project, students build the specialty

knowledge and technical competencies for a successful data analysis profession.

The delivery format of the MSDA also emulates the fast-paced workplace for Data Analysts in

Canada today. The 48-credit program can be completed in five 12-week terms. It is designed to be

delivered on campus, online and in a hybrid mode. The hybrid delivery mode is very accessible to

working students and full-time students alike. As described in Section 1.1, most of the learning in

the hybrid model is done online with three weekend on-campus intensives each term.

The MSDA program provides the core training in the Data Science Lifecycle – from Problem

Framing and Hypothesis Formulation to Data Exploration, Warehousing, Analysis, and

Visualization. Furthermore, the program allows students to choose from one of three learning

tracks to gain deeper problem-solving experience. The three tracks are:

1. Marketing Analytics Specialty – for those who want to solve marketing-related problems in

customer relationship management (CRM), product and pricing management, campaign

performance, etc.

2. Operations Analytics Specialty – for students who want to use analytics to improve quality

and optimize processes in manufacturing, supply chain management, project

management, tourism operations, healthcare delivery, and so on.

3. General Analytics – for those who want to apply analytics to multi-disciplinary problems in

government, non-profits, research, consulting, and many other fields.

Section II. Degree Level Standard

2.1 Depth and Breadth of Knowledge

Data analysis is a systematic study of data to support evidence-based decisions. The MSDA

program introduces the student to a broad range of real-world data problems, from marketing to

supply chain, to cloud computing. Furthermore, the program exposes the student to a broad set of

technological tools for data collection, exploration, warehousing and visualization commonly used

by industry. The program also draws extensively from statistics and management science to

enable an in-depth understanding of problems, solutions, and decision implications.

Program Learning Outcomes: 1, 2, 3, 4, 7, 9, 10

Please refer to list of Program Learning Outcomes in Section IV.

2.2 Conceptual & Methodological Awareness

The established process for solving data analysis problems generally begins with problem framing

and hypotheses formulation; the analyst then proceeds to data collection, data exploration and

visualization, data warehousing, data analysis and interpretation for decision support. The MSDA

curriculum builds professional competence by applying this process to solve increasingly complex

problems using diverse conceptual and technological tools in management and data science.

There is an authentic analytics case to guide learning in each of the five academic terms. The

student demonstrates competency in data analysis by applying the conceptual and technological

tools learned to the case study solution.

Program Learning Outcomes: 1, 2, 6, 7, 8

Please refer to list of Program Learning Outcomes in Section IV.

2.3 Application of Knowledge

The program greatly reduces the “knowing-doing gap” by using a predominately problem-based

approach to learning. The problems that guide learning are integrative and authentic in nature.

They require the student to apply both conceptual knowledge and technical skills to deliver a

complete solution. Furthermore, the University employs only faculty members with at least 5

years of professional experience in data analysis, data science, or business intelligence to facilitate

learning in this program.

Program Learning Outcomes: 1, 2, 3, 4, 9, 10

Please refer to list of Program Learning Outcomes in Section IV.

2.4 Communication Skills

Professional data analysts require highly developed communication skills. Likewise, the program

integrates communication skills development throughout the data analysis process – skills such as

listening, asking questions, negotiation, conflict resolution, framing problems, leading project

teams, and presenting complex insights to clients using various data visualization tools.

Program Learning Outcomes: 5, 6, 7, 8

Please refer to list of Program Learning Outcomes in Section IV.

2.5 Awareness of Limits of Knowledge

While the university provides learning resources, students take responsibility for their own

learning in order to achieve the intended outcomes in a problem-based learning environment. This

will invariably involve inquiries into concepts, technologies, and methodologies outside of the

body of knowledge prescribed in the MSDA program.

Program Learning Outcomes: 1, 2, 4, 6, 8

Please refer to list of Program Learning Outcomes in Section IV.

2.6 Autonomy and Professional Capacity

An underlying theme of our programming is digital fluency. The pace of technology adoption and

change continues at an unprecedented rate leading to automation and disruption resulting in new

jobs requiring new skills. High demand occupations are already emerging with titles that did not

exist a short time ago (World Economic Forum, 2020). For example, Cloud Engineer, Content

Specialist, Digital Marketing Specialist, Talent Acquisition Specialist, Agile Coach, AI Specialist, Big

Data Analyst and many more. Our teaching and assessment framework will ensure that our

students have the specific and transferrable skills to succeed. Our goal is to develop fully engaged

students who can learn anytime and any place for life.

Professional and academic integrity is at the heart of the MSDA experience. At every stage of

learning, the program challenges the students to critically examine the ethical use of big data

analytics; especially involving issues of confidentiality, privacy, human agency, and social

responsibility. The University also provides and enforces its policy and guidelines for academic

integrity in all its programs.

Program Learning Outcomes: 1, 2, 5, 6, 7, 8

Please refer to list of Program Learning Outcomes in Section IV.

Section III. Admission, Promotion and Graduation

3.1 Admission Requirement for Direct Entry

In addition to the standard graduate admissions requirements provided below, successful

applicants to the MSDA program will have a background in business management, math, and

computer information systems. They will have completed at least one undergraduate course in

statistics or quantitative methods, an undergraduate course in computer programming or

management information systems and are competent in MS-Office applications.

UNF’s admission policies and regulations are intended to identify students who are best able to be

successful in their academic studies and contribute to the university community. The Admissions

Policy outlines the general criteria and regulations for admission to UNF’s academic programs.

The University accepts qualified applicants who meet the stated admission requirements, subject

to program enrollment limits. The University has clearly articulated admission requirements that

support the likelihood of success in undergraduate and graduate programs and adhere to

necessary academic standards. In addition, English is the language of instruction at the University

and, therefore, successful applicants must demonstrate English language proficiency levels

essential for academic success in a Canadian university.

3.1.1. Graduate Admissions Requirements

Applicants to the graduate programs may qualify for admission based on one of the following:

3.1.1.1. Bachelor’s degree

Completion of a recognized undergraduate degree equivalent to the four-year honours degree

standard identified in the PEQAB Degree Level Standard and the Ontario Qualifications

Framework, in an appropriate specialization, or relevant bridging studies, with CGPA of 3.0 (on

4.33 scale) or better.

3.1.1.2. Special Admission

Applicants who do not meet the minimum admission standards may be considered for Special

Admission into a graduate program by the Registrar if they demonstrate a significant depth and

breadth of relevant work experience and hold an undergraduate degree. Applications under this

category would be subject to the Prior Learning Assessment and Recognition policy and

procedures.

3.1.1.3. Admission to a Second or Subsequent Master’s Degree

Applicants who have been awarded a Master’s Degree or higher level credential from a recognized

Canadian university (or equivalent) with a CGPA of 3.00 (on 4.33 scale) or better may be admitted

to a graduate program. The principal areas of study or academic emphasis of the second degree

must be distinct from that of the first degree.

