The demand for big data experts is exploding
Representing one of the largest talent shortages in Canada, big data opportunities are exploding in every sector from marketing to financial services to professional sports. The Greater Toronto Area is at the epi-centre of this talent gap. Employers frequently report that they cannot find qualified candidates and are heavily investing in re-training their workforce.
Did you know?
The demand will continue to grow with open access to data sets, the introduction of new big data software applications, and the increased applicability of big data to new market segments.
Harness big data to achieve your organizational goals
We have access to more data than ever before. But that information is meaningless without people who know how to properly analyze it. This is why both public and private sector organizations now rely on big data experts and predictive analysts to help them make informed decisions.
If you are currently working in analytics or a related field, the Big Data Analytics Program can train you to identify and leverage key opportunities to support your organization’s strategic objectives. And once you know how to turn your findings into actionable insights, you will be indispensable to any employer.
Even if you aren’t working in the analytics field, becoming an expert in data analysis is invaluable in many industries. Specialists in areas such as marketing, insurance, finance, human resources, and policy deal with big data every day. These certificates will help set you apart from the rest of the pack and enable you to grow your career options.
The program provides a comprehensive education in contemporary data analytics. You will learn data analytics foundations, basic and advanced methods, and relevant big data analytics toolsets. In addition to online coursework, you will also attend bi-weekly in-class labs to access the leading software applications with which many employers require familiarity. For a list of the software used in each course, please see the “Software” drop-down section below.
“Today more than ever, analytics play a crucial role in creating a positive customer experience. As a member of the program’s advisory council, I look forward to working with York University to help establish an analytics program that will set students up for success and close the skills gap for employers.”
Roland Merbis, Director of Customer Insights & Analytics at Scotiabank.
Program delivery
The part-time Big Data Analytics Program is delivered online on a part-time basis with on-campus evening lab time. Comprised of two unique certificates—the Certificate in Big Data Analytics and the Certificate in Advanced Data Science and Predictive Analytics. You can choose to take one certificate or both.
Each course is only eight weeks long and you can earn your first certificate in just six months. That means in under a year you will be able to add two highly valuable credentials to your resume. A full-time, fast-track option will be introduced in the summer of 2018 for those who wish to complete both certificates in just four months.
Who should take this program?
Admission Requirements
The Certificate in Big Data Analytics is a direct registration program. No application process is required; simply enrol in the session of your choice to get started.
To enrol in the Certificate in Advanced Data Science and Predictive Analytics, you must either complete the Certificate in Big Data Analytics or be able to demonstrate equivalent education/work experience.
Not sure if you have the necessary skills and knowledge to take this program?
Please contact us for more details.
Advisory Council
Senior executives from many of Toronto’s leading organizations help us ensure that our graduates have the skills and knowledge that employers value most, including the following:
Hashmat Rohian, Senior Director & Managing Enterprise Architect at Cooperators Group of Companies
Hashmat is currently the Senior Director & Managing Enterprise Architect at Cooperators Group of companies. He is responsible for leading The Cooperator’s digital strategy and execution, with a focus on Enterprise Architecture & Applied Innovation.
Hashmat’s career has centred on ‘data science’ and ‘digital transformation’ with a focus on delivering significant and sustainable value right through from strategy to execution.
Besides being a practitioner, Hashmat also teaches software engineering, data science and agile delivery. He co-founded DisrupIT Labs that helps clients harness data analytics and digital in support of business strategy, deliver cross-functional, rapid prototyping capability to push and test new value propositions in the market fast.
His professional training as a software engineer, quantitative analyst, experience as a data scientist, fraud examiner, information security professional, digital integration specialist and entrepreneur gives him a unique advantage in driving organisational transformation and value.
Tarundeep Dhot, Principal Consultant, Capco
Tarun Dhot is a principal consultant at Capco and advises financial institutions in how they can derive business value through their data assets. Tarun is a recognized leader in the fields of advanced analytics and machine learning, serving on advisory councils of industry bodies. Tarun previously held management and consulting roles at CIBC including leading the advanced analytics group and supporting multiple business verticals that included client experience, fraud strategy, decision science, channels, products and payments. Tarun has also collaborated with multiple organizations including the Los Alamos National Laboratory and Google. Tarun holds a Master’s degree in Computer Science specializing in Artificial Intelligence and a Bachelors in Electrical Engineering.
