certificate in data analytics proposal appd by senate 2014

7/26/2019 Certificate in Data Analytics Proposal Appd by Senate 2014

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Proposed

Certificate in

Data Analytics, Big Data & Predictive Analytics

Submitted by:

Dr. Sri Krishnan, Dean, Faculty of Architectural Science (Dean of Record)

in collaboration with

Dr. Imogen Coe, Dean, Faculty of Science

The Faculty of Science,

and

Dr. Marie Bountrogianni, Dean

The G. Raymond Chang School of Continuing Education

November 13, 2013


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Certificate in Data Analytics, Bid Data & Predictive Analytics ii

TABLE OF CONTENTS

Acknowledgements ......................................................................................................... iv

Executive Summary ........................................................................................................ 1

1

Introduction to the Certificate ................................................................................... 6

1.1 Curriculum Rationale .......................................................................................... 6

1.2 Co-Academic Coordinators .............................................................................. 10

1.3

The Goals of the Certificate.............................................................................. 10

1.4

Degree Credit ................................................................................................... 11

1.5

Admissions Policy ............................................................................................ 11

1.6

Development Plan ............................................................................................ 12

1.7

Comparator Programs ...................................................................................... 14

1.8

Academic Home Unit ....................................................................................... 19

1.9

Curriculum Structure and Professional Competencies ..................................... 19

1.10

Proposed Program Courses ......................................................................... 21

1.11 Professional Development Award ................................................................. 24

1.12

Target Participants and Job Opportunities .................................................... 24

2 Rationale ................................................................................................................ 25

2.1 Relevance to Ryersons Goals......................................................................... 26

3

Course Descriptions and Learning Outcomes ........................................................ 27

3.1 The Certificates Learning & Competency Outcomes....................................... 27

4

Course Outline Prospecti ....................................................................................... 31

4.1

Introduction to Big Data Analytics .................................................................... 31

4.2

Data Access and Management ........................................................................ 32

4.3

Data Analytics: Basic Methods ......................................................................... 33


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Certificate in Data Analytics, Bid Data & Predictive Analytics iii

4.4

Data Analytics: Advanced Methods ................................................................. 34

4.5

Big Data Analytics Tools .................................................................................. 35

5

Certificate Management ......................................................................................... 39

5.1

Academic Management Structure .................................................................... 39

5.2 Academic Coordinators of the Certificate ......................................................... 40

5.3 Certificate Delivery ........................................................................................... 41

5.4

Academic Governance ..................................................................................... 41

5.5

Student Advising .............................................................................................. 41

5.6

Registration and Graduation Requirements ..................................................... 41

6

Societal Need ......................................................................................................... 42

7

Ryerson Library Resources .................................................................................... 44

7.1

Collections........................................................................................................ 44

7.2

Interlibrary Loans ............................................................................................. 44

7.3

In Person Services ........................................................................................... 44

7.4 Online Services ................................................................................................ 45

7.5 Drop-in Workshops .......................................................................................... 45

7.6 Liaison with The Chang School ........................................................................ 45

8 Financial Viability ................................................................................................... 46

9

Conclusion ............................................................................................................. 47

Appendices

Appendix 1 Academic Coordinator job description

Appendix 2 Letters of SupportAppendix 3 Certificate Curriculum Committee ParticipantsAppendix 4 Membership of the Program Advisory CouncilAppendix 5 Agenda and Meeting Notes of the Curriculum Committee/Program

Advisory Council


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Certificate in Data Analytics, Bid Data & Predictive Analytics iv

Acknowledgements

Since its inception in the Summer of 2013, this Certificate proposal has benefited fromthe thoughtful input and creative collaboration of staff and faculty from various bodieswithin Ryerson University. We would like to acknowledge the contributions of the

following individuals:

DeansSri Krishnan (Dean of Record)Imogen CoeMarie Bountrogianni

Previous Chang School DeanGervan Fearon

Faculty Liaison and Academic LeadAlex Ferworn

Certificate Co-Academic CoordinatorsAyse BenerAlex Ferworn

Standing Curriculum Committee Co-Chairs for the CertificateAyse BenerAlex Ferworn

Program Director of Engineering, Architecture & Science at The Chang SchoolAnne-Marie Brinsmead (Chang School Lead)

Certificate Curriculum CommitteeKimberly Bates

Ayse BenerAnne-Marie Brinsmead ex-officio memberDejan DelicLiping FangXavier Fernando

Alex FerwornJacob FriedmanMurtaza HaiderPawel Pralat

Alireza SadeghianMarcos SantosKhaled M. SennahIssac Woungang


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Certificate in Data Analytics, Bid Data & Predictive Analytics v

The G. Raymond Chang School ofContinuing EducationFred AngerManny Aranas

Anne-Marie Brinsmead (Chang School Lead)

Des GlynnShannon KoumpholDijana PraskacJuliya Vasyliv

Program Advisory CouncilAnthony Bonato, Associate Dean, Students and Programs, YSGSStephen Perelgut, IBM University LiaisonShaohua Zhang, Manager, R&D, Big Data, Blackberry

Atif Ahmad, CEO Wind Mobile


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Certificate in Data Analytics, Bid Data & Predictive Analytics Page | 1

Executive SummaryCertificate GoalsThe focus of the proposed Certificate program is to deliver relevant, timely and effectiveeducation in the areas of Data Analytics Foundation, Basic and Advanced Analytics

Methods and Big Data Analytics Tools, including a Capstone course with a final project,where theory, methods, techniques and processes shall be applied and used inpractice. When the curriculum is delivered, the program will provide professionals thatis, the certificate registrantsthe opportunity to complete course assignments thatclosely address their identified professional needs and career goals.

Certificate Structure and Learning OutcomesCurriculum is designed to meet the requirements of INFORMS Certified AnalyticsProfessional (CAP) program. CAP requires proficiency and skills in the followingseven domains:

1. Business Problem Framing2. Analytics Problem Framing3. Data4. Methodology5. Model Building6. Deployment7. Model Life Cycle Management

This Certificate requires the completion of six courses, including one Capstone coursethat cover the aforementioned seven knowledge domains (Figure 1).


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Figure 1. Curriculum and Competency Mapping

Development PlanAt this juncture, there is consensus that a comprehensive, staged deployment of theCertificate will most effectively meet the educational needs, as well as the careeradvancement aspirations, of individuals at different stages and levels of responsibilityalong their career paths, while keeping their emerging interests firmly in mind. Thecurriculum discussed in this proposed certificate will be developed with the followingroll-out planned.

First and Second coursesFall 2014in-class Third courseWinter 2015in-class Fourth and Fifth coursesSpring 2015in-class plus Capstone course (with Final Project)Fall 2015

The choice of Capstone course final project will be conducted in consultation with a

faculty advisor.

Societal Need and Target GroupThe focus of the certificate program is to deliver pertinent, practical, timely and effectiveeducation in the areas of Data Analytics, Big Data and Predictive Analytics. Each ofthese domains is widely recognized as having significant and growing societalimportance: with respect to organizational performance in R&D, products and services;with respect to communications to clients and customers; with respect to commerce,


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finance, research, public utility, law enforcement, government institutions andinfrastructure. Big Data implementation and analytics, predictive analytics methods andmodels, predictive analytics platforms and qualified professionals knowledgeableharnessing them are in high demand from private and public sector organizations,including from the scientific, technology, legal, social and business perspectives.