3.1.2. English Language Proficiency

Applicants whose first language is not English, or who have received their education in another

language, must provide evidence of English language proficiency at a university level in one of the

following ways:

a. Required score on a recognized English proficiency test as follows:

TEST

MINIMUM SCORE for ADMISSION

Academic IELTS

6.5 overall with minimum of 6.0 in

the writing band

TOEFL iBT

88 overall with minimum of 20 in

each component

PTE Academic

61 overall with minimum score of

60 in writing

CAEL

70 overall with minimum of 60 in

each subset

Duolingo 2 110 overall

Password

6.5 overall with minimum of 6.0 in

the writing band

Cambridge Test 3 176 overall

b. Successful completion of a minimum of 30 credits of academic post-secondary education at a

recognized institution where English is the language of instruction and where the school is

located in a country where English is an official language.

c. Successful completion of the University’s English preparation courses.

d. Successful completion of a recognized English preparation course from another institution

where students have demonstrated proficiency at an equivalent to the required IELTS score or

better.

e. Successful completion of ON English 12 or equivalent taken in Canada as part of a high school

graduation program with a final overall grade of C or better.

f. Successful completion of a 3-credit academic English course from a Canadian post-secondary

institution that is transferable to UNF. A minimum grade of C or higher is required.

g. Graduation from a secondary school attended for four or more consecutive years of full-time

education where English was the language of instruction and where the school is located in a

listed country approved by the Registrar Completion of International Baccalaureate English

A1/A2 or English Literature and Performance with a score of 4 or higher.

The Registrar may require proof of English language proficiency from applicants who attended

English language-based education systems if there are deficiencies in language proficiency when

the application package is reviewed.

English language proficiency test scores are valid for admissions purposes for a maximum of 2

years from the date of the score report.

Applicants who do not meet the minimum English language proficiency requirements may be

conditionally admitted to a program, subject to the successful completion of the appropriate level

of a specified English preparation program. Applicants will not be permitted to start the program

until proof of the required minimum level of English language proficiency is achieved.

The Admissions Policy is found in the UNF Policies and Procedures attachment.

3.2. Advanced Standing and Degree Completion

The University supports and abides by the principles of the Pan-Canadian Protocol on the

Transferability of University Credits and the principles of transfer credit as defined by the ON

Council on Articulation and Transfer. Students should not be required to retake academic courses

successfully completed elsewhere nor should they expect to receive duplicate credit for equivalent

courses.

The University is committed to enabling students to transfer academic credit taken at other

recognized institutions and programs where there is a reasonable fit or match with the majority of

learning outcomes of a course.

The following precepts will apply to transfer credit articulation and evaluations:

1. Transfer arrangements will maintain the academic integrity of the University’s courses and

programs.

2. Only courses taken at recognized post-secondary institutions (public or private) or

institutions/organizations approved by Academic Council will be considered for transfer credit.

3. Courses completed through non-recognized institutions or organizations and learning

obtained through work and life experience may be considered for recognition through the

Prior Learning Assessment & Recognition (PLAR) process for possible credit. Such experiences

are not awarded through transfer credit.

4. Once transfer credit has been granted for a course from the sending institution, it cannot be

used for transfer credit towards any subsequent course(s).

5. Only courses completed within the last 10 years will usually be eligible to be considered for

transfer credit.

6. In general, transfer credit precedent decisions are valid for no more than 5 years after which

courses must be re-articulated.

7. Transfer credit will normally be granted for a course only where the University offers a parallel

or similar course with substantially the same content at a similar level.

8. Specifically, lower-level undergraduate courses (100-200) do not normally receive credit as

upper level (300-400) undergraduate courses, and undergraduate courses do not receive

credit as graduate courses.

9. For undergraduate programs, courses of appropriate academic content for which the

University does not offer a similar course may be considered for elective credit.

10. Not all courses are eligible for transfer credit. Some courses must be completed as part of the

degree pathway requirements. A list of courses not eligible for transfer credit is maintained by

the Registrar’s Office.

For graduate programs:

• Transfer credit will only be granted for graduate courses with a minimum grade equivalent of

3.00 (B) or better.

• Students may receive credit for up to 50% of the program through a combination of transfer

credit and PLAR toward degree requirements with no more than two (2) courses granted

through PLAR.

• Courses used to meet the requirements of a previously earned credential will not be eligible

for transfer credit.

• Courses used to satisfy admission requirements to a program will not be eligible for transfer

credit.

The Registrar’s Office maintains records of articulation agreements as well as articulation

precedents; undertakes preliminary assessment of all requests for transfer credit and for assigning

credit based on precedents and articulation agreements; researches and develops

recommendations for transfer credit precedents; logs and tracks all transfer credit and precedent

decisions; and is responsible for notifying students of transfer credit decisions, including the right

of appeal. The VP Academic, in consultation with the Program Chair and Academic Council,

approves transfer credit and block transfer credit decisions for articulation agreements.

The Transfer Credit Policy is included in the UNF Policies and Procedures attachment.

3.3. Prior Learning Assessment

UNF recognizes that students come to the University with a variety of backgrounds and learning

experiences. Evaluation of prior learning can improve access to and accelerate a student’s

progress toward completion of a degree or program. The University will grant credit, where

warranted, for a student’s demonstrated knowledge and skill that is consistent with the learning

outcomes and education standards of the University’s courses and programs. The awarding of

credit for prior learning must maintain the academic integrity of the University’s courses and

programs. The Prior Learning Assessment and Recognition Policy, included in the UNF Policies and

Procedures attachment, establishes principles, standards and criteria for the granting of academic

credit for learning gained through non-formal or unrecognized education, training, or experience.

The University adheres to the following principles in the recognition and evaluation of prior

learning:

1. Recognition and credit will be given for demonstrated knowledge, skills and attributes and not

for experience alone.

2. Learning assessed for post-secondary credit should be:

a. linked to established learning outcomes or other criteria consistent with institutional

standards for a given course and program;

b. transferable to contexts other than the one in which it was learned;

c. current and relevant;

d. at a level of achievement equivalent to that of other learners engaged in studies at

that level in that program or subject area;

e. assessed using a range of strategies consistent with institutional standards for a given

course.

3. Some courses must be completed at the University as part of the degree pathway

requirements and are not available for credit from prior learning assessment. A list of such

courses will be maintained by the Registrar’s Office.

4. The amount of credit awarded for prior learning is granted under the following conditions:

5. For graduate programs:

a. students may receive up to 50% of a combination of transfer credit and PLAR toward

degree requirements with no more than two courses granted through PLAR.

b. courses used to meet the requirements of a previously earned credential will not be

eligible for credit toward the graduate degree.

6. Assessment of the learning is the responsibility of faculty who are content specialists.

7. Credit given as a result of a prior learning assessment will be identified as such on the student

transcript and will not have an assigned grade. PLAR credits are not included in the

University’s cumulative grade point average calculation.

3.4. Promotion and Graduation

The Promotion and Graduation Policy, included in the UNF Policies and Procedures attachment,

outlines the University standard for progression from one level to the next within a program of

study and for eligibility to graduate from a program of study.

3.4.1. Promotion

In order to move from one level to the next within a program of study, a student must complete

their current term with an academic standing of Good Standing, Academic Alert, or Academic

Probation as defined in the Academic Standing policy. Student with Academic Suspension or

Required to Withdraw status are not eligible to progress within their program of study. In addition,

students must meet any additional progression requirements for their program of study as

outlined in the Academic Calendar. Individual courses may also have pre-requisite conditions that

must be satisfied prior to enrollment in the course.

