Learn more about the academic programs we are delivering in Summer 2025. If you have any questions about part-time studies, please contact us.
Core Information
More Information
Overview
This graduate certificate program will provide you with the skills to identify and collect meaningful data, prepare data for analysis, conduct analysis of data sets and present results in a meaningful format to help inform business decisions across the enterprise.
Key topics of study include: statistics needed for analytics, programming languages for data analysis, security and privacy for the field of business analytics, data predictive analytics and leadership in the business environment.
Accreditation
Graduates will be eligible to receive the SAS Academic Specialization.
Part-time Studies courses are being offered in either of the following four formats: Online, Flexible, In-person, Hybrid. Click Availability below to see current offerings.
Curriculum
This course will introduce the business analytics process and how to apply statistical methods in different phases of the process. Students will learn statistical methods of creation, collection, analysis, validation and visualization of quantitative data to make recommendations for business decisions
This course introduces students to the opportunities and challenges related to data in a business environment in terms of identifying data for decision making, collecting, storing, and processing data. This course helps students understand structured and unstructured big data that is available from a variety of sources. The course will cover techniques to obtain data from the web, APIs, and databases in various formats. Students will learn the basics to store, clean, prepare, share, process, and report on this data.
This course helps students apply business analytics process in real business environments. Modern businesses collect vast amounts of data that are critical for managing the business today and driving innovations for tomorrow. Case studies will be used to prepare students for real world problem solving by developing their ability to recognize the different types of problems, the analytics methodology used, how the analysis was performed using statistical analysis software tools, how the results were interpreted, and how the analysis was translated into a presentation to inform business decisions.
This course introduces students to programming languages for effective data analysis. Students will learn to design, code and test programs necessary for a statistical programming environment. Data types and control flow will be applied to manipulate data, interpret it and model it to present meaningful output.
This course uses practical case analysis to provide an overview of methods, processes, architectures, applications and technologies that support data analytics with a focus on data visualization for presenting data in a meaningful way that will help businesses make decisions and take actions.
This course provides students with leadership and communications skills through the lens of big data and business intelligence to help students solve problems, manage risks and enhance competitive advantage for organizations. Students will gain understanding on how to communicate with other stakeholders to evaluate business needs, and how to communicate the outcomes of the analytics process to guarantee the effective application of its outcomes. Students will also gain people-management, negotiation, and conflict management skills for implementing useful business intelligence solutions, providing guidance for the organization and its partners, and coaching the team on the different kinds of analytics or data modeling. Students will apply strategic frameworks and personal and organizational leadership models to real-life business issues through case studies.
This course introduces students to techniques used to collect data from different sources, i.e., social media sources. Students will apply statistical methods to process text data in any natural language with minimum human effort. Students will also apply algorithms in text mining involving correlation, regression, pattern recognition, and knowledge extraction to derive insights about data sources and their potential applications.
This course will introduce students to recent developments in advanced analytics techniques. Predictive analytics encompasses a variety of machine learning techniques that analyze current and historical facts to make predictions about unknown future events. In this course, students will learn how to build a predictor through supervised or unsupervised techniques in a training stage, and will evaluate the predictor performance through a tester stage.
Students explore the ethical and social considerations of privacy in a globally connected work. This course examines the effect of the internet and biometrics on privacy laws, policies, and technology, and the impact to business sectors and individual and corporate rights. Students evaluate the cost of new personal data privacy initiatives and the impact to application domains in the future.
This course is structured along the 4-stage process of data mining, i.e.: (i) Problem Identification, (ii) Creating the Analytical File, (iii) Conducting the Analytics, and (iv) Measurement and Implementation. Students will learn how to both identify and prioritize business problems, while appreciating the importance of data and the data audit process. In addition to exploring the key principles in designing analytical reports, advanced data mining and modelling are discussed. Moreover, methods for measuring and deploying solutions are explored.
In this course, students apply knowledge acquired during the program to a project involving actual data in a realistic setting. They will go through different steps of the business analytics process to create business value. Using various techniques like, ways of cleaning-up data, multiple algorithms for analysis and modeling, students will make data-driven decisions in response to a real business challenge.
This mandatory course for WIL students prepares students to job search for their co-ops/work terms. Students will reflect on their skills, attitudes, and expectations and evaluate and interpret available opportunities in the workplace. Self-marketing techniques using resumes, cover letters, cold-calls, and interviewing will be learned and students will learn the expectations, rules, and regulations that apply in the workplace with regards to social, organizational, ethical, and safety issues while developing an awareness of self-reflective practice.
Program Outcomes
Program Learning Outcomes
This Seneca program has been validated by the Credential Validation Service as an Ontario College Credential as required by the Ministry of Colleges and Universities.
As a graduate, you will be prepared to reliably demonstrate the ability to:
- Extract, manipulate, and mine data sets from various sources to meet organizational needs.
- Apply business analytics and business intelligence tools as a business process to support evidence-based decision-making.
- Design statistical models using data mining techniques to meet the needs of a specific business process.
- Develop software applications to manipulate data sets, correlate information, and produce reports.
- Create and present data visualizations to communicate information to business stakeholders using multiple forms of communication.