New research1by the McKinsey Global Institute projects that there will be a 50 to 60percent gap between supply and demand of people with data analytical competencies.These professionals will need advanced education in data analytics, statistics andpredictive analytics as well as the ability to analyze large data sets (Big Data). Thestudy projects that there will be approximately between 425,000 and 475,000 unfilleddata analytics positions in North America (Canada and the US) by 2018 and a shortageof 1.5 million managers (and those data analysts that report to them) who have theability to harness the analysis of data provided to them and to make decisions usingdata analysis, including Big Data analytics. The report estimates that a steady stream ofanalytic talent will be required in all industries, as companies use data, databases and

big data and predictive analytics platforms as a means of competitive advantage.

Admissions RequirementsThis program is open to adults within a range of academic and/or professionalbackgrounds, subject in some instances to the approval of the certificates academiccoordinator(s)

Certificate applicants are to hold:

i) An OSSD with six Grade 12 U or M credits (with a minimum grade point average of 70percent), including:

a Grade 12 U course in English; a Grade 12 U course in Advanced Functions; A 12 U course in Calculus and Vectors OR a 12 U course in Mathematics of Data

Management, AND one (1) of EITHER: A Grade 12 U course in Physics; OR a Grade 12 U course in Chemistry; OR a

Grade 12 U course in Biology;

ii) OR, equivalent academic status, for example: Sufficient University degree coursework (obtained within the last 10 years) in

mathematics, computer science, science, engineering, or business with a

minimum cumulative GPA of 1.67.or

1http://www.mckinsey.com/NotFound.aspx?item=%2fmgi%2fpublications%2fbig_data%2fpdfs%2fmgi_big_data_exec_summary&us

er=extranet%5cAnonymous&site=website,accessed July 2013.
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A three-year college diploma (obtained within the last 10 years) in mathematicscomputer science, science, business with a minimum 3.0/B/70% cumulativeGPA.

or

A relevant or related certificate in the field of data analytics.

OR

iii) Mature Student Status:For Mature Student Status, the Certificate applicants are to have other relevantacademic qualifications or relevant professional experience (to be assessed/evaluatedby Co-Academic Coordinator Professor Ayse Bener in consultation with the applicant):

Four years of relevant professional experience.

Note: For applicants who fall under the Mature Student Status category:To see if you qualify, please contact Professor Ayse Bener, Co-Academic Coordinator,[email protected] for a one-one-one consultation, or attend a Program OpenHouse.

Undergraduate students wishing to pursue a continuing education certificate programshould be aware of possible restrictions; please refer to the Curriculum Advising websiteat www.ryerson.ca/currentstudents/curriculum advising for complete details.

Academic Management and GovernanceThe Steering Curriculum Committee members for the proposed certificate made the

decision with Departmental Chairs present (Mathematics, Computer Science andMechanical and Industrial Engineering) that the Academic Home for the proposedcertificate will be the Department of Mechanical and Industrial Engineering in theFaculty of Engineering and Architecture Science.

The Dean of Record will be the Dean of the Faculty of Engineering and ArchitecturalScience.

The Computer Science and Mathematics Departments will serve as teachingdepartments and will contribute courses to the certificate. Along with faculty from the

Academic Home department of Mechanical and Industrial Engineering, faculty from the

Mathematics Department and the Computer Science Department (as well as facultyfrom other academic departments and Faculties) will be members of the StandingCurriculum Committee for the certificate. The Standing Curriculum Committee shallhave a majority of faculty members (RFA).

With the permission of the respective Departmental Chairs, faculty from the Departmentof Computer Science and faculty from the Department of Mathematics will be invited toteach courses (as faculty overload) in the certificate. The Co-Academic Coordinators
mailto:[email protected]:[email protected]


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will be one (1) faculty member from the Department of Mechanical and IndustrialEngineering and (1) faculty member from the Department of Computer Science.


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Mostcompanies today collect hundreds of trillions of characters of data. This data isused in many industries, from businesses to governments, from finance to R&D, from

product and service performance to marketing and advertising [...] getting full value fromthe data requires not only the ability to use the tools of data management, data analysisand predictive analytics, but, at least as important, the ability to frame the right

questions, understand the domain of use and clearly articulate the findings for thepurposes of sound decision making.

-David Belanger, Chief Scientist of AT&T Labs

1 Introduction to the CertificateData Analytics, Big Data and Predictive Analytics can be leveraged to seek out insightsinto an organizations performance, stakeholders, products, services, channels,solutions, future directions, initiatives and innovations. Additionally, Data Analytics, BigData and Predictive Analytics and the insights they provide need to be applied anddeployed strategically to support making better decisions within an organization, be it in

the private, public, para-public or non-profit sectors.

The proposed name for this Certificate is appropriate and consistent with current usagein the area of study of Data Analytics, Big Data and Predictive Analytics. (Please seethe section on Comparator Programs.)

1.1 Curriculum Rationale

The increased flow of digital information, characterized by high volume and variety,provides opportunities for transforming this data (Big Data) into business intelligenceand is resulting in a growing demand for data analytics expertise within all sectors andacross a variety of business domains. According to Gartner2, Big Data is the

information of extreme size, of diversity and of complexity.

Data scientistwas named the sexiest job of the 21stcentury3by Harvard BusinessReview magazine, describing data scientist as a high-ranking professional with thetraining and curiosity to make discoveries in the world of big data. Numerouspublications examined in this paper suggest both a labour and a skills shortage withinthe data analytics field. However, it also appears that technical competencies alone willnot fully address the particular skills-mix shortage.

A combination of business acumen and of statistical/mathematical competencies seemsto be the most demand by the marketplace. The proposed certificate programs

positioning may have greater impact with the inclusion of strategic and/or businesselements (e.g., business analysis is a growing field).

2Accessed on 26 September 2013 at:http://www.gartner.com/technology/topics/big-data.jsp

3Harvard Business Review magazine (October 2012). Data Scientist: The Sexiest Job of the 21st Century. Accessed on 18

September 2013 at:http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/
http://www.gartner.com/technology/topics/big-data.jsphttp://www.gartner.com/technology/topics/big-data.jsphttp://www.gartner.com/technology/topics/big-data.jsphttp://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/http://www.gartner.com/technology/topics/big-data.jsp


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Crucially, the proposed curriculum is designed to meet the requirements of the CertifiedAnalytics Professional (CAP) program recently launched by INFORMS in 20124. TheCAPdomains of practice that adhere to the analytics end-to-end process are:business problem framing, analytics problem framing, data, methodology selection,model building, deployment, and model life-cycle management.

Program registrants will learn how analytics can help to improve decisions throughoutan organizations value chain,to understand the different forms of analytics (descriptive,predictive and prescriptive), and to develop a good understanding of the methods usedin each, and to acquire hands-on experience with analytics tools that are widely used inpractice.The proposed program is a unique offering in the GTA, competing only withonline programs at selected U.S. institutions, Centennial College and, to some extent,with a certificate program at the University of Toronto School of Continuing Studies.

If the maximum benefit to Canadas economy is to be obtained from Data Analytics, BigData and Predictive Analytics, Canada will need skilled data analysts with Data

Analytics, Big Data and Predictive Analytics applied expertise, including proficiency inBig Data implementation and open source data. In anticipation, this proposed certificateprogram meets this identified market need. Literally tens of thousands of individualshave been hired globally by organizations mandated by statute, or otherwise recognizedand credible, in order to apply the technical and analytical skills required to run and tomaintain information technology systems and databases.

However, only a small subset of these professionals have also been trained in thecreation, management, integration and organization/delivery of data analytics methodsand processes applied to databases, Big Data implementation and predictive platformswithin the enterprise. Even fewer IT-educated professionals have the knowledge,competencies and skill sets that inspire credibly-based yet assured and confident beliefin the integrity, authenticity, accuracy and completeness of the Data Analytics, Big Dataand Predictive Analytics being produced as outputs of processed information.