3.4.2. Graduation

To be eligible to graduate from a program of study, a student must be in Good Academic Standing

as defined in the Academic Standing policy. In addition, a student must satisfy all graduation

requirements for their program of study as outlined in the Academic Calendar in effect at the time

of admission, unless alternate requirements have been approved in writing by the Program Chair.

A student must have paid all outstanding fees to the University to be eligible to graduate.

3.4.3. Academic Standing

The Academic Standing Policy and the Academic Standing Procedure, included in the UNF Policies

and Procedures attachment, outlines the University standard for assessment of academic standing

and continuance in University registration. Students’ academic performance is assessed at the end

of each term of enrolment to determine their academic standing. Until a student has completed 2

(two) courses at the University, there is no academic standing evaluation completed.

Subsequently, academic standing will be evaluated at the completion of every term.

Graduate students are deemed to be in good academic standing if their CGPA is 3.0 or higher.

3.4.4. Grades Scale Calculations and Evaluation

Academic grades are a measure of the performance of a student in individual courses or graded

components of a program of study. The Grades Scale, Calculations and Evaluation Policy, included

in the UNF Policies and Procedures attachment, outlines the framework for grading and grade

scales used for graduate courses at the University.

3.4.4.1. Grading Scale

All final grades and GPAs are assigned and calculated according to the graduate grading scales.

Each letter grade used at the University has a corresponding numeric value which is used to

calculate grade point averages.

The Graduate Grading Scale is:

Definition

Standard of Evidence

Percentage

Letter Grade

Numeric Value

A – Excellent

Considerable evidence

Exceptional. Expertise in all

learning outcomes.

90% - 100%

A+

4.33

of: original thinking;

analysis and synthesis;

extensive knowledge

Outstanding. Expertise in some

learning

outcomes and mastery

of most.

85% - 89%

4.00

base; initiative; and,

fluency of expression

Excellent. Mastery of most

learning outcomes, expertise in

some.

80% - 84%

A-

3.67

B – Good

Clearly above average

Very Good. Mastery of all

learning outcomes.

76% - 79%

B+

3.33

performance with

knowledge of principles

Good. Mastery of most learning

outcomes, competent in some.

72% - 75%

3.00

and facts generally

complete

Competent. Competent in most

learning outcomes, mastery of

some.

68% - 71%

B-

2.67

C – Satisfactory

Evidence of some

understanding of the

subject matter and

ability to develop

solutions to basic

problems.

Adequate. Competent in all

learning outcomes.

60% - 67%

2.00

F – Fail

Knowledge of principles

and facts is fragmentary.

Fail. No basic ability in most

learning outcomes.

0 - 59%

0.00

3.4.4.2. GPA Calculation

Only grades for courses completed at the University are calculated into the term and cumulative

GPA. Grades for transfer credits from other institutions will not be calculated into the University’s

GPAs. Grades for Prior Learning Assessment (PLAR) credits granted will not be calculated into the

University’s GPAs.

When a course is taken more than once, the credits, grades, and corresponding grade point values

will show on the student’s record in each instance but will count only once towards the degree.

The GPA is calculated using only the highest grade achieved for the course. Courses with

Withdrawal notations are not included in the GPA calculations.

Section IV.Program Content

4.1. Overview

The MSDA program focuses on preparing graduates to help organizations utilize data to support

decision-making that increases organizational value in today’s data driven world. Sample jobs in

data analytics include big data specialist, policy analyst, business intelligence officer, data

visualization specialist, and customer intelligence analyst. These jobs require a strong foundation

in big data - collecting, managing, analyzing and presenting data to shape policy, create innovative

products and services, drive process improvement, enhance user experience, and increase ROI.

4.2. Program Learning Outcomes

Core Outcomes and Assessment Criteria:

The MSDA is a professions-oriented program and, upon completion, graduates are competent in:

1. METHODOLOGY - Provide insights to complex business and operational problems by

applying a rigorous data analysis process involving problem articulation, hypotheses

formulation, data collection and management, statistical analysis, visualization, and

presentation.

a. Demonstrate understanding of what is data science and what data analysts do.

b. Demonstrate use of methodologies in the execution of the analytics cycle.

2. TECHNOLOGY - Utilize software tools to support the data analysis process; including tools

for data mining, data warehousing, statistical analysis, and visualization. Graduates can

demonstrate advanced proficiency in SQL, Excel, Power BI, Tableau, Python, and SAS.

a. Demonstrate ability to identify and collect data – multiple formats.

b. Demonstrate ability to manipulate, transform, and clean data.

c. Demonstrate expertise with techniques to deal with missing values, outliers,

unbalanced data, as well as data normalization.

d. Demonstrate through a project the ability to construct usable data sets.

e. Demonstrate understanding of different modeling techniques.

f. Demonstrate understanding of model validation and selection techniques.

g. Deploy and monitor a validated model in an operational environment.

h. Demonstrate ability to visualize data and extract insights.

i. Demonstrate knowledge of Python programming skills.

3. STATISTICS - Apply descriptive, predictive, and prescriptive statistical methods to create

decision models for inference, forecasting, simulation, and optimization.

a. Understand sampling, probability theory, and probability distributions.

b. Demonstrate knowledge of descriptive statistical concepts.

c. Demonstrate knowledge of inferential statistics.

d. Implement descriptive and inferential statistics using Excel and/or Python.

e. Demonstrate understanding of Linear Algebra principles for machine learning.

4. APPLICATIONS - Analyze and solve data analysis problems in marketing, finance,

manufacturing, supply chain, as well as various public and non-profit domains.

a. Demonstrate ability to characterize a business problem.

b. Demonstrate ability to formulate a business problem as a hypothesis question.

c. Demonstrate through a project the ability to analyze a dataset and communicate

insights.

d. Demonstrate through a project the ability to deploy and use a deployed model.

e. Demonstrate through a project the ability to test different models on a dataset,

validate and select the best model, and communicate results.

5. COMMUNICATIONS - Demonstrate effective communication and leadership skills in a

high-performance, cross-cultural team environment typical in a Canadian workplace.

a. Participate as a data analyst on client engagements (internal or external)

b. Communicate results translating insight into business value.

c. Contribute to the profession by teaching or mentoring others.

6. SOFTWARE ENGINEERING - Manage data analytics solutions development lifecycle based

on acceptable software engineering methodologies.

a. Demonstrate through a project the ability to plan for the execution of a project.

b. Demonstrate through a project the ability to test different models on a dataset,

validate and select the best model, and communicate results.

7. DATA ETHICS – Evaluate ethical implications of big data as involving issues of

confidentiality, privacy, human agency, and social responsibility.

a. Understand the principle of keeping customer identity private in big data.

b. Understand how to treat private information confidential.

c. Understand how to prevent institutionalized unfair or biased practices in big data.