Duncan Rowe, Manager, Analytics and Visualization, Corporate Services Department, Regional Municipality of York
Duncan Rowe is a public sector executive who cares about helping York Region experience the power and value of using data for good. A leader in the data, analytics and visualization services branch, the team Duncan leads loves to work with people and their data. Duncan gets great satisfaction when clients enhance their programs informed by analytics, are inspired by visuals to share, and connect deeply through data storytelling.
He has over 25 years’ experience working in the field, and has been working at the Regional Municipality of York for more than 15 years. In addition to building data and analytics capacity across the organization, Duncan and his team provide corporate analytics and visualization services to all of the service areas delivered in York Region.
Duncan has an undergraduate degree from York University, and a Masters Certificate in Municipal Leadership from the Schulich School of Business.
Roland Merbis, Director, Customer Insights & Analytics, Scotiabank
Roland Merbis is responsible for leveraging customer data and feedback into meaningful insights that drive business results for Scotiabank. He has close to 20 years’ experience in the customer insights profession, starting as a market research professional supporting clients in various industries, from consumer packaged goods to government agencies. In his current role, Roland champions the voice of the customer and manages a team of data analysts and data scientists with a mandate to integrate market research insights with the bank’s customer and transactional data. His team is responsible for developing strategic insights using consumer research data and customer analytics to effectively manage business issues and identify business growth opportunities.
Roland currently serves as treasurer on the Board of the Canadian Mental Health Association (Durham) and recently completed his term as member of the National Board of Directors of the Marketing Research & Intelligence Association (MRIA).
Rachel Soloman, Executive Director, Performance Improvement, CAMH
Rachel Solomon is the executive director of performance improvement at the Centre for Addiction and Mental Health, with accountability for enabling standardized, evidence-informed and data-driven decision-making and quality improvement throughout the hospital. Previously, Rachel held a variety of senior management roles at Toronto Central Local Health Integration Network (LHIN). Most recently, she was the senior director of performance measurement and information management, leading the LHIN’s analysis and performance measurement work, capital planning, and initiatives to improve quality and equity in health care.
Prior to joining the Toronto Central LHIN, Rachel worked at University Health Network as the director of health system integration, leading the provincial development and implementation of the multiple provincial information systems. In this capacity, she also oversaw the first regional implementation of a referral program in Toronto that matches patients’ needs to system resources. Previously, Rachel worked in the Ministry of Health and Long-Term Care in several capacities, including managing the provincial Wait Time Strategy and playing an instrumental role in the implementation of the Ontario Stroke Strategy.
Ian Scott, Partner and Chief Data Scientist , Strategic Analytics and Modelling, Deloitte Analytics
Ian Scott is Deloitte’s Chief Data Scientist and leads advanced analytics and big data practice. He received a Ph.D. in Physics in 1993 from Harvard University.Previous positions in private industry he has held include vice-president at Lattice-Engines (a Silicon Valley big data company), CTO of Angoss (Toronto-based analytics software company on the Gartner Magic Quadrant serving banking and the broader financial services industry) and data scientist at Capital Fund Management (a Paris-based hedge fund).
Ian’s academic career includes professor and postdoctoral positions at the University of Wisconsin-Madison, Stanford University, and CERN (European Centre for Particle Physics Research in Geneva).
Boris Kralj, Chief Information and Analytics Officer, Ontario Medical Association
Boris Krajl holds a PhD in Economics from York University. Dr. Kralj has lengthy experience in various management, research, and consulting positions with both public and private sector organizations. Currently he holds the positions of Chief Information Officer/Chief Analytics Officer at the Ontario Medical Association (OMA). He provides leadership and oversees the technology as well as the economics, research, and analytics functions. He is responsible for the effective planning, development, deployment, security, operation and support of OMA and its subsidiaries, information and communication technologies to support the OMA’s strategic business objectives and operations. Additionally, Dr Kralj provides guidance and oversight to the economic research, policy, and evaluation work associated primarily with the negotiation and implementation of physician fee-for-service (FFS) and non-FFS agreements and the physician fees/tariff setting processes. He he is also responsible for enterprise analytics initiatives.
