Woman doing a business presentation at the office

Big Data Analytics Program

The demand for big data experts is exploding.

 

get-qualified

Certificate in Big Data Analytics
Certificate in Advanced Data Science and Predictive AnalyticsBeginning Winter 2018

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?

did-you-know1
did-you-know2

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 computer 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 - layer mask

 

Roland Merbis, Director of Customer Insights & Analytics at Scotiabank.

 


 

Program delivery.

 

The part-time Big Data Analytics Program is delivered online with on-campus evening computer lab time. Comprised of two unique certificates—the Certificate in Big Data Analytics and the Certificate in Advanced Data Science and Predictive Analytics— the program covers the domains identified by the INFORMS Certified Analytics Professional (CAP®) Program. You can choose to take one certificate or both. However, if you want to pursue INFORM’s CAP® designation, you should plan to complete 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.


Prepare for your
Certified Analytics Professional (CAP®) designation.

Certified badge

The Certified Analytics Profession (CAP®) designation is the premier global professional certification for analytics practitioners. The CAP® designation is offered through the Institute for Operations Research and the Management Sciences (INFORMS), the largest society in the world for professionals in the field of operations research (O.R.), management science, and analytics. If you are interested in pursuing the CAP® designation, see the INFORMS (visit https://www.certifiedanalytics.org/ for full details.)

 


 

Who should take this program?

Current analytics professionals who are pursuing the Certified Analytics Professional designation or seeking a university credential for career advancement.
Specialists in other fields (such as marketing, insurance, finance, human resources, and policy) who want to confidently leverage big data to excel in their sector.
Any professional who wants to enter the quickly growing field of big data analytics.

 


YOR0076 Data_Analytics_graphic

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?

Take our prep course to find out. When you register for the Certificate in Big Data Analytics, the cost of the prep course will be deducted from your program fee. Please contact us for more details.

Advisory Council

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, Architecture, IT Strategy & Applied Innovation, The Co-operators

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.

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

Instructors

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.

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.

Imad Jawadi, BASc

As an executive of a financial institution, Imad leads the analytics, business intelligence, data governance and enterprise content management functions for his organization. He has over 20 years of experience in information technology, analytics and data management across all aspects of information management. Imad’s strategic and analytical thought leadership has enabled organizations to streamline operations, optimize cost, become data-driven, and respond to business needs at a fast pace. With sound strategy, clear and actionable execution planning, and focused program delivery, he delivers on the ‘art of the possible’.

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.

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.

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 with emphasis on preparing you for the INFORMS CAP® designation.

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.

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.

Certificate in Advanced Data Science and Predictive Analytics

 

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.

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.

Software

As our program keeps up to date with current industry software demands, the below software listing provides an overview of the different software that may be used in each of the courses.  Please note that the actual software taught within each course is subject to change.

Certificate in Big Data Analytics

Introduction to Big Data

  • Excel (power query)
  • SQL
  • Tableau

Basic Methods of Data Analytics

  • Python
  • R
  • RapidMiner
  • Rodeo
  • Rstudio
  • Tableau
  • Weka

Big Data Analytics Tools

  • Apache Cassandra
  • Apache Hadoop
  • Apache Hive
  • Apache Spark
  • Elastic Search

Certificate in Advanced Data Science and Predictive Analytics

Data Organization for Analysis

  • Apache Hadoop
  • Apache Spark
  • Denodo Express
  • Excel (power query)
  • MongoDB
  • MySQL (standard edition)
  • Neo4j
  • Redis
  • Qlik Connectors
  • Qlik Sense
  • Qlik View
  • Tableau
  • Talend Data Prep
  • Talend Open Studio for Data Integration

Advanced Methods of Data Analytics

  • Python
  • R
  • RapidMiner
  • Rodeo
  • Rstudio
  • Tableau
  • Weka

Advanced Analytics Capstone Course

  • Your choice of any software

Computer Requirements

All students will require access to a laptop personal computer while in the Data Analytics program.

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