Enter Ryerson trained Data Analytics, Big Data and Predictive Analytics specialists anddata analystswell trained, mature, multi-disciplinary in approach and versed not onlyin technicalities, but in the organizations identified needs and those organizations data,databases and processes. To which can be added, skills in Big Data analytics and inimplementation.

Moreover, this well-rounded professional becomes a critical component, not only inaddressing pressing needs for predictive analysis and findings, but in re-establishing the

broadly-based trust that ultimately fuels so much of the economys and governmentsperformance and productivity.

4https://www.informs.org/Certification-Continuing-Ed/Analytics-Certification/Candidate-Handbook#1b
https://www.informs.org/Certification-Continuing-Ed/Analytics-Certification/Candidate-Handbook#1bhttps://www.informs.org/Certification-Continuing-Ed/Analytics-Certification/Candidate-Handbook#1bhttps://www.informs.org/Certification-Continuing-Ed/Analytics-Certification/Candidate-Handbook#1bhttps://www.informs.org/Certification-Continuing-Ed/Analytics-Certification/Candidate-Handbook#1b


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The certificates graduands will contribute professionally to the maximization oforganizational value to be extracted from Data Analytics, Big Data and Predictive

Analytics outputs and best practices, the dynamics and exigencies of the necessary R& D and performance management, the products and services relevance and efficacyand the inter-functional communication with internal and external customers, thus

ultimately augmenting productivity and contributing to Canadas economic prosperity.

With no other universities in the GTA offering a professional continuing educationcredential at this advanced level, Ryerson acquires a first-mover competitive edge inthis emerging market space.

The following Faculty members have been identified with the academic background andexpertise to provide the necessary guidance and leadership to make the proposedcertificate program successful.

Faculty MemberHome Unit and

Graduate Programs

Relevant Area(s) of Expertise

Prof. Dejan Delic Mathematics Modelling and SearchingComplex Networks in the BigData Era

Prof. Pralat Pawel Mathematics Modelling and SearchingComplex Networks in the BigData Era

Prof. Ayse Bener Mechanical andIndustrial Engineering

Big Data applications to tacklethe problem of decision makingunder uncertainty using: machinelearning methods to build

recommender systems andpredictive models; cognitivescience to model humanbehaviour; and game theoreticmodels to determine strategies insoftware analytics, healthsciences, and green analytics/smart energy.

Prof. Mohamad Jaber Mechanical andIndustrial Engineering

Modeling organizational, groupand individual processesperformance

Supply/chains, Inventorymanagement in reverse logisticsProf. Alexander Ferworn Computer Science Computational Algorithms and

Models, Computer Security,Survivable Networks

Prof. Liping Fang Mechanical andIndustrial EnFgineering

Risk Analysis, Decision TreeAnalysis, Reliability Engineering,and Decision Support Systems


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Prof. Xavier Fernando Electrical and ComputerEngineering

Computer communications,analytics and networking.

Prof. Alireza Sadeghian

Computer Science Intelligent systems, Big Data,data verification, compromiseddata issues.

Prof. Khaled Sennah Civil Engineering Numerical & ExperimentalSimulationsProf. Isaac Woungang

Computer Science Computer Security and DigitalForensics, Network survivability,Data networks.

Prof. Marcos Santos Computer Science Application of evolutionarycomputing systems

Prof. Cherie Ding Computer Science Web information retrieval, webanalytics and web usage mining

Prof. Ali Miri Computer Science Secure Data De-duplicationFramework for Cloud

EnvironmentsProf. Abdolreza Abhari Computer Science Web 2.0 Social Networking, Web

Mining and Info Retrieval, DataMining and Database Systems

Prof. Sherareh Taghipour Mechanical andIndustrial Engineering

Data management, managerialdecision making

Prof. Mohamed Ismail Mechanical andIndustrial Engineering

Decision support systems

Prof. AhmadGhasempoor

Mechanical andIndustrial Engineering

Applications of ArtificialIntelligence

Prof. Farrokh Janabi-

Sharifi

Mechanical and

Industrial Engineering

Applications of Artificial

Intelligence in Robotics andBiomedical Engineering

Prof. Patrick Neumann Mechanical andIndustrial Engineering

Simulation and virtualperformance modeling

Prof. Seth Dworkin Mechanical andIndustrial Engineering

High performance parallelcomputing

Faculty who may teach in the program are highly qualified experts and leaders in theirfields of expertise. Ryerson Universitys is well-known for its uncompromising standardof academic excellence in the field of professional education.

1.1.1 Potential Faculty Resources

It is reasonable to assume that, over time, there are a number of faculty members whowill be able to support further curriculum development in this area than is currentlyproposed. In the following section, we identify the teaching departments who will becontributing the proposed certificate courses, while the academic home will be theDepartment of Mechanical and Industrial Engineering:


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The Department of Computer ScienceThe Department of MathematicsThe Department of Mechanical and Industrial Engineering

We look forward over time to adding curriculum from a wide variety of contributingsources and invite participation of additional faculty on the Standing CertificateCurriculum Committee, once the proposed certificate is approved and implemented.

1.2 Co-Academic Coordinators

The Co-Academic Coordinators for the Certificate will be Professor Ayse Bener of theDepartment of Mechanical and Industrial Engineering and Professor Alexander Ferwornof the Department of Computer Science. They also serve as Co-Chairs of theCurriculum Steering Committee for the proposed certificate. If-and-when the standingcurriculum committee for the certificate and the department council of the academichome department, (Mechanical and Industrial Engineering) place items regarding the

certificates curriculum on meeting agendas, Professor Ayse Bener will ask theDepartmental Chair to invite Alexander Ferworn to be present at those meetings.

When any of the courses teaching departments Computer Science, Mathematics andMechanical and Industrial Engineeringpresent at their departmental councils orcurriculum committee meetings agenda items that pertain to the courses that they areteaching in the certificate, both Co-Coordinators shall be invited by the relevantdepartment Chair to those meetings.

1.3The Goals of the Certificate

The focus of the proposed Certificate program is to deliver relevant, timely and effective

education in the areas of Data Analytics Foundation, Basic and Advanced AnalyticsMethods and Big Data Analytics Tools, including a Capstone course with a final project,where theory, methods, techniques and processes shall be applied and used inpractice. When the curriculum is delivered, the program will provide professionals thatis, the certificate registrantsthe opportunity to complete course assignments thatclosely address their identified professional needs and career goals.

The Certificate emphasizes the acquisition of knowledge and skill sets to leverage dataanalysis learned from the collective experiences of the organization in order for it to stayahead of the competitive game when it comes to performance optimization, toinnovation and to long-term sustainability.

This Certificate is a synthesis-focused program intended for working adults across anarray of professional expertise areas to give them the opportunity to develop and toacquire the functional knowledge and skills they need to succeed in the high-demandfield of Data Analytics, Big Data and Predictive Analytics.


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Crucially, the curriculum is designed to meet the requirements of INFORMS CertifiedAnalytics Professional (CAP) program. Upon successful completion of this certificate,the participants will be prepared to take INFORMS CAP examto become certifiedprofessionals.