Dr. Kralj has authored and published a broad variety of research reports. His publications relate to physician payment reform, primary care, medical services utilization, physician human resources, workers’ compensation, physician billing, oncology practices, prescribing patterns, and walk-in clinics. He has lectured on microeconomics, macroeconomics, industrial organization, and labour economics for over 15 years.
Deepak Sharma, Director, Health Information Management, Business Intelligence, North York General Hospital
Deepak Sharma is an executive in the healthcare industry who uses big data analytics in leading corporate initiatives such as funding optimization analysis, clinical quality improvement projects, IT system implementation, privacy policy development, and performance measurement in large hospitals and government agencies. Currently, Deepak is the director of patient flow, health information management, and business intelligence at North York General Hospital. Previously, he was the director of decision support at London Health Sciences Centre, and the director of transformation and performance evaluation services at Rouge Valley Heath System.
Brent Fagan, Consultant, Data & Analytics, KPMG
Jason Garay, Vice President, Analytics and Informatics, Cancer Care Ontario
Courses
Certificate in Big Data Analytics
Introduction to Big Data
Growing opportunities to collect and leverage digital information has given birth to various new areas of data analytics. Gain an introduction to the exciting world of data analytics.
Basic Methods of Data Analytics
Exploratory analysis and prediction are among the most common tasks performed people new to the field of big data. Learn the basic components of exploration, visualization, feature engineering and building and applying basic prediction models with an emphasis on practical applications.
Data Organization for Analysis
Study the evolution of approaches to data provision and extensions that support big data. Learn data provisioning techniques to support new types of big data and the related analytic demands and workloads applied to it. You will capture, store and understand how to provide metadata to processes and analysts who require it.
Certificate in Advanced Data Science and Predictive Analytics
Big Data Analytics Tools
The potential insights that big data can provide can be very valuable for organizations. However storing, transforming and analyzing big data can be challenging. Explore the analytics tools that will allow you to apply traditional data analytics and business intelligence skills to big data.
Advanced Methods of Data Analytics
Discover the complete process of building prediction functions, including data collection, feature creation, algorithms, and evaluation in this advanced course. A range of machine learning model based and algorithmic machine learning methods will be introduced. You’ll also learn how to apply learning algorithms to mining social media and network (sentiment, influence), text understanding (web search, anti-spam), database mining, and other areas.
Advanced Analytics Capstone Course
Exercise the skills acquired in the previous five courses as you analyse a case study with data sets. This hands-on course requires analyzing a real-life scenario, including data collection, preparation, integrating, modelling and analyzing and will result in a practical example that you can use to demonstrate your skills and knowledge to potential employers.
Instructors
Leotis Buchanan, MPhil
Leotis is currently a Data Engineer at RBC. In this role, he builds big data applications that transform, store and analyze large quantities of data in a performant manner. Previously Leotis was the owner and co-founder of a startup that focused on developing applications for farmers. He also has worked as an electrical power engineer before coming to Canada, and has been teaching for the last 10 years. Currently his focus is on designing and building big data processing systems. In his spare time he studies deep learning and its application to self-driving cars.
Mayy Habayeb, MASc, BASc, PMP
Mayy is a software engineering professor and she has practical hands-on experience in developing machine learning algorithms and big data recommended systems. Her research work has been published in the IEEE Transactions on Software Engineering journal and she has participated and published conference papers at the “IEEE Big Data” and the “Data mining software repositories” conferences. Mayy has developed and delivered courses in the areas of big data, data analytics, and data mining at universities and colleges across Canada.
Her practical 20+ years of experience covers the banking, telecommunication and academic industries. She has led many projects in fields of CRM, geo-marketing research, business intelligence, network expansions and channel migration across several countries worldwide. She continues to support data analytic initiatives in organizations, with a focus on maximizing the value obtained from data by building analytical models that relate to data & business processes with the objective of providing information for key decisions.