8. DIGITAL MINDSET – Experience “digital transformation” on a personal level; able to see

and embark on pathways to career growth in the digital world.

a. Understand the concept of big data, and how big data is used at organizations.

b. Understand the big data ecosystem and its major components.

9. MARKETING ANALYTICS: Synthesize large enterprise data sets from various sources to

derive meaningful and actionable insights and influence decision making. Students will

have practical experience in CRM analytics tools from leading software providers like

Salesforce and Tableau.

a. Demonstrate ability to characterize a marketing problem.

b. Demonstrate ability to formulate a marketing problem as a hypothesis question.

c. Demonstrate through a project the ability to analyze a marketing dataset and

communicate insights.

d. Demonstrate through a project the ability to deploy and use a deployed marketing

model.

10. OPERATIONS ANALYTICS: Perform complex data analysis to identify opportunities to

reduce operational costs as well as improve efficiencies and customer experience.

Students will have practical experience in ERP analytics tools for major platforms like SAP.

a. Demonstrate ability to characterize an operations problem.

b. Demonstrate ability to formulate an operations problem as a hypothesis question.

c. Demonstrate through a project the ability to analyze an operations dataset and

communicate insights.

d. Demonstrate through a project the ability to deploy and use a deployed.

Master of Science Data Analytics – Curriculum /Learning Outcome Table

Program

Curriculum

Learning Outcomes

Core Requirements

DA500

Principle of

Analytics

CS500

SQL Databases

BI500

Marketing Analytics

DA501

Data Analytics Case

Study 1

DA510

Predictive Analytics

CS510

Data Warehousing

and Visualization

BI510

Operations

Analytics

DA511

Data Analytics Case

Study 2

DA600

Prescriptive

Analytics

CS600

Advanced Data

Visualization

CS610

Python for

Analytics

Program

Curriculum

Learning Outcomes

DA611

Data Analytics Case

Study 3

CS620

Agile Software Dev.

DA699

Capstone Project

Specializations and Non-Specialization Internships

BI621

Adv Marketing

Analytics Internship

BI622

Adv Operations

Analytics Internship

BI623

Adv Analytics

Internship

4.4. Core Courses

The following table provides descriptions of each course as they will appear in the academic

calendar and related documentation. Core and Specialization course outlines can be found in

Appendix A.

Term 1

Principles of Analytics

This course covers the core concepts and applications of

analytics in different domains. First part of the course

introduces the students to the main concepts and tools of

analytics (e.g., data querying and reporting, data access and

management, data cleaning, statistical programming, data

warehousing, relational databases, and statistical analysis of

databases). There are intentional discussions of the social and

ethical issues of data analytics (e.g., privacy, confidentiality).

The students then apply the principles of descriptive analytics

to different domains such as marketing, quality control, public

policy and other domains of their interest.

SQL Databases

SQL competency is the single most important skillset for a Data

Analyst. This course provides a comprehensive introduction to

the language of relational databases: Structured Query

Language (SQL). Topics covered include: Entity-Relationship

modeling, the Relational Model, the SQL language: data

retrieval statements, data manipulation and data definition

statements.

Marketing Analytics

Using a case-study method, this course introduces students to

the scope of marketing analytics. It explores how organizations

use data analytics to support the 4 Ps of marketing – product,

price, place, and promotion. This course emphasizes the

practical needs of employers in social media marketing, search

engine optimization, customer insights, campaign

performance, pricing strategy, category management, and

sales effectiveness. With a clear vision of the depths and

breadths of marketing analytics, the student formulates his or

her own plan to acquire the knowledge and skills required for

successful employment in this field.

Data Analytics Case Study 1

This course uses a real-world analytics problem to guide and to

apply learning in DA500, CS500, and BI500. Students use

knowledge in data science lifecycle, descriptive analytics, SQL,

Excel, and relational database to support a marketing decision

problem (e.g., customer relationship management, product

management, KPI management, etc.). They formulate research

questions and hypotheses, prepare internal and external data,

build and deploy models, and use charts and visualizations to

share insights.

Term 2

Predictive Analytics

This course builds upon the knowledge and skills learned in

DA500 Principles of Data Analytics course. It begins with

framing machine learning problems for analysis and moves

progressively into predictive modelling methods like

regression analysis and forecasting techniques. Finally, this

course introduces predictive analytics to data mining and

machine learning applications in everyday life like healthcare

diagnostics, consumer behavior, credit risks analysis, etc.

Data Warehousing and

Visualization

This course provides an end-to-end hands-on data analytics

experience using Microsoft’s Power BI business analytics

service. The students learn to connect, import, and clean data

from multiple sources, create data models, analysis to find

insights, and to create visual reports, dashboards, as well as

mobile apps for users.

Operations Analytics

Using a case-study method, this course introduces students to

the scope of operations analytics. It explores how

organizations use data analytics to reduce operational costs

and improve efficiencies and customer experience. This course

emphasizes the practical needs of employers in supply chain

planning and forecasting, resources scheduling, business

process optimization, quality control, and strategy execution.

With a clear vision of the depths and breadths of operations

analytics, the student formulates his or her own plan to

acquire the knowledge and skills required for successful

employment in this field.

Data Analytics Case Study 2

This course uses a real-world analytics problem to guide and to

apply learning in DA510, CS510, and BI510. Students use

knowledge in machine learning, predictive analytics, SQL,

Excel, Power BI to support an operational decision problem

(e.g., supply chain management, quality control, business

process optimization, etc.). They formulate research questions

and hypotheses, prepare internal and external data, build and

deploy models, and use charts and visualizations to share

insights.

Term 3

Prescriptive Analytics

This course introduces statistical optimization methods and

tools to build decision support and automation models.

Students have opportunity to explore and evaluate

prescriptive analytics models in healthcare, finance, logistics

and other fields of their own interest. Students also develop

user-friendly decision support models using Excel, Python, and

other tools available to them.

Advanced Data Visualization

This course focuses on advanced data visualization

development using Tableau. Students first explore the range of

visualizations published by the worldwide Tableau community

on Tableau Public. They then acquire end-to-end experience

using Tableau Desktop to extract, clean, explore, and analyze

data from different sources, share insights using visuals, maps,

reports and dashboards. Students can further explore how

different industries use Tableau as part of their business

intelligence solution.

Python for Data Analysis

This course focuses on using Python in a data analysis context.

Students learn to manipulate data, perform statistical analysis

and visualization with Python and tools like Jupyter, Numpy,

and Pandas. This course also considers how to integrate

Python codes with other analytics tools like Excel, Power BI,

and Tableau.

Data Analytics Case Study 3

This course uses a real-world analytics problem to guide and to

apply learning in DA600, CS600, and CS610. Students use

knowledge in machine learning, prescriptive analytics, Python,

Tableau Desktop to support a complex decision. They

formulate research questions and hypotheses, prepare

internal and external data, build and deploy models, and use

Tableau visualizations to share insights.

Term 4

Agile Software Development

This course introduces agile software development practices

to big data analytics. The agile approach involves discovering

requirements and delivering solutions through the

collaborative effort of self-organizing and cross-functional

teams and their clients. It advocates adaptive planning,

evolutionary development, early delivery, and continual

improvement, and it encourages flexible responses to change.