1.4Degree CreditOnce the certificate is approved by Senate, relevant department councils will determinewhich of the certificate courses may be approved and submitted for degree credit status(either as Open Electives or as Professionally Related Electives). In addition, CourseExclusions and Course Restrictions, where appropriate, and where decided upon by therelevant teaching departments, will be determined. The Steering Curriculum Committeemembers for the Certificate defer to the teaching departments for making the decisionsas to which degree credit course designation will be conferred to those certificatecourses by the appropriate academic department, be it Mechanical and IndustrialEngineering, or Computer Science or Mathematics:

Introduction to Big Data Analytics (Teaching Department: Mechanical andIndustrial Engineering / Computer Science)

Data Access and Management (Teaching Department: Computer Science) Data Analytics: Basic Methods (Teaching Department: Mechanical & Industrial

Engineering) Data Analytics: Advanced Methods (Teaching Department: Mathematics) Big Data Analytics Tools (Teaching Department: Mechanical and Industrial

Engineering) Capstone Course: Emerging Best Practices in Data Analytics & Predictive

Analytics (Teaching Department: Mechanical and Industrial Engineering)

1.5Admissions PolicyThis program is open to adults within a range of academic and/or professionalbackgrounds, subject in some instances to the approval of the certificates academiccoordinator(s)

Certificate applicants are to hold:

i) An OSSD with six Grade 12 U or M credits (with a minimum grade point average of 70percent), including:

a Grade 12 U course in English; a Grade 12 U course in Advanced Functions; A 12 U course in Calculus and Vectors OR a 12 U course in Mathematics of Data

Management, AND one (1) of EITHER: A Grade 12 U course in Physics; OR a Grade 12 U course in Chemistry; OR a

Grade 12 U course in Biology;


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ii) OR, equivalent academic status, for example:

Sufficient University degree coursework (obtained within the last 10 years) inmathematics, computer science, science, engineering, or business with aminimum cumulative GPA of 1.67.

or A three-year college diploma (obtained within the last 10 years) in mathematics

computer science, science, business with a minimum 3.0/B/70% cumulativeGPA.

or A relevant or related certificate in the field of data analytics.

OR

iii) Mature Student Status:

For Mature Student Status, the Certificate applicants are to have other relevantacademic qualifications or relevant professional experience (to be assessed/evaluatedby Co-Academic Coordinator Professor Ayse Bener in consultation with the applicant):

Four years of relevant professional experience.

Note: For applicants who fall under the Mature Student Status category:

To see if you qualify, please contact Professor Ayse Bener, Co-Academic Coordinator,[email protected] for a one-one-one consultation, or attend a Program OpenHouse.

Undergraduate students wishing to pursue a continuing education certificate programshould be aware of possible restrictions; please refer to the Curriculum Advising websiteat www.ryerson.ca/currentstudents/curriculum advising for complete details.

1.6Development Plan

It is envisioned that development will continue around the concept of data collection,data analytics and data privacy into the future. In the next sections, we address both thenear and long-term development efforts related directly to the proposed Certificate.

1.6.1 Near-term Development

At this juncture, there is consensus that a comprehensive, staged deployment of theCertificate will most effectively meet the educational needs, as well as the careeradvancement aspirations, of individuals at different stages and levels of responsibilityalong their career paths, while keeping their emerging interests firmly in mind. Thecurriculum discussed in this proposed certificate will be developed with the followingroll-out planned.
mailto:[email protected]:[email protected]


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First and Second coursesFall 2014in-class Third courseWinter 2015in-class Fourth and Fifth coursesSpring 2015in-class plus Capstone course (with Final Project)Fall 2015

The choice of Capstone course final project will be conducted in consultation with afaculty advisor.

To ensure that both the course content, course delivery and Capstone course of thisCertificate respond to the needs of participants in this emerging field, high-levelpractitioners from the sector will be involved in supporting faculty in the development ofthe course content, and expert practitioners from the public, private and not-for-profitsectors will be involvedthrough guest lecturing and/or co-teaching with facultymembers in-classin the program. In addition to the applied experience suchpractitioners will bring to program delivery, their participation will provide valuablenetworking opportunities for program participants.

1.6.2 Longer-term Development

In the longer term, as curriculum becomes available and is added to the Certificateprogram, we will take advantage of the nexus of curriculum, research and students thatthis Certificate program will be able to provide.

In addition, consideration will be given by the Standing Curriculum Committee to anyconcepts not yet explored; for example there undoubtedly exists (or may be developed)curriculum in i) Web Analytics and ii) Creating an Analytical Workplace Culture.

The intention of all three departments contributing the certificate coursesMechanical

and Industrial Engineering, Computer Science and Mathematicsand approved by theSteering Curriculum Committee, is to offer these courses as Professionally RelatedElectives and/or Open Electives in the Computer Science, Mathematics and Mechanicaland Industrial Engineering Departments.

In addition, there is a prospective Masters in Data Science that is currently goingthrough a fiscal viability process at YSGS and at Ryerson University. YSGS, Mechanicaland Industrial Engineering and Computer Science and other academic departmentsacross the university are engaged in looking at next steps to move forward with thisinitiative.

The certificate aims to provide strong foundation in Analytics, Tools, and Statistics. Thecertificate holders will become analytics professionals or quasi-professionals. Theywould adhere to the highest standards of good analytics practice and they would be wellgrounded to follow a path of taking an advanced study (i.e. Masters program) tocontinue their professional development in analytics. Therefore,it is the intention of theSteering Curriculum Committee for this certificateand its faculty members whorepresent the three academic departmentsthat the six certificate courses and the


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certificate itself will provide an opportunity for selected graduands for the certificate toapply to, and, if successful, ladder into, the prospective Masters of Engineering in DataScience. This certificates curriculum has been designed to accomplish this objective.

1.7 Comparator Programs5

Several comparable programs were found in Canada (full list of comparative programs,including the sample of the U.S. institutions, is available in the table at the end of thissection):

University of Fraser Valley (Data Analysis post-degree certificate), UBC (Digital Analytics), U of T (Certificate in Management of Enterprise Data Analytics), Centennial College (Graduate Certificate in Marketing-Research and Analytics) University of Windsor (MA in Social Data Analysis).

The Data Analytics Certificate offered at the University of Fraser Valley has the mostresemblance to the proposed program; however, the courses are delivered in-classwhich limits the reach in the GTA market. Even though Digital Analytics at the UBC isentirely online, the focus is mainly web analytics and thus more narrowed than that ofthe proposed program. Within the GTA, the main comparative institution is the U of Twith the 3-course Certificate in Management of Enterprise Data Analytics whichincorporates technical and managerial focus. Another competing offer in Toronto is theGraduate Certificate in Marketing-Research and Analytics provided by CentennialCollege6.

The suggested curriculum appears largely technical not including business componentsrelated to decision-making, strategic perspective and critical thinking. A strong technical

focus may be viewed as a unique aspect of the proposed program, but it may alsopresent a lower competitiveness in relation to more holistic data analyticsprogramming.

The programs shown in the table are only university and college-level programs. Therewere other private providers such as Predictive Analytics online certificate7offered byPrediction Impact. This program is accessible by the Ryerson programs target market;however, it is not considered a direct competitor due to the program scope andacademic level.

5Most information from program websites and Vanguard Magazine Publication Emergency Management Education Showcase.

6Colleges in the GTA should be considered as competitors, based on The Chang School students consideration set. Findings from

The Chang Schools annual Student Surveys show that our students predominantly consider University of Toronto and the GTA

colleges when deciding where to enroll. Anecdotal evidence also shows that for some programs students consider colleges to be

more suitable. Therefore, discounting the role of colleges as our competitors may lead to incomplete information for decision-

making.