Masoum Mosmer
Masoum is the Director of Data Science at Royal Bank of Canada where he leads a team of highly-skilled data scientists and engineers. Prior to joining RBC, Masoum was the Senior Manager of Advanced Analytics at Aimia, a data-driven marketing and loyalty analytics company. As a hands-on practitioner, he has experience building and deploying predictive models that optimize marketing decisions for clients in the Retail, CPG, Financial Services, Travel, and Loyalty sectors. He holds a B.Sc. in Mechanical Engineering from the University of Toronto and a Master of Management Analytics from Queen’s University.
Mark Peco, CBIP, MASc, BASc
Mark is a leader, consultant and educator in the fields of business intelligence and data analytics. In the corporate education area, Mark has developed and delivered many courses on topics related to big data, data provisioning, data quality, data governance, analytics and business intelligence. He has delivered courses in both classroom and online formats to corporate and government clients on a global basis. In the consulting and operations fields, Mark has worked in information strategy, performance management, control center operations, transaction control, compliance, business intelligence, analytics, simulation, application development, 5 program leadership and project management. He has built and developed high performing teams that delivered quality results.
Mark has significant industry experience in the energy sector gained from operational, leadership and consulting roles at production, transmission and distribution companies. His domain experience is primarily in system operations, commercial transaction control and asset management. As a Certified Business Intelligence Professional (CBIP) at the Mastery Level, his expertise has been demonstrated in the areas of analytics, information management and team leadership.
Hashmat Rohian, MSc, FnEng, CISSP, PMP, CFE, ACP, TOGAF, Lean Six Sigma
Hashmat is the Senior Director and Managing Enterprise Architect at The Co-operators Group. For over 10 years he has effectively led initiatives that have involved rapid advancement in data, analytics, digital, and business leadership. As a technology enthusiast and lean startup evangelist, he is renowned for building a “never rest, kill complexity and care more” culture that manages existing technical debt and prepares the organization for future disruptions by leveraging emerging business models and digital ecosystems. Hashmat is a published data science, IT, and agile delivery practitioner and academician with over 5 years teaching and curriculum development experience at colleges and universities in Canada. He has also organized conferences and workshops, and been interviewed on print, radio, social media and TV as an analytics expert.
Matthew Tenney, Phd, MA
Matthew has spent the past several years working with big data and smart cities in order to better understand where, when, and by whom location-data might be used in real-world applications. His interests has been focused on how code, space, and place coalesce through physical and virtual worlds, as people use mobile and digital technologies to navigate through their everyday lived experiences. Matthew’s work is the implications of big data technologies as they integrate more and more into our daily lives with potential of altering forms of participatory democracy. Matthew is a Senior Geospatial Data Scientist at the City of Toronto, PhD Fellow at McGill University, and was previously a Research Developer at Esri Canada Inc.
With over 10 years of experience, much of Matthew’s work has focused on developing and implementing new ways to explore and use big data in decision support systems that provide real-time feedback in domains such as urban planning, policy formation, and community representation.
Software
The list below provides an overview of the different software that may be used in our courses. As our program keeps up-to-date with current industry demands, please note that the actual software used and taught within each program is subject to change.
| Certificate in Big Data Analytics | Certificate in Advanced Data Science and Predictive Analytics |
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Computer Requirements
All students will require access to a personal computer while in the Data Analytics program. If the program has its in-class sessions held in a computer lab, a desktop computer is sufficient. However, if you are registered in a program that has its in-class sessions held in a classroom, a laptop computer is required.
Students will have access to a pre-installed server environment where they can utilize the software and datasets required for the program. In order to connect to the server, students will need to install free Virtual Private Network (VPN) and Virtual Network Computing (VNC) software. We recommend the following minimum specifications:
- Microsoft Windows 7, 8.1 or 10 / Mac OSX 10.9 or above / Linux operating system
- Intel / AMD 1.8GHz or higher CPU
- 2GB RAM
- 500 MB disk space
If students wish to install the course software on their own computers, we recommend the following minimum specifications:
- Latest or recent version of Microsoft Windows (7 or 10) or Linux operating system
- Dual or Quad-core CPU (Intel Core i3 or better or AMD equivalent)
- 8 GB RAM
- 20 GB disk space
Confidentiality and Financial Security
Given the experiential and practical nature of the courses and the application of Ontario’s Freedom of Information and Protection of Privacy Act (FIPPA) to York University, The School of Continuing Studies works to ensure that instructors and participants acknowledge and respect the privacy and confidentiality of personal information that may be presented in the context of instruction. Instructors will limit the amount of personal information that is collected, used or disclosed in their sessions, and will ensure that all identifying personal information (including proper name, address, etc.) is omitted from all written documents in order to protect personal privacy and confidentiality. Instructors should not bring or share personal or other confidential files or records with the class or allow students to do so.