It is the predominant guiding methodology in today’s data

analytics workplace.

Advanced Analytics Specialty

Internship

The Advanced Analytics Internship develops consulting skills

and provides the student the opportunity to gain analytics

qualifications in their chosen specialty. A learning contract

governs the obligations of the instructor and the student in

this self-directed learning experience. An acceptable learning

contract satisfies these basic requirements:

1. The instructor, the student, and the client agree to the

deliverables in the Internship portfolio.

2. Within the scope of the student’s chosen specialty and

substantively applies the data analytics knowledge and skills

acquired in this program.

3. Leads to new specialty knowledge, e.g., Salesforce/CRM,

SAP/ERP, SAS or R programming, Oracle DBMS, SPSS, etc.

4. Combines structured learning and experiential learning to

meet university policy requirements. Students demonstrate

structured learning with certifications, e.g., Coursera

certificates, PMP, Six Sigma, SAP, Tableau Certification, etc.

5. A reflective paper summarizing the outcomes from the

Internship.

Term 5

Capstone Project

In this course students complete a major big data consulting

project with a client. The client can be a student’s employer, or

a client of Spark Niagara (or equivalent). Students focus on

project initiation, planning, execution, monitoring, controlling

and closing a project guided by a faculty advisor. The student

signs a customized learning contract reflects the specific

project responsibilities, deliverables, and learning objectives

for the course. Students are responsible for identifying the

projects that they will work on, writing the project description,

submitting the description to their advisor for approval and

completing all project deliverables in a timely manner.

4.5. Work-integrated learning

There are two work-integrated learning courses required in the MSDA program where students

are required to work with external clients on an analytics project sponsored by the client.

1. BI621/622/623 - Analytics Internship (depending on the student’s chosen specialty)

2. DA699 - Capstone Project

The detailed descriptions for these courses are provided in Appendix A.

The MSDA Program Chair and the course instructor will extend to students support in:

a. Client connection. In many cases, the students’ employers will be sponsoring their

projects. However, should a student need to find a sponsor, the program will provide

all necessary help to arrange for an appropriate sponsor.

b. Learning contract. The instructor will work with the student and the client to develop

a learning contract that will satisfy the requirements of each party.

c. Ongoing guidance. The instructor is available to student during the work-integrated

experience to provide guidance and to help resolve problems with client. In addition,

the instructor will meet with the client and student at least once during the course for

formative assessment.

The following are the learning outcomes of the work-integrated learning experiences for the

MSDA program and the method by which they will be assessed.

a. Assessment Criteria. The performance criteria will be included in each student’s

learning contract in the form of a rubric. Although all the core outcomes will be

assessed, the criteria will differ based on the student’s chosen specialty (Marketing,

Operations, No Specialty), and the project.

b. Formative Assessment. The course instructor will meet with the student and client, at

least once, during the course to evaluate the learning progress and to provide

feedback.

c. Summative Assessment. The course instructor will meet with the student and the

client to evaluate how well the course outcomes have been achieved.

d. Assessment Feedback. The assessment results for the class will be fed back to the

Program Chair in order to enhance the curriculum, especially considering the changing

requirements of the job market and the relevance of the program delivery.

The following is a general guide for developing criterion-referenced rubrics in all work-integrated

experience courses in the MSDA program:

BI621/622/623, DA699 (Work-Integrated Learning Courses)

Assessment Component and Performance Criteria

Weight

Outcomes

Assessed

Data Analytics Project

• Demonstrate use of methodologies in the execution

of the analytics cycle.

• Demonstrate the ability to complete a project using

all skills acquired up to this point: data exploration,

descriptive and inferential statistics, and data

visualizations.

• Learn how to build models using libraries and

algorithms such as regressions, logistic regressions,

decision trees, boosting, random forest, support

Vector Machines, association rules, classification,

clustering, neural networks, time series, survival

analysis, etc.

1, 2, 3

Oral Presentation and Discussion

• Understand what big data is and how big data is used

at organizations; understand the concepts and major

applications of distributed and cloud computing

paradigm; demonstrate knowledge of the big data

ecosystems.

• Demonstrate the ability to setup a new project and

follow the application of the scientific method and

the Agile methodology; build a report explaining the

project plan; deliver a presentation sharing the

project plan and demonstrate solid communication

skills (written and verbal).

6, (7, 8)

Project Client Mentor Evaluation

• Formulate business problem as a research question

with associated hypotheses, determine what data is

needed to test the hypotheses, and ensure

4, 5

hypotheses to be tested are aligned with business

value.

• Participate as a data analyst in project with clients;

demonstrate teamwork abilities, and the ability to

manage; project risks, and stakeholder conflict.

The work-integrated learning experience should, preferably, be a paid contract or position. Most

likely, the project sponsor will be the student’s employer, or prospective employer. If the student

does not have a work permit in Canada, the Program Chair will arrange for a unpaid internship.

In addition, the MSDA program also integrates real world work experience into the program by

using:

1. Competency models from IBM and Indeed to guide learning outcomes formulation

(See Appendix B & C). This ensures that workplace knowledge and skills are fully

integrated into the curriculum at a graduate level.

2. Proprietary case studies developed exclusively for this program by analytics

consultants. These progressively complex cases develop the student’s ability to solve

real world problems. Analytics professionals develop the assessment criteria for these

cases based on authentic workplace expectations and standards.

3. The Advanced Analytics Internship develops real-world consulting skills and provides

the student the opportunity to gain analytics qualifications in their chosen specialty,

and by working with a client organization.

4. The Capstone Project where students complete a major big data consulting project

with a client. The client can be a student’s employer, a potential employer, or a client

of Spark Niagara (or a similar incubator or innovation hub).

4.6. Program Advisory Committee

The University will solicit input on a regular basis from Program Advisory Committees (PACs) to

ensure the continued currency, relevancy and quality of its existing programs and to support new

program development. The Program Advisory Committee Policy is included in the UNF Policies and

Procedures attachment.

4.6.1. Program Advisory Committee Functions

Program Advisory Committees may be engaged in any or all of the following activities:

• Assist UNF in defining objectives and outcomes of a program of study that are aligned with

the University’s goals and specific skills needed by students to achieve program objectives.

• Advise UNF in the development of the curriculum to meet program objectives and learning

outcomes.

• Assist in the evaluation of a program of study, the curriculum, and national or provincial

requirements where applicable.

• Assist with student work placements.

• Advise UNF on labour market changes that may impact courses and programs, as well as

the employment of UNF graduates.

• Advise UNF on requirements for new programs of study that will meet new or emerging

needs within the community, province, country, or abroad.

• Participate in the Self-Study process during formal program reviews, as outlined in each

Program Review Policy and Procedure.

In undertaking these functions, PACs help to foster alliances with key organizations for the

University, ensure a flow of appropriately educated and professionally aware graduates into the

community, and promote UNF and its programs.

4.6.2. Membership

Program Advisory Committees will draw their membership from business, industry and

professions related to programs of study at UNF. Appointments to Program Advisory Committees

will be made by the Vice President, Academic.