7http://www.predictionimpact.com/predictive-analytics-online-training.html
http://www.predictionimpact.com/predictive-analytics-online-training.htmlhttp://www.predictionimpact.com/predictive-analytics-online-training.htmlhttp://www.predictionimpact.com/predictive-analytics-online-training.htmlhttp://www.predictionimpact.com/predictive-analytics-online-training.html


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Institution Program Focus/Components Website

Universityof FraserValley

Dataanalysiscertificate

Data

AnalysisPost-Degreecertificate

Students will acquire the skills needed to extract reliable informationfrom large data sets. With carefully designed courses taken in bothcomputing and statistics, students will gain the data-base skills neededto house, extract, manipulate, and maintain data, and will learn thestatistical techniques needed to collect data correctly, assess its quality,

analyze it, and present the information effectively to decision makers.The industry standard statistical software environments SAS and R willbe used throughout.

Required courses: Introduction to Data Analysis and Statistical Modeling

Statistical Graphics and Languages Databases and Data Management Systems

Data Quality

5 from the following:

Applied Regression Analysis Design of Experiments

Survey Sampling Applied Generalized Linear Models and Survival Analysis

Time Series and Forecasting Applied Multivariate Statistical Analysis Data Mining

Advanced Database Topics Project Management

Fee: $425.01 per 3-credit course.

http://www.ufv.ca/cendar/2012_13/ProramsM-P/MATH_DATA.ht

Universityof BritishColumbia,Continuin

g Studies

DigitalAnalyticsAward inAchievement

This program is entirely online: 16 weeks online, 100 hours total,program fee is $2,840.

The award-winning program focuses on how to analyze and understand

the data generated by digital analytics reporting tools, to makesuggestions for improvements to the reports generated, and totransform the information contained in these reports into actions thecompany can take to improve website visitor centricity and profitability.Experience with a web analytics reporting tool is recommended, beforetaking the courses, as the curriculum does not address the "hands on"elements of working with a specific tool.

The program's content is provided by the Education Committee of theDigital Analytics Association (formerly the Web Analytics Association),consisting of industry professionals, and guided by experiencedadministrators of adult education programs.

Courses:Introduction to Web AnalyticsWeb Analytics for Site OptimizationMeasuring Marketing Campaigns OnlineCreating and Managing the Analytical Business Culture

http://cstudies.ubc.a/web-analytics-intelligence/award-of-achievement-in-

digital-analytics/index.htm?gclid=CPX2vLCE7kCFWNgMgodJjk_A
http://www.ufv.ca/calendar/2012_13/ProgramsM-P/MATH_DATA.htmhttp://www.ufv.ca/calendar/2012_13/ProgramsM-P/MATH_DATA.htmhttp://www.ufv.ca/calendar/2012_13/ProgramsM-P/MATH_DATA.htmhttp://www.ufv.ca/calendar/2012_13/ProgramsM-P/MATH_DATA.htmhttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://cstudies.ubc.ca/web-analytics-intelligence/award-of-achievement-in-digital-analytics/index.html?gclid=CPX2vLCE07kCFWNgMgodJjkA_Ahttp://www.ufv.ca/calendar/2012_13/ProgramsM-P/MATH_DATA.htmhttp://www.ufv.ca/calendar/2012_13/ProgramsM-P/MATH_DATA.htmhttp://www.ufv.ca/calendar/2012_13/ProgramsM-P/MATH_DATA.htmhttp://www.ufv.ca/calendar/2012_13/ProgramsM-P/MATH_DATA.htm


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UniversityofToronto,School ofContinuin

g Studies

Certificate inManagementof EnterpriseDataAnalytics (Big

Data)

Designed to address the growing need for data analysts, qualifiedanalytics managers and data scientists, each course in this ground-breaking program considers the technical andmanagerial/organizational aspects of enterprise data management andanalytics in parallel. It will challenge both business focused and

technically minded participants to broaden their horizons, adopt newways of thinking and embrace the promise of a smarter, better futureachievable through data analytics.

The certificate program is open to students with an Undergraduatedegree or college diploma in business, statistics or organizationaldynamics, change management, business processes engineering,computer science, mathematics, accounting or finance, plus a minimumof 3 years full time work experience.

Required courses:Foundations of Enterprise Data AnalyticsConcepts and ControlsValue Proposition and Technologies of Enterprise Data AnalyticsData Management from Enterprise Data Analytics to Data-Based

Decision MakingIn-class delivery, fee per non-credit course = $995 ($2985 for the wholeprogram)

http://learn.utorontoca/courses-programs/businessprofessionals/certifates/big-data-

management

Universityof Victoria,Continuing Studies

CertificateProgram inPopulationHealth DataAnalysis(PHDA)

This new, one-of-a-kind certificate will fill a gap in the present trainingneeds for those working in the field of epidemiology and statisticalanalysis, as well as researchers, policy makers, graduate students andfaculty members working with data analysis pertaining to populationhealth. The overall goal of the program is to provide you with strongfoundational knowledge and data

analysis skills to support your workwithin the health and social services sector.

This is entirely online program.

http://www.uvcs.uv.ca/population/

University

of Windsor

MA in Social

Data Analysis

The MA in Social Data Analysis (MASDA) is unique in Canada,

designed to prepare graduates for careers in data analysis, ingovernment, business or human services.

Both public and private sector organizations require skilled individualsto analyze information, draw conclusions and recommend practicalapplications.MASDA graduates will have:

A very strong background in methods and statistics; The ability to access, retrieve and analyze complex data; and Experience in writing reports in formats commonly used in

applied research settings.

http://www1.uwinds

r.ca/masda/
http://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-managementhttp://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-managementhttp://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-managementhttp://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-managementhttp://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-managementhttp://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-managementhttp://www.uvcs.uvic.ca/population/http://www.uvcs.uvic.ca/population/http://www1.uwindsor.ca/masda/http://www1.uwindsor.ca/masda/http://www1.uwindsor.ca/masda/http://www1.uwindsor.ca/masda/http://www.uvcs.uvic.ca/population/http://www.uvcs.uvic.ca/population/http://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-managementhttp://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-managementhttp://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-managementhttp://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-managementhttp://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-managementhttp://learn.utoronto.ca/courses-programs/business-professionals/certificates/big-data-management


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CentennialCollege

GraduateCertificate inMarketing-Researchand Analytics

Graduates of this program will learn different research methods andobtain the expertise necessary for creating effective surveys, statisticalanalysis, industry trends and consumer behaviour.

The two semester program consists of a mixture of lectures and labs

where students are exposed to SAS Enterprise Guide and SASEnterprise Miner. These are leading software solutions used in theResearch and Analytics fields.

Upon completion of the three specified SAS courses, students willreceive a certificate of recognition.

http://www.centennalcollege.ca/Programs/ProgramOvervw.aspx?Program=245

QueensUniversity,ExecutiveEducation

StrategicAnalytics

The program provides hands-on experience with the concepts, tools,and techniques that can help the organization to effectively implementstrategic objectives at all levels. Online databases and powerfulanalytical tools have made complex analysis both economically feasibleand timely.

The program is for Managers and executives who are involved in theirorganizations' strategic planning process.2-day session, fee: $1950+tax. Offered in Toronto in October 2013.

http://business.quensu.ca/Conversionocs/Execdev/shorterm_program/stratgic_analytics_progm.pdf
http://www.centennialcollege.ca/Programs/ProgramOverview.aspx?Program=2845http://www.centennialcollege.ca/Programs/ProgramOverview.aspx?Program=2845http://www.centennialcollege.ca/Programs/ProgramOverview.aspx?Program=2845http://www.centennialcollege.ca/Programs/ProgramOverview.aspx?Program=2845http://www.centennialcollege.ca/Programs/ProgramOverview.aspx?Program=2845http://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://business.queensu.ca/ConversionDocs/Execdev/short_term_program/strategic_analytics_program.pdfhttp://www.centennialcollege.ca/Programs/ProgramOverview.aspx?Program=2845http://www.centennialcollege.ca/Programs/ProgramOverview.aspx?Program=2845http://www.centennialcollege.ca/Programs/ProgramOverview.aspx?Program=2845http://www.centennialcollege.ca/Programs/ProgramOverview.aspx?Program=2845http://www.centennialcollege.ca/Programs/ProgramOverview.aspx?Program=2845


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US InstitutionsInstitution Program Focus/Components Website

ColoradoStateUniversityOnline Plus

DataAnalysisCertificate

This data analyst training program is designed for practitionerslooking to derive answers from raw data and "big data" using acomprehensive range of statistical analyses and methods. If you'reresponsible for organizing and analyzing complex data, regardless

of what industry you're in, you will benefit from the data analysistraining offered in our online program.