We are committed to protecting your privacy and your financial security, and we do this in several ways:
- Your credit card information is never received or stored by our system. Only your financial institution has access to your credit card information.
- Your Student Portal is password-protected. To access any personal and academic information, you must enter your username and portal password.
How to Register
Online – Visit our website at continue.yorku.ca to register in any course or program offered by the School of Continuing Studies.
All registrations are processed on a first-come, first-served basis, so early registration is recommended.
The School of Continuing Studies reserves the right to alter fees, other charges, instructors and course dates/locations described in this brochure.
Fees
Instalment Plan Policy
The School of Continuing Studies allows students in certificate programs the option of paying in two or more instalments. You must pay the initial payment as stated below and then you can create a payment schedule that works for you, as long as the balance is paid before the stated due date.
The instalment plan comes with a one-time, non-refundable administrative fee of $125 due at the time of registration. The following are the details of the instalment plan policy according to the program you have registered for:
- Certificate in Big Data Analytics– The first payment of $1299.00 (CAD) is due upon registration with the balance due by program start date.
- Certificate in Advanced Data Science and Predictive Analytics– The first payment of 1599.00 (CAD) is due upon registration with the balance due by program start date.
- Bundle package for both programs (Certificate in Big Data Analytics + Certificate in Advanced Data Science and Predictive Analytics) – The first payment of 2475.00 (CAD) is due upon registration with the balance due by the program start date.
- Customized payment plans are available to align to employer policies, but are not available for individuals. For further information please contact the Registration and Student Records Coordinator at 416-736-5616.
- A student that fails to make instalment payments as required will not be permitted to continue in the program.
Mailing Address and Changes in Personal Status
All correspondence, including your registration confirmation, will be sent to the email address provided at the time of registration.
To maintain accurate student records, notification of any changes to your name, address and contact information are required. To update your personal information:
- Log in to the Student Portal with your student ID and password and update your personal information
OR
- go online to Contact Us and email all changes
OR
- submit a written request to the Registration and Student Records Coordinator at continue@yorku.ca
All name-change requests must be accompanied by official documentation justifying such a change.
We will not accept telephone requests to change a name or address.
Education and Amount Certificates (Income Tax Receipts) (T2202A)
Income Tax receipts will be available online in February of the following year. Please refer to the income tax guide for allowable deductions.
Session Transfer
Students must contact the Program Manager for permission to transfer to another session.
Transfer requests are only granted for documented medical reasons. Please contact the program manager for information on the required documentation.
Withdrawal from the Program
Students registered in a part-time program that is less than 1 year in length may withdraw from the program. However, they are strongly advised to consult with the Program Manager before a final decision is made. A full refund is granted only when the School of Continuing Studies cancels a program. Refunds will be issued using the initial method of payment or by cheque, if original payment was made by money order. Withdrawal requests must be submitted on the official School of Continuing Studies Withdrawal Request Form to the School of Continuing Studies and subject to the terms listed below.
- If you withdraw 7 calendar days prior to the start of the program, you will receive a 75% refund of program tuition instalment and no academic penalty.
- If you withdraw between 6 calendar days prior to the start of the program to 7 calendar days after the start of the program, you will receive a 50% refund of program tuition instalment and a $50 administrative fee and no academic penalty.
- No refunds will be issued after the first 7 days of the program.
Notification of change or cancellation of classes
When necessary, the School of Continuing Studies may alter, postpone or cancel classes. In these instances, students will be notified by email, based on the information provided at the time of registration.