- Apply basic entrepreneurial strategies to identify and respond to new business opportunities.
- Adhere to ethical and legal guidelines to ensure data security, integrity, and confidentiality when presenting analytical information.
- Apply interpersonal, teambuilding, and leadership skills when participating in diverse environments.
- Recommend business solutions for various business domains by utilizing research (findings) and analytical skills.
Entry Requirements
Ontario university/college degree, college diploma, advanced diploma or equivalent.
Applicants with a combination of partial postsecondary and/or significant relevant work experience that is the equivalent of the above may be considered. A relevant resume and references must be provided.
Applicants with credentials from outside of Canada must provide a "document-by-document" credential assessment from a recognized agency such as WES (World Education Services) or ICAS (International Credential Assessment Service).
Please note, that all international applicants must meet Seneca's English requirements. Additional documents/assessments may be required upon request.
Application Form
To apply for the Business Analytics Graduate Certificate, please complete the application form.
In addition to completing the application form, you must also submit supporting transcript(s) per the program's entry requirements to fcebusinessapplications@senecapolytechnic.ca.
Please use "Part-time Business Analytics Application" as the subject line of your email when submitting your transcript(s).
If you have any questions about the entry requirements or general questions about the program,
please contact:
Belinda Becker
Program Assistant
belinda.becker@senecapolytechnic.ca
Financial Aid/OSAP Funding
Financial Aid/OSAP Funding Available
This program is eligible for OSAP funding.
Course load is used by OSAP to determine funding options for programs. If you are taking one
to two courses at the same time, you may be considered for part-time student grants and loans.
- one course (20 per cent)
- two courses (40 per cent)
If you are taking three or more courses at the same time, you may be considered for full-time student grants and loans.
- three courses (60 per cent)
- four courses (80 per cent)
- five courses (100 per cent)
To find out if you qualify and to learn how to apply, please visit the OSAP website.
For information on other awards and financial assistance, please see Financial Aid.
Credit for Prior Learning
Prior Learning Assessment
Earn college credits for what you already know.
Prior Learning Assessment is a method of assessing and recognizing learning that is equal to
college level learning, but has been gained outside a traditional classroom (through work
experience, volunteering, outside study, etc.). If you can prove that the knowledge you have gained
meets the outcomes of a Seneca course, then credit will be awarded.
How does the PLA process work?
Prior Learning is demonstrated through a "challenge" process. The process measures learning
through a variety of methods which may include tests, portfolio assessment, interviews,
demonstrations, essays, and work samples. The method used will be determined in consultation with a
Program Coordinator.
For more information and to determine if you are eligible for PLA, please call the Program
Coordinator.
The process may take from 6 to 8 weeks.
Note: Not all courses can be challenged. For more information go to PLA website or contact your Program Coordinator.
Transfer Credit
Many students who enter Seneca Polytechnic will have earned academic credits in postsecondary educational institutions which they may be able to apply toward completion of a Seneca Polytechnic program.
Requests for Transfer Credit must be for a specific course and must be accompanied by an official transcript and course outline. A minimum grade of "C" (60 percent) is generally required for a course to be considered for Transfer Credit.
Download a Transfer Credit Request form. An official copy of your transcript and applicable detailed course outlines should be attached and submitted. Please note it may take 4 to 6 weeks for a Transfer Credit decision.
More Information
Please visit the Office of the Registrar.
Graduation or Completion
Graduation/Convocation
When you meet all program requirements and become eligible for a certificate, diploma, or degree, you must inform the Registrar by completing a Graduation Application form and paying the graduation and alumni fee. Certificates, diplomas, and applied degrees are issued twice a year in the Fall (October), Spring (June) and Winter (February).
For further information including deadlines and fees, please visit the Convocation website or contact the Convocation Office at theservicehub@senecapolytechnic.ca.
Minimum Performance for Graduation
A student will be eligible to graduate from a certificate, diploma, advanced diploma or graduate certificate program if they have achieved a minimum graduating GPA of 2.0.
A student will be eligible to graduate from a degree program if they have achieved a minimum graduating GPA of 2.5, which includes a minimum GPA of 2.5 in the courses in their main field of study and a minimum GPA of 2.0 in breadth courses.
Optional Work Term
Students meeting all academic requirements may have the opportunity to complete an optional work term(s) in a formal work environment. The work term(s) is similar in length to an academic semester and typically involves full-time work hours that may be paid or unpaid. In programs with limited work term opportunities, additional academic requirements and a passing grade on a communication assessment may be required for eligibility. Eligibility for participation does not guarantee a work position will be secured. Additional fees are required for those participating in the optional work term stream regardless of success in securing a work position.
Career Opportunities
As a graduate of this program, you may find employment in corporations and government organizations that have large, rich data sets and the resources to mine data. You may pursue future career options, such as:
- Business analyst
- Business consultant
- Data analyst
- Computer system analyst
- Information systems business analyst
Program Contacts
Belinda Becker
Program Assistant
belinda.becker@senecapolytechnic.ca
Lisa Ballantyne
Manager, Academic Programs
lisa.ballantyne@senecapolytechnic.ca