Each PAC will normally consist of a minimum of five external members who will serve a three-year

term, with a maximum of two terms per member. Newly formed PACs will have terms staggered

and for terms of less than 3 (three) years, the maximum number of terms served will be increased

to three. The appropriate academic leader (normally the Program Chair) for the program(s) of

interest will be an ex-officio member of the committee.

4.6.3. Pre-PAC Consultations

Upon approval of University status and the program, the Program Advisory Committee (PAC) will

be established.

For the purpose of the development of this program application, Pre-PAC consultation has

occurred in various formats – ongoing Steering Committee consultation; individual consultations

and reviews of proposed program content and outcomes; and a formal consultation session held

on February 12th. In addition, Section VII of this application documents market research

conducted to valid the program design, content, and outcomes. Letters of endorsement from the

Pre-PAC consultation process are included in Appendix D.

Section V. Program Delivery

The MSDA program uses a problem-based learning approach. Teaching and learning simulate how

knowledge and skills are acquired in the workplace. Students achieve the learning outcomes

through the mediation of a rich library of data science resources and MOOCs, an experienced and

credentialed instructor, and real-world proprietary case studies to guide learning.

Assessment is authentic and criterion referenced. There is a major case study each term

integrating the intended learning outcomes of that term. A corresponding assessment rubric

specifies criterion of achievement levels. The instructor uses both the case study and the rubric as

a tool for teaching and for discussion with students about their achievement level.

5.1. Program Delivery Modes

Initially, we propose to deliver all programs in an on-campus format with full-time and part-time

study options available. On-campus delivery will be built around a quarter system rather than the

traditional 2 or 3 semester system so each academic year will consist of four terms – Fall (Oct –

Dec), Winter (Jan – Mar), Spring (Apr – Jun), Summer (Jul – Sep). While the terms are slightly

shorter than traditional terms, the level of effort will be the same and graduate students will

typically enroll in three, three-credit courses per term. Each three-credit course typically requires

4 contact hours (structured learning, not necessarily lectures) per week over 10 weeks for a total

of 40 hours of contact per course compared to 36 contact hours per semester in the typical

semester system. As a result, the quarter system offers slightly more contact time than a

semester system; however, the students' workload is nevertheless manageable because a full-time

graduate student typically takes 3- 4 courses per quarter, rather than 4-5 courses as is typical in a

semester system.

In addition to on-campus delivery, we anticipate demand in other formats, particularly online and

hybrid delivery. For many students, the full-time on-campus model does not meet their individual

needs and part-time evening and weekend models can take a long time to complete.

UNF will offer a hybrid model that would consist of one weekend every six weeks on-campus with

the remaining components of the course delivered in an on-line format. A typical term schedule

would be as follows for Fall term 2021 as an example:

OC 1

•First on campus weekend

•12 hours contact - orientation and instruction

•25-26 Sep

OL1

•FIrst on-line session

•6-weeks instruction

•27 Sep - 5 Nov

OC 2

•Second on-campus weekend

•12 hours contact - orientation and instruction

•6-7 Nov

OL 2

•Second on-line session

•6-weeks instruction

•8 Nov - 17 Dec

OC 3

•Third on-campus weekend

•12 hours - instruction and assessment

•18/19 Dec

For a 3-course load term, each student would be expected to commit 22 hours per week of effort

on course work to be able to complete the term. While this is a significant commitment it is not

unrealistic, Royal Roads University in Victoria, BC has 25 years of success with a similar model

involving similar levels of effort in the on-line portions (see for example,

https://www.royalroads.ca/prospective-students/ma-professional-communication/learning-

model). Subject to student demand, we would introduce the hybrid model in year 1 for a single

cohort in the program.

In addition, we intend to build out full online delivery capabilities by year 3 of operation. This will

allow students with the flexibility to choose between on-campus, online or hybrid course delivery,

including the ability to enroll in a combination of delivery modes, to best suit their personal needs

and learning style. This will also provide the university with operational resilience to quickly pivot

the instructional model if needed in response to significant changes in its operating environment,

such as restrictions placed on in-class learning in 2020-21 because of the COVID-19 pandemic.

Global University Systems, the parent company of UNF, has extensive experience in the delivery of

on-line learning and that, coupled with the Centre for Teaching Excellence, will ensure that any on-

line delivery and assessment follow learning outcomes and assessment criteria that are identical

with the fully on-campus version. Existing expertise includes content digitisation, online teaching

methods and pedagogy and online learner engagement techniques and is successfully used at

institutions across the globe, including University Canada West. To support online learners, GUS

institutions offer Virtual Student Lounges with access to virtual Student Services including

Academic Advising & Enrollment Services and live streaming of guest speakers and special events.

Where possible, once programs are approved, UNF will build courses in the form of units. Each

unit will have learning outcomes, content, and assessment. This concept of learning objects or

units is not new (Churchill, 2007) but becomes difficult if not built initially into the course design.

Units can be reused in other courses or bundled with other units to potentially make micro-

credentials that could be offered independently of degree credit as continuing studies offerings.

5.2. Student Evaluation of Instruction

The University will administer a Student Evaluation of Instruction survey for online and on-campus

courses each term.

The purpose of the SEI instruments will be:

• To obtain feedback on UNF courses and instructor activities from a student perspective which

will assist in quality assurance.

• To provide faculty with information to be used for the improvement of courses and

instruction.

• To assist the program head in the annual evaluation of teaching performance.

• To identify opportunities for course improvement, professional development and greater

consistency in course delivery.

The SEI survey instrument will contain standard items to be used for teaching performance

evaluation and will allow for narrative comments from students. Confidentiality of student input

will be maintained as no identifiable information about individual students will be part of the

survey instrument. The Student Evaluation of Instruction Policy is included in the UNF Policies and

Procedures attachment.

Section VI.Capacity to Deliver

6.1. Facilities

It is anticipated that facilities for the initial years of operation will be leased space suitable for

instructional purposes with the ability to expand as needed. We have done a preliminary

assessment of the commercial real estate market in Niagara Falls and are confident that suitable

space can be available.

As the institution grows, it is expected that a purpose-built campus will be ready for occupancy in

year 6 of operations and the University has already commenced discussions with developers and

the city of Niagara Falls to explore development opportunities. NFU’s sister institution, University

Canada West, provides an excellent example of the quality of campus planned with their new

Vancouver House Campus, which can be viewed here: https://youtu.be/GF-pCqm-MB8

. Both the

initial facility and eventual permanent campus will offer students with access to computer labs,

group meeting space, quiet study space and wifi throughout.

To maximize space utilization, instructional hours for on-campus study will be set as 8:00 am –

9:00 pm, Monday – Thursday and 8:00 am – 5:00 pm Friday. Hybrid delivery of Master’s programs

will see up to 50% of total graduate enrollments delivered in this format, which combines periods

of online study with weekend instruction on-campus– utilizing instructional facilities during

periods that are traditional idle.

6.2. Library and Learning Resources

In addition to the general resources outlined below, UNF will provide the following learning

resources to students for this program:

a) Office 365 Education

b) Coursera for Campus License

c) Power BI Premium Academic Plan

d) SAS University Edition

e) Tableau for Students, Tableau eLearning

f) Salesforce Academic Alliance

g) SAP University Alliance

The UNF Library will support students’ success in their academic careers. The library facilities will

include a computer lab, individual and group study spaces, as well as printing and scanning tools.