Cancellations or changes will also be posted on the School of Continuing Studies Twitter account
Cancellation of courses/programs – Fee Refunds
The School of Continuing Studies reserves the right to withdraw or cancel programs/courses. Should a course or program be withdrawn or cancelled, the School will issue a full refund of fees paid.
University Policy on Student Conduct
- Students and instructors are expected to maintain a professional relationship characterized by courtesy, collegiality and mutual respect, and to refrain from actions that would be disruptive to such a relationship;
- It is the responsibility of the instructor to maintain an appropriate academic atmosphere in the classroom, and the responsibility of the student to cooperate in that endeavour; and,
- The instructor is the best person to decide, in first instance, whether such an atmosphere is present in the class, and may, at their discretion, take steps that they feel are appropriate to resolve an issue or dispute.
In any case where a student feels that this policy has been violated, they are urged to notify the instructor of the course/program as soon as possible. Students may be asked to provide a detailed written description of their complaint to the instructor. The instructor may take measures they feel are appropriate to resolve the issue and/or may forward the complaint to the Program Manager for review. Please refer to the full policy document on the York University website at: http://www.yorku.ca/scdr/
Grading
Students registered in certificate programs will be evaluated using the following categories of achievement:
| Grade | Grade Point | Per Cent Range | Description |
| A+ | 9 | 90-100 | Exceptional |
| A | 8 | 80-89 | Excellent |
| B+ | 7 | 75-79 | Very Good |
| B | 6 | 70-74 | Good |
| C+ | 5 | 65-69 | Competent |
| C | 4 | 60-64 | Fairly Competent |
| D+ | 3 | 55-59 | Passing |
| D | 2 | 50-54 | Marginally Passing |
| E | 1 | (marginally below 50%) | Marginally Failing |
| F | 0 | (below 50%) | Failing |
Note: all of the above-noted grades are used to calculate averages and credits.
Definitions of Grading Descriptions
A+ Exceptional. Thorough knowledge of concepts and/or techniques and exceptional skill or great originality in the use of those concepts/techniques in satisfying the requirements of an assignment or course.
A Excellent. Thorough knowledge of concepts and/or techniques together with a high degree of skill and/or some elements of originality in satisfying the requirements of an assignment or course.
B+ Very Good. Thorough knowledge of concepts and/or techniques together with a fairly high degree of skill in the use of those concepts/techniques in satisfying the requirements of an assignment or course.
B Good. Good level of knowledge of concepts and/or techniques together with considerable skill in using them to satisfy the requirements of an assignment or course.
C+ Competent. Acceptable level of knowledge of concepts and/or techniques together with considerable skill in using them to satisfy the requirements of an assignment or course.
C Fairly Competent. Acceptable level of knowledge of concepts and/or techniques together with some skill in using them to satisfy the requirements of an assignment or course.
D+ Passing. Slightly better than minimal knowledge of required concepts and/or techniques together with some ability to use them in satisfying the requirements of an assignment or course.
D Barely Passing. Minimum knowledge of concepts and/or techniques needed to satisfy the requirements of an assignment or course.
E Marginally Failing.
F Failing.
If you do not pass a course (achieve a final grade below 50% or D) you will still be permitted to continue in the Program but will have to repeat that course with another cohort.
Grade Appeal and Reappraisal and Petitions
Students may, with sufficient grounds, request a reappraisal of any “tangible” work required for a course/program. Tangible work may include written, graphic, digitized, modelled, video recording or audio recording formats. Students seeking a grade reappraisal must complete and submit the attached form, along with the original work and instructions for the assignment, to the Program Manager within 2 weeks of the date of issue of the letter of grade.
Students and instructors will be informed in writing of the reappraisal result and the reappraiser’s comments. The School of Continuing Studies will ensure the anonymity of both the student and the reappraiser.
Financial Petitions
You may submit a financial petition if you experience a serious documented medical illness or a death of an immediate family member that causes you to drop courses. Financial petitions may be granted at the discretion of the University, and will be considered for a period of one year after the occurrence of the illness or death.
The Financial Petition form is located here: http://sfs.yorku.ca/refunds/petitions/
Please complete the form and return it via email continue@yorku.ca or via fax at 416-650-8042.