10-11 credits, $679 per credit

http://www.online.costate.edu/certificas/data-analysis/

New JerseyInstitute ofTechnology

GraduateCertificate inData Mining

Introduction to data mining with an emphasis on large-scaledatabases as a source of knowledge generation and competitiveadvantage. This certificate is designed for data analysts workingwith large organizations to design and use their data resources.Courses can be applied to a selection of Masters degrees.

4 Course (2 required, 2 electives) Online, can be completed in 1year.

Cost: $915 per credit (about $3000 for a 3 credit course)

http://adultlearner.nt.edu/programs/datmining-cert.php

StanfordCentre forProfessionalDevelopment,StanfordUniversity

MiningMassiveData SetsGraduateCertificate

Techniques and algorithms for extracting information from largedatasets such as the web, social-network graphs, and largedocument repositories. Target market: Software engineers,statisticians, predictive modelers, market research and analyticsprofessionals and data miners working with large amounts of rawdata.

Online, 4 required courses, can be completed in 1-2 years.

Cost: base price of about $3900 per 3 credit course

http://scpd.stanfordedu/public/categorcourseCategoryCeficateProfile.do?mehod=load&certificaId=10555807

University ofCalifornia SDExtension,San Diego

Data MiningCertificate

Designed to provide individuals in business and scientificcommunities with the skills necessary to design, build, verify andtest predictive data models. Target students are professionals inscientific and business communities.

5 courses (4 required, 1 elective), online. Cost: $625 per course

http://extension.ucs.edu/programs/inde.cfm?vaction=certdail&vcertificateid=18&vstudyareaid=14

We believe that for the certificate participants, one of the most attractive features of theproposed certificate is that the curriculum will be available in semester and in intensiveweeknight and weekend formats. Flexible delivery modes are one of the most attractivestrengths of Chang School programs according to the most recent survey of ChangSchool graduates (Fall 2012).

Another attractive feature of the proposed Ryerson certificate is the Capstone course

with a final capstone project to complete the Certificate. Flexible access over time tomodularized course curriculum content combined with progressive final course projectsthat attest to professional development and that may be placed in an individuals careerportfolio will be distinguishing features of this Certificate program.
http://www.online.colostate.edu/certificates/data-analysis/http://www.online.colostate.edu/certificates/data-analysis/http://www.online.colostate.edu/certificates/data-analysis/http://adultlearner.njit.edu/programs/datamining-cert.phphttp://adultlearner.njit.edu/programs/datamining-cert.phphttp://adultlearner.njit.edu/programs/datamining-cert.phphttp://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://extension.ucsd.edu/programs/index.cfm?vaction=certdetail&vcertificateid=128&vstudyareaid=14http://extension.ucsd.edu/programs/index.cfm?vaction=certdetail&vcertificateid=128&vstudyareaid=14http://extension.ucsd.edu/programs/index.cfm?vaction=certdetail&vcertificateid=128&vstudyareaid=14http://extension.ucsd.edu/programs/index.cfm?vaction=certdetail&vcertificateid=128&vstudyareaid=14http://extension.ucsd.edu/programs/index.cfm?vaction=certdetail&vcertificateid=128&vstudyareaid=14http://extension.ucsd.edu/programs/index.cfm?vaction=certdetail&vcertificateid=128&vstudyareaid=14http://extension.ucsd.edu/programs/index.cfm?vaction=certdetail&vcertificateid=128&vstudyareaid=14http://extension.ucsd.edu/programs/index.cfm?vaction=certdetail&vcertificateid=128&vstudyareaid=14http://extension.ucsd.edu/programs/index.cfm?vaction=certdetail&vcertificateid=128&vstudyareaid=14http://extension.ucsd.edu/programs/index.cfm?vaction=certdetail&vcertificateid=128&vstudyareaid=14http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=10555807http://adultlearner.njit.edu/programs/datamining-cert.phphttp://adultlearner.njit.edu/programs/datamining-cert.phphttp://adultlearner.njit.edu/programs/datamining-cert.phphttp://www.online.colostate.edu/certificates/data-analysis/http://www.online.colostate.edu/certificates/data-analysis/http://www.online.colostate.edu/certificates/data-analysis/


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The Faculty of Engineering and Architectural Science, and the Faculty of Sciencealready have connections with industry partners such as IBM Canada, St. MichaelsHospital, Blackberry, etc.

1.8Academic Home Unit

The Steering Curriculum Committee members for the proposed certificate made thedecision with Departmental Chairs present (Mathematics, Computer Science andMechanical and Industrial Engineering) that the Academic Home for the proposedcertificate will be the Department of Mechanical and Industrial Engineering in theFaculty of Engineering and Architecture Science.

The Dean of Record will be the Dean of the Faculty of Engineering and ArchitecturalScience.

The Computer Science and Mathematics Departments will serve as teachingdepartments and will contribute courses to the certificate. Along with faculty from the

Academic Home department of Mechanical and Industrial Engineering, faculty from theMathematics Department and the Computer Science Department (as well as facultyfrom other academic departments and Faculties) will be members of the StandingCurriculum Committee for the certificate. The Standing Curriculum Committee shallhave a majority of faculty members (RFA).

With the permission of the respective Departmental Chairs, faculty from the Departmentof Computer Science and faculty from the Department of Mathematics will be invited toteach courses (as faculty overload) in the certificate. The Co-Academic Coordinatorswill be one (1) faculty member from the Department of Mechanical and IndustrialEngineering and (1) faculty member from the Department of Computer Science.

1.9 Curriculum Structure and Professional Competencies

Curriculum is designed to meet the requirements of INFORMS Certified AnalyticsProfessional (CAP) program. CAP requires proficiency and skills in the followingseven domains:

1. Business Problem Framing2. Analytics Problem Framing3. Data4. Methodology5. Model Building

6. Deployment7. Model Life Cycle Management

This Certificate requires the completion of six courses, including one Capstone coursethat cover the aforementioned seven knowledge domains (Figure 1).


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The Capstone course will be available to students who have completed at least 4certificate courses and/or who are taking the fifth certificate course concurrently with theCapstone course. The Capstone course may be taken separately, or on its own.

The certificate shall provide a strong foundation in Analytics, Tools, and Statistics. Thecertificate holders shall become qualified as analytics professionals or quasi-professionals. They will have acquired the highest standards of good analytics practiceand they will be well-grounded to pursue a path to taking advanced study (i.e. mastersprogram) to continue their professional development in analytics.

See the Curriculum Mapping that follows

(Competency)

Figure 1. Curriculum and Competency Mapping


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1.10Proposed Program Courses

The curriculum of the Certificate consists of the following six courses:

1.10.1 Introduction to Big Data Analytics (Teaching Department:

Mechanical and Industrial Engineering / Computer Science)

This course is designed to give students overview of big data, state of the practice inanalytics, the role of the data scientist, big data analytics in industry verticals, andanalytics lifecycle as an end-to-end process. It focuses on key roles for a successfulanalytic project, main phases of the lifecycle, developing core deliverables forstakeholders, team work skills, and problem solving skills. The course involves in-classlectures, individual assignments, and team projects where students solve a problem,work on a project, present their work in written form as project report, as well as bymaking presentations.