The library will provide students and faculty with high-quality resources and services, including:

• Library Reference Services (in-person, phone, email, and instant messenger).

• Research Assistance.

• Workshops and training sessions on APA citation style, plagiarism, research skills and library

orientation.

In line with the digitally-focussed mission of the university, the Library will focus on developing its

collection primarily electronically with a small number of physical stacks on site. This will allow

students and faculty to have 24/7 access to all online library resources via the Library portal, which

will connect users to a collection estimated

to include:

• Over 380,000 e-books.

• Over 60,000 full text scholarly journals.

• Thousands of magazines, periodicals and trade publications.

• Close to 25,000 business case studies.

• Harvard Business Publishing Student Success Package containing 2,200 case studies and the

Harvard Business Core Curriculum including 76 readings covering a range of essential business

topics, including entrepreneurship, finance, accounting, marketing, operations management,

organisational behaviour and strategy.

• Other resources including company profiles, industry and market research reports, economic

country reports plus SWOT analyses.

6.3. Student Services

The Student Services Department will be responsible for providing a suite of services to students

including:

• New student orientation – 4-day welcome program;

• Student Peer Leaders program to support new students;

• Native language speaking international student advising;

• Mental health support through student health insurance program;

• Alumni Program;

• Career Development Centre;

o Arrange Career Fairs, regular webinars, guest speakers and networking events;

o Job Board for students;

o Work Integrate Learning development and support;

o Implement and manage micro-credentials/badging program for student career

development;

• Academic Advising;

o Create Program Completion Plans;

o Monitoring of Academic Standing and helps students who are not in good standing

with plans and supports to return student to good standing

o Provide workshops and individual student support;

• Student Life

o Social events;

o Student Society and Club support;

o Recreational opportunities.

• Math and Writing support

For students who do not meet the minimum English requirements for admission, we will deliver a

University Access Program (UAP) fully accredited by Languages Canada. The program will focus on

academic English, ensuring that students will be able to study and complete assignments with

Based on University Canada West collection

confidence. UAP will also cover specific academic skills, such as essay writing, research methods,

note taking, critical thinking and seminar and tutorial discussion.

6.5. Faculty Qualifications

In addition to the information provided below, the Master of Science Data Analytics requires the

following faculty qualifications:

• Data science is an applied discipline that integrates knowledge from math, computer science,

and business management. As such, an effective instructional team for the MSDA program

brings together experts with deep and diverse analytics work experience with appropriate

academic and professional qualifications. In general, faculty qualifications fall into four

categories:

a) Math/AI focused – for DA500, DA510, DA600, DA699, PhD in Statistics, Artificial

Intelligence, or Operations Research, and 5+ years of analytics experience.

b) Computer Science/ IT focused – for CS500, CS510, CS600, CS610, CS620, PhD/MS in

Computer Science, Information Technology, or Data Analytics, plus 5+ years of IT and

analytics experience. Prefer certified in SAS, Power BI, Tableau, Python, SQL DBMS, MS

Excel at an advanced level.

c) Business Intelligence focused – for BI500, BI510, BI621, BI622, BI623, DA699, PhD or DBA

in Marketing, Management Science, or MIS, plus 5+ years relevant experience, preferably

in consulting.

d) Case Study Facilitators – for DA501, DA511, DA611, MBA or MS in Math/Computer

Science/Data Science, plus 5+ years of analytics consulting experience. Prefer professional

certifications in SAS, Power BI, Tableau, Python, SQL DBMS, MS Excel, etc.

Academic Council determines and recommends to the President and Board of Governors the

standards for qualifications for faculty members.

All faculty candidates should:

• Have completed, or be near completion of a doctoral or terminal degree in a discipline related

to the discipline area taught with evidence of related scholarly work; or

• Hold a master’s degree related to the discipline with extensive related professional/ business

experience; or,

• Hold both doctoral and master’s degrees with post-secondary teaching experience in the field.

Professional and/or business experience is an asset for all faculty positions. Exceptions to these

requirements may be approved by the Vice President, Academic. The Hiring and Appointment of

Faculty Policy, included in the UNF Policies and Procedures attachment, outlines the hiring process

and requirements including the role of Human Resources to verify the academic credentials of all

faculty hired and of the Selection Committee to perform due diligence with respect to the

academic credibility of the credential granting institution for all qualifications claimed by faculty

members.

At the graduate level, a minimum of 80% of the program will be taught by faculty holding a

terminal qualification (usually doctoral). Members of supervising and examination committees will

all hold terminal qualifications and records of on-going scholarly activity.

6.6. Research

Full time faculty will be required to engage in research and scholarly activity, recognizing that UNF

will be primarily a teaching university. The University also recognizes that research and scholarly

contributions will vary from one faculty member to another, depending upon qualifications, field

of research, emphasis on teaching responsibilities, and stage of academic career. The range of

scholarly activities at the University includes discipline-specific research; research and scholarship

related to business or professional practice; and research and scholarship related to

improvements in university teaching and learning.

Annual plans for scholarly activity will be jointly discussed between each faculty member and the

Program Chair. An amount of funding for professional development will be allocated to each

faculty member, and they may also apply for additional funding. A small bonus will be granted to

faculty for each publication in scholarly journals.

6.7. Faculty policies

Policies and Procedures related to Hiring and Appointment of Faculty, Intellectual Property,

Professional Development, Research Ethics, Scholarly Activity for Faculty are included in the UNF

Policies and Procedures attachment.

Section VII. Credential Recognition Standard

According to World Economic Forum’s Future of Jobs Survey 2020,

the top ten emerging jobs in

Canada include Data Analysts and Scientists, AI and Machine Learning Specialists, Big Data

Specialists, Digital Marketing and Strategy Specialists, and Process Automation Specialists.

The Government of Canada expects a labour shortage for Database Analysts (NOC2171) through

2029. It also reports good job prospects for data analysts in all regions of Ontario, except the

North.

Two key factors driving the job growth are the move into cloud computing and big data by

industries such as finance, e-commerce retail, and telecommunications. Further growth comes

from the growing autonomous vehicle industry, an increasing number of financial technology

(FinTech) start-up companies, and other dominant technological trends related to artificial

intelligence, block chain infrastructure, and virtual and augmented reality.

UNF performed a detailed job market requirements analysis to support the planning of the MSDA

curriculum. By analyzing a randomly selected sample of 50 actual (entry to mid-level) data analyst

job listings on indeed.ca,

we ensure that the curriculum can deliver the outcomes most

employers require. Please see Appendix B for the Market Research Report.

Industry consultations were conducted during the development of this program to ensure that the

curriculum and learning outcomes were aligned with the needs of employers. Please see

Appendix D for letters of endorsement from consulted organizations.

In order to validate graduate outcomes for the purpose of ongoing program review, UNF will

conduct graduate surveys that mirror the Ministry of Colleges and Universities’ Ontario University

Graduate Survey. The survey will be administered to all graduates two years after graduation and

will collect information on their employment outcomes at six months and two years after

graduation.

As a professionally oriented Master’s degree, it is expected that most graduates of this program

will go on to careers in the field; however, this degree was designed to align with the Ontario

Qualifications Framework and follows established standards in Canadian higher education. As

such, this degree will be recognized for the purposes of further study.

https://www.weforum.org/reports/the-future-of-jobs-report-2020

https://www.weforum.org/reports/the-future-of-jobs-report-2020/in-full/country-and-industry-profiles

https://www.jobbank.gc.ca/marketreport/summary-occupation/17882/ON

https://ca.indeed.com/data-analyst-jobs

Section VIII. Regulation and Accreditation

This program does not lead to occupations that are subject to government regulations and is

not subject to any requirements by a regulatory and/or accrediting body.

Section IX. Nomenclature

The Degree awarded will be a Master of Science Data Analytics. This degree is named in keeping

with standard practices in Canadian higher education and is in alignment with the Degree Level

Standards in Ontario, as articulated in the PEQAB Manual for Private Organizations 2020 and the

Ontario Qualifications Framework. This degree was chosen in keeping with UNF’s mission to

prepare graduates to be leaders in a digital world by embracing a digital mindset which is a

mindset that encompasses both digital fluency and a growth mindset.

The Master of Science designation conveys that this is a graduate degree in science focussed on

data analytics. As a professionally oriented Master’s degree, this program requires students to

demonstrate research, analytical, interpretive, methodological, and expository skills through

problem-based learning, small integrative projects, and a capstone project. Students will

demonstrate their knowledge and skill development during an Internship. Graduates of this

program will have a portfolio of work which they can show to prospective employers or for

admission to doctoral studies.

The MSc Data Analytics: Marketing Analytics and MSc Data Analytics: Operations Analytics convey

the major areas of specialization available to students within this degree. After extensive research

and analysis of the current job market in this field we found that:

• A "General Data Analyst" can find work across industries. Many employers are consulting

companies and have projects from different clients in different industries. The generalist

can work with a broad range of tools and platforms used by different clients.

• A "Marketing Data Analyst" can solve more complex marketing problems like CRM

marketing, Pricing, Product management problems.

• An "Operations Data Analyst" can solve complex logistics, quality control, project

management problems.

Thus, these three types of Data Analysts cover almost all the jobs currently available and therefore

this program was structured accordingly.

Section X. Internal Quality Assurance and Development

10.1 Program Review Policy

All University of Niagara Falls Canada programs must undergo a multi-stakeholder program

review, including internal and external reviews, in five-year cycles. Regular program review

ensures that all UNF programs are current, relevant, meet the quality assurance standards and

requirements of the Ministry of Colleges and Universities and/or relevant accreditation bodies,

and identifies opportunities for program improvements and further development. The program

review process will include a Self Study and an External Review. The Program Review Policy and

the Program Review Procedure are included in the UNF Policies and Procedures attachment.

In additional to formal program review, each program will be reviewed annually for

appropriateness of materials, assignments, readings etc. and course outlines will be updated.

Each individual course will also be evaluated by students as outlined in the Student Evaluation of

Instruction Policy included in the UNF Policies and Procedures attachment.

The objective of these reviews is to foster improvement, maintain relevance, support the mission,

vision and goals of the University, and ensure that institutional learning outcomes and degree

level standards are being met. Suitable timelines, handbooks and project support will be supplied

by the Office of the VP Academic.

10.2 Procedures for the Self-Study

The Vice-President Academic (VPA) initiates program reviews and, in consultation with the

appropriate Program Chair, identifies timeframes and resource requirements for the completion

of the self-study and external review. Program reviews will be anticipated and budgeted for, with

costs expensed to the appropriate budget centre.

The Program Chair responsible for the program(s) under review recommends the appropriate

standards and criteria for review (based on standards set by the Degree Quality Assessment Board

and/or other relevant accreditation bodies). The designated Chair assembles a Self-Study

Committee comprised of a combination of four to seven (4-7) full-time and sessional faculty who

teach within the program(s) under review.

The Self-Study Committee establishes the criteria, processes and timelines for completion of the

Self- Study Report; formulates a Self-Study Report outline, a list of questions to be addressed, and

identifies the data and resources required to complete the self-study. Criteria for assessment must

be aligned with PEQAB guidelines, or quality assurance and compliance standards as well as

standards for any accreditation body for which the University has received, or is applying for,

accreditation. The Self-Study Committee develops a work plan specifying collective and individual

duties and delivery dates.

The Self-Study Committee secures data from staff and appropriate departments to support the

self- study analysis and reporting requirements. The Program Chair uses appropriate methods for

gathering and analyzing input from the relevant faculty members, staff, students and the Program

Advisory Committee (PAC).

A draft of the Self-Study Report with initial key findings is reviewed by the VPA prior to the writing

of the final report providing an opportunity for review and clarification of the findings and

recommendations.

The Self-Study Committee produces a comprehensive and clearly written Self-Study Report

identifying program strengths, areas for short and longer-term improvements, and opportunities

for new course or program development. The Self-Study Committee submits the final Report to

the VPA who commissions qualified external reviewers to assess and report on program

operations and deliverables. The Self-Study Committee may recommend appropriate reviewers to

be considered for appointment by the VPA. The VPA or Designate provides the Self-Study Report

to the external reviewers, assists the reviewers to plan and coordinate site visits and access to

information required by the reviewers.

10.3 Procedures for the External Review

The VPA or Designate appoints an External Review Panel comprised of three to five (3-5) qualified

individuals (depending on the size of the program). External reviewers will be reimbursed for

expenses (travel, accommodation, meals, honoraria).

The primary focus of the External Program Review Panel is on academic quality, curriculum and

program learning outcomes. The External Program Review Panel considers the Self-Study Report

for the program(s) and any documentation regarding University policies, procedures, the

University Academic Calendar and website, detailed course outlines, online courses, and data on

student and faculty performance. The Chair of the External Program Review Panel prepares an

agenda for the panel’s site visit. The agenda is reviewed by the VPA or Designate and the Review

Panel Chair to ensure availability of participants to meet with the External Program Review Panel.

The External Program Review Panel provides preliminary feedback to the VPA and the Chair at the

conclusion of their site visit. External reviewers compile a draft report identifying program

strengths and areas for further development. The draft report is forwarded to the VPA with a copy

to the Chair, within fifteen (15) working days of site visit. The VPA, in consultation with the

Chair(s), the Self-Study Committee, the President, the Registrar or other senior leadership staff

(where applicable) has fifteen (15) working days in which to respond in writing to the External

Program Reviewer Panel accepting the report, correcting elements noted therein, or questioning

or disputing findings. The External Program Review Panel reviews the feedback from the

University and completes the final version submitting the report to the VPA.

10.4 Final Report

The VPA presents the Report of the External Program Review Panel to Academic Council normally

at the meeting immediately following receipt of the Final Report. The VPA includes a formal

response to the Report from the External Program Review Panel outlining action plans to address

recommendations made by the Self-Study Committee and the External Program Review Panel.

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