1.10.2 Data Access and Management (Teaching Department: Computer

Science)

The course focuses on data querying and reporting techniques as well as data miningand cleaning techniques. Overview of Database Management Systems (DBMS),differences between database architectures, the role of data and databaseadministrators, fundamental concepts and need for data warehousing.

1.10.3 Data Analytics: Basic Methods (Teaching Department: Mechanical

& Industrial Engineering)

This course is an introduction to R, analyzing and exploring data with R, and using Rwith a database. It focuses on statistics for model building and evaluation. Topics coverexperimental research, correlation analysis, regression, confidence intervals, group

comparisons, parametric and non-parametric models.

1.10.4 Data Analytics: Advanced Methods (Teaching Department:Mathematics)

This course builds on the Basic Methods course and covers more advanced conceptsincluding classification and clustering algorithms, decision trees, linear and logisticregression, time series analysis, and text analytics. The course will provide appliedknowledge on how to analyze large scale network data produced through social media.In this context topics include network community detection, techniques for link analysis,information propagation on the web and information analysis of social media.

1.10.5 Big Data Analytics Tools (Teaching Department: Mechanical andIndustrial Engineering)

This course is an introduction to learning big data tools such as Hadoop and advancedSQL techniques. Students will gain a clear understanding of Hadoop concepts,technologies landscape and market trends. They will construct SQL queries ofmoderate to high complexity to retrieve data from a relational database.


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1.10.6 Capstone Course: Emerging Best Practices in Data Analytics &

Predictive Analytics (Mechanical and Industrial Engineering, &

Mathematics, Computer Science)

This course involves a hands-on application of analytics to apply what has been learnedin the previous five courses in a project setting. The Capstone course is intended to

provide an opportunity in a Final Project to bring to fruition an applied synthesis of dataanalytics methods, techniques and applications (learned in the coursework previouslyundertaken in the program) and apply the competencies that this synthesis affords to areal-world area of interest (AOI). Working with a faculty supervisor, participants willapply what they have learned in respect to their real-world AOI.


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Certificate in Big Data, Data Analytics and Predictive Analytics

Curriculum Structure

All courses prior to the Capstone course may be taken in any order. The following sequence is

recommended:

-The Introduction to Big Data Analytics and Data Access Management Is to be taken first, but it may be

taken concurrently with another certificate course of the students choice.

-Data Analytics: Basic Methods must be taken prior to the Data Analytics: Advanced Methods.

-It is recommended that Data Analytics: Basic Methods be taken before Data Analytics: Advanced

Methods.

-It is recommended that four of the certificate courses be taken before the Capstone course.

The Capstone course may be taken in conjunction with the students fifth course in the certificate.

Admissions

Introduction to

Big Data

Analytics

Capstone Course

Emerging Best Practices in Data Analytics

&

Predictive Analytics

Data Access

&

Management

Data Analytics:Basic Methods

Data Analytics:

Advanced

Methods

Big DataAnalytics

Tools


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1.11Professional Development Award

Given that some students may not need the full certificate, and that others mayappreciate recognition along the path to achieving the full certificate, The Chang Schoolwill award, upon request from students, a recognition of professional development whenstudents have completed three of the required certificate courses.

1.12Target Participants and Job Opportunities

The Certificate is targeted at those who wish to receive an excellent grounding in DataAnalytics, Big Data and Predictive Analytics. Participants may include those individualswho:

wish to become, or already are, professionals who wish to use Data Analytics,Big Data and Predictive Analytics to optimize performance at a variety of levels ina wide range of sectors such as private enterprise, government, non-profits,industry, high technology, and in R&D and the delivery products and services;

are interested in the field of Data Analytics and wish to contribute to a range of

employment and technical disciplines, and are employed in a related field such as data warehousing, data management, IT,

etc. and wish to gather the requisite competencies and credentials for promotionor other career advancement, including competencies related to Big Dataanalytics.

For those participants seeking career advancement or career mobility (change), theproposed program offers opportunities in a variety of dynamic, challenging and well-paying positions that are demonstrably in need of skilled practitioners. Graduates mayapply for positions in Toronto specifically, and Southern Ontario generally, in theknowledge that the preparation they received will hold them in good stead 8:

Web Analytics specialist Data Analyst (in various industry domains) Data Analytics Project Lead Data Science Specialist Data Warehouse Specialist Statistical Modeling Analyst Data Analytics Modeling Analyst Predictive Analytics Modeling Analyst

8Retrieved from Indeed.com, September 3, 2013.


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2 RationaleThe overarching goal of the Certificate is to offer professional, career-relevant educationin Data Analytics through the integration of a predictive analytics approach in order toenhance organizational effectiveness resulting from evidence-based data analytics thatpredict future events across an organization or sector.

The curriculum will integrate both theory9and practice10, to produce graduates with acapacity to harness the power of data analytics, with a sound and relevant skill set andwith overall mid-level managerial competencies related to the complexities of decisionmaking by producing fully processed data sets compatible for building robust predictivemodels that may, in turn, be deployed to create actionable initiatives substantiated bydata rich results.

The curriculum emphasizes the knowledge, capacity-building, methods, strategies,processes, applied skills, tools, managerial decision making, mechanisms and actionsnecessary to ensure that an organizations data analytics activities can be executed

successfully, using the best practices, effective coordination, technologies, provenprocedures together with human talent and other resource allocations.

A key goal of the curriculum is to engage participants in a comprehensive spectrum ofdebate in the data analyticsfield, including critical perspectives toward leveraging theknowledge of best practices in creating an analytical workplace culture to drive highperformance, innovation and sustainability within organizations. Obtainingorganizational buy-in for data analytics findings and data-driven decision making,choosing and deploying the right solutions and initiatives to meet identified needs,together with disseminating immediately useful analyses to diverse stakeholders withinan organizationare all critical to success.

The Standing Certificate Curriculum Committee, once the certificate is approved, willdiscuss and decide regularly whether it is necessary to update the Certificates coursecontent for currency and for professional and academic quality.

9Ranging from principles to international best practices.

10Through course projects and the Capstone course Final Project, all involving real-world applications.


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2.1Relevance to Ryersons Goals

This Certificate directly addresses Ryersons mission:The special mission of Ryerson University is the advancement of applied knowledgeand research to address societal need, and the provision of programs of study that

provide a balance between theory and application and that prepare students for careersin professional and quasi-professional fields

11.

This Certificate directly addresses the realities of our market society and howgovernments, non-profits, industry and products and servicesproviders need toleverage data (including Big Data) to better understand their clients and learn from thecollective experiences of their organizations in order to optimize actionable initiativesbased on Data Analytics and to be sustainable, relevant, current, productive andcommercially successful.

The curriculum focuses on direct practical application while providing sound academicand technical education in Data Analytics together with Big Data. In essence, thisCertificate program meets a range of pressing societal needs and lies at the heart ofwhat we need to do to provide real-life solutions that optimize our world and humanenterprise for optimal performance, innovation and sustainability.

11Ryerson University Mission Statement

Existence is no more than the precarious attainment of relevance in an intensely

mobile flux of past, present, and future.

Susan Sontag, American author, literary theorist, and political activist.


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3 Course Descriptions and Learning Outcomes

3.1The Certificates Learning & Competency Outcomes

The Certificates learning outcomes and professional competencies are correlated withthe INFORMS knowledge domains as follows:

Knowledge Domain 1 - Business Problem Framing

Obtain or receive problem statement and usability requirements Identify stakeholders Determine whether the problem is amenable to an analytics solution Refine the problem statement and delineate constraints Define an initial set of business benefits Obtain stakeholder agreement on the problem statement

Knowledge Domain 2 - Analytics Problem Framing

Reformulate problem statement as an analytics problem Develop a proposed set of drivers and relationships to outputs State the set of assumptions related to the problem Define key metrics of success Obtain stakeholder agreement

Knowledge Domain 3Data

Identify and prioritize data needs and sources Acquire data

Harmonize, rescale, clean, and share data Identify relationships in the data Document and report findings (e.g., insights, results, business performance) Refine the business and analytics problem statements

Knowledge Domain 4Methodology

Identify available problem solving approaches (methods) Select software tools for appropriate methods Test approaches (methods) Select approaches (methods)

Knowledge Domain 5 - Model Building

Identify model structures Run and evaluate the models Calibrate models and data Integrate the models


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Document and communicate findings (including assumptions, limitations, andconstraints)

Knowledge Domain 6Deployment

Perform business validation of the model Deliver report with findings; OR Create model, usability, and system requirements for production Support deployment

Knowledge Domain 7 - Model Lifecycle Management

Document initial structure Track model quality Recalibrate and maintain the model


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Learning Outcomes

Course Learning Outcomes

Introduction to Big Data

Technical FoundationCritical Thinking

Knowledge Extension Transfer

Research Application

Report Writing

Obtain or receive problem statement and usability requirements Identify stakeholders

Determine whether the problem is amenable to an analyticssolution

Refine the problem statement and delineate constraints Define an initial set of business benefits Obtain stakeholder agreement on the problem statement Reformulate problem statement as an analytics problem

Develop a proposed set of drivers and relationships to outputs State the set of assumptions related to the problem Define key metrics of success

Obtain stakeholder agreementData Access andManagement

Application of KnowledgeTransfer

Adv. Critical Thinking


Report Writing

Identify and prioritize data needs and sources

Acquire data Harmonize, rescale, clean, and share data Identify relationships in the data Document and report findings (e.g., insights, results, business

performance)

Refine the business and analytics problem statements

Data Analytics: BasicMethods

Critical thinking

Experimental research

Identify available problem solving approaches (methods) Select software tools for appropriate methods Test approaches (methods)

Select approaches (methods) Conduct experimental research Statistical model building

Data Analytics: AdvancedMethods




Report Writing


Identify available problem solving approaches (methods) Select software tools for appropriate methods Test approaches (methods)

Select approaches (methods) Utilize techniques to leverage Big Data, Networked Information

and Social Media Analysis. Explore how to practically analyze large scale network data and

how to reason about it through models for network structureand evolution.

Develop skills in graph theory, allowing them to understand thestructure and dynamic in self-organizing networks, such as the

Web. Employ network ranking and network searching techniques in

modern searching engines.

Understand the small world phenomena. Apply probabilistic models of information flow to understand

cascading behavior


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Course Learning Outcomes

Big Data Analytics Tools



Report Writing

Identify model structures Run and evaluate the models Calibrate models and data

Integrate the models

Document and communicate findings (including assumptions,limitations, and constraints) Use data analytics tools and technologies such as Hadoop,

SQL, etc.Capstone Course:

Emerging Best Practices inData Analytics




Report Writing


Demonstrate through a final data mining project how algorithmswork qualitatively

by applying and reviewing best practices and the influenceof various option choices on data analytics models.

Perform business validation of the model

Deliver report with findings; OR

Create model, usability, and system requirements forproduction

Support deployment

Document initial structure Track model quality Recalibrate and maintain the model

At the end of the Certificate program, participants will be well versed in using a varietyof databases and data sets to analyze and understand data and predict futureeventualities, trends and patterns, as well as be proficient in laying the groundwork,strategies and implementation of decision management in order to substantiate futureinitiatives that lead to innovation, high performance and sustainable outcomes forsuccess.


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4 Course Outline Prospecti

4.1 Introduction to Big Data Analytics

4.1.1 Course Description

This course is designed to give students overview of big data, state of the practice inanalytics, the role of the data scientist, big data analytics in industry verticals, andanalytics lifecycle as an end-to-end process. It focuses on key roles for a successfulanalytic project, main phases of the lifecycle, developing core deliverables forstakeholders, team work skills, and problem solving skills.

Textbook:

Thomas H. Davenport, Jeanne G. Harris (2010. Analytics at Work: Smarter Decisions,Better Results [Hardcover] 240 pages. Harvard Business Review Press.

4.1.2 Teaching MethodThe course involves in-class lectures, individual assignments, and team projects wherestudents solve a problem, work on a project, present their work in written form as projectreport, as well as by making presentations. Additionally, industry expert-practitionersguest speakers to share their professional wisdom and experience.

4.1.3 Evaluation

Students will be evaluated through one final written assignment, a data analyticsresearch plan applying course learnings in carrying out the research plan. Three papersand/or projects are judged by four criteria: Outline (10%); Quality of Data AnalyticsResearch Plan and Applied Analysis (40%); Quality of Writing (40%); Quality of

Presentation (10%).

4.1.4 Learning Outcomes

Obtain or receive problem statement and usability requirements Identify stakeholders Determine whether the problem is amenable to an analytics solution Refine the problem statement and delineate constraints Define an initial set of business benefits Obtain stakeholder agreement on the problem statement Reformulate problem statement as an analytics problem

Develop a proposed set of drivers and relationships to outputs State the set of assumptions related to the problem Define key metrics of success Obtain stakeholder agreement


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Topics What is data science? What skills are required for data scientists? What is data analytics? Overview of Data and Data Model Overview of Statistical Tools for Data Analytics

Basics for Data Analytics The need for Combining Knowledge and Skills from Statistics and Database Introduction to Big Data (From Database to Data Warehouse) Processof Data Analytics Overview ofMethods and Toolsfor Data Analytics (for structured and unstructured data) Modeling and Reporting Results

4.1.5 Course Prerequisites

This course is to be taken first. It may be taken concurrently with anothercertificate course of the students choice.

4.2Data Access and Management


The course focuses on data querying and reporting techniques as well as data miningand cleaning techniques. Overview of Database Management Systems (DBMS),differences between database architectures, the role of data and databaseadministrators, fundamental concepts and need for data warehousing.

Textbook:

J.D. Ullman, J. Widom (2007). A First Course in Database Systems, 3rd Edition.

Prentice Hall.

Topics:

Introduction Data and Data Management Relational Model Relational Algebra SQL PL/SQL Overview of query processing and DBMS architecture Data warehousing

Data mining and cleaning techniques NoSQL

4.2.2 Teaching Method

This course will be available through in-class and online delivery through a series oflecture presentations, discussions, simulations, case studies, readings, self-guided


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videos, etc. Additionally, industry expert-practitioner guest speakers will participate toshare their professional wisdom and experience

4.2.3 Evaluation and Learning Outcomes

Students will be evaluated through homework (20%), lab work (20%) and exams (Mid-

Term:20%; Final Exam: 40%).

Learning Outcomes

Identify and prioritize data needs and sources Acquire data Harmonize, rescale, clean, and share data Identify relationships in the data Document and report findings (e.g., insights, results, business performance) Refine the business and analytics problem statements

Prerequisites

None

4.3Data Analytics: Basic Methods


This course is an introduction to R, analyzing and exploring data w

certificate in data analytics proposal appd by senate 2014

Documents