About the Program
The Honours Bachelor of Data Science and Analytics is a four-year degree program designed to meet the growing need for data-driven business solutions. You’ll gain the expertise to identify, analyze and interpret data using advanced algorithms, and develop the skills to draw meaningful insights that drive strategic outcomes.
The program places a strong emphasis on the integration of artificial intelligence (AI), equipping you with the ability to apply machine learning techniques and intelligent systems to solve real-world challenges. Your learning will be enhanced through a blend of theoretical knowledge and hands-on training, including case studies, guest speakers and a co-op work experience.
As a graduate of this program, you will be uniquely positioned to make an impact by combining solid foundation in business strategy with an in-depth knowledge of computer science, statistics, analytics and AI.
Credential Awarded
Honours Bachelor Degree
Duration
8 Semesters (4 Years)
Program and Course Delivery
This program is offered in Seneca's hybrid delivery format with some courses available in Seneca's flexible delivery format. Some coursework is online and some must be completed in person. Students will need to come on campus to complete in-person learning requirements. For courses offered in the flexible delivery format, professors use innovative learning spaces and technology to teach students in a classroom or lab and broadcast in real time to students attending remotely. In flexible courses, students have the choice of coming on campus or learning online.
Skills
Throughout this program you will develop the following skills:
- Management skills that demonstrate initiative, personal responsibility, and accountability to work in the field of data science
- Communication and negotiation skills to work in diverse inter-professional group environments
- Problem solving, reasoning, and critical thinking skills to engage in evidence-informed and ethical practice and decision-making
- Technical skills in machine learning and AI, including the ability to apply AI models to analyze complex datasets and automate data-driven solutions across various domains.
Work Experience
Mandatory Degree Co-op
This program includes a mandatory co-op work term to provide you with valuable hands-on experience in your field of study. A mandatory co-op is typically a paid position that is completed between two academic semesters. It is similar in length to an academic semester and requires a minimum of 420 hours of work experience. You must be in good standing and meet all identified requirements prior to participating in the work experience. Successful completion of the co-op work term(s) is required for graduation.
The co-op search is student-driven and participation does not guarantee that a work position will be secured. However, you will receive guidance and support through in-class career workshops and one-on-one coaching to help prepare for the co-op term. Additional fees are required for programs with mandatory co-op, regardless of success in securing a work position. For more information, see fee details.
The co-op work term can be completed in person, online or in a hybrid format, depending on the hiring organization.
Work-Integrated Learning Model
Academic Schedule
| Year |
September |
January |
May |
| Year 1 |
Semester 1 |
Semester 2 |
Break |
|
|
OR |
|
| Year 1 |
n/a |
Semester 1 |
Semester 2 |
| Year 2 |
Semester 3 |
Semester 4 |
Break |
| Year 3 |
Semester 5 |
Semester 6 |
Work Term |
| Year 4 |
Semester 7 |
Semester 8 |
|
Your Career
As a graduate of this program, you may pursue future career options, such as:
- AI specialist
- business data strategist
- business intelligence analyst
- data analyst
- data architect
- data engineer
- data scientist
- database developer
- predictive modeler
- quantitative analyst
Program of Study
Course List | Course Code | Course Name | Weekly Hours |
| BDA100 | Introduction to Data Science | 3 |
| BDM100 | Discrete Mathematics | 3 |
| BDM110 | Introduction to Calculus | 3 |
| BDM150 | Statistical Methods for Data Science | 3 |
| BDP100 | Introduction to Programming | 3 |
| ENG106 | Writing Strategies | 3 |
| BDA200 | Security, Privacy and Ethics in Data Science | 2 |
| BDD200 | Structured Database Design | 3 |
| BDM200 | Mathematical Methods for Data Science | 3 |
| BDP200 | Programming for Data Science | 3 |
| LSO440 | Globalization in the 20th Century and Beyond | 3 |
| BDA300 | Data Preparation | 3 |
| BDA350 | Introduction to Algorithms and Analyzing Data | 3 |
| BDD300 | Advanced Database Design | 3 |
| BDM300 | Data Mining | 3 |
| 3 |
| BDA420 | High Performance Computing | 3 |
| BDA450 | Simulation and Modelling | 3 |
| BDB400 | Business Intelligence I | 3 |
| BDM400 | Introduction to Data Visualization | 3 |
| 3 |
| BDA500 | Machine Learning | 3 |
| BDB500 | Strategic Analysis and Evidence Based Decision-Making | 3 |
| BDM500 | Predictive Analytics | 3 |
| BDM550 | Text Mining | 3 |
| LSP400 | Presentation Skills | 3 |
| WTP200 | Work Term Preparation | 1 |
| BDA600 | Social Media Analytics | 3 |
| BDA620 | High Performance Computing | 3 |
| BDB650 | Project Management | 3 |
| BDM600 | Advanced Data Visualization | 3 |
| 3 |
| DSA771 | Data Science Analytics, Co-op | 35 |
| BDA700 | Health Analytics | 3 |
| BDA720 | Advanced Machine Learning | 3 |
| BDA730 | Machine Learning Operations | 3 |
| BTM710 | Research Methods | 3 |
| 3-4 |
| 3 |
| BDA850 | Intelligent Systems Analytics | 3 |
| BDB810 | Risk Management | 3 |
| BDC800 | Capstone Project | 3 |
| 3-4 |
| 3 |
Professional Options
Course List | Course Code | Course Name | Weekly Hours |
| BTH745 | Human-Computer Interaction | 4 |
| BTM600 | Digital Entrepreneurship | 3 |
| DPS920 | Computer Vision | 4 |
| DPS950 | Introduction to Microsoft Cloud Technologies | 4 |
| QUC955 | Quantum Computing | 4 |
| SEA820 | Natural Language Processing | 4 |
| SEA850 | Agentic AI | 4 |
Seneca has been granted consent by the Minister of Colleges, Universities, Research Excellence and Security to offer this degree for a seven-year term starting Nov. 4, 2025. In conformity with the Minister's criteria and requirements, Seneca will submit an application for the renewal of the consent for this program 12 months prior to the expiration of the consent. Seneca shall ensure that all students admitted to the above-named program during the period of consent will have the opportunity to complete the program within a reasonable time frame.
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:
- Prepare data for analysis by gathering, cleaning, and storing it into application specific data models.
- Generate knowledge through the analysis of big data sets by using statistical, mathematical, and computational methodologies and techniques.
- Analyze and visualize datasets using software tools, data models and algorithms to inform tactical and strategic business decisions.
- Apply visualization techniques and communications technologies to present analytical findings for a range of audiences.
- Conduct research to provide evidence to support data-driven decision-making and alignment with organizational strategy.
- Adhere to ethical and legal guidelines to ensure data security, integrity, and confidentiality in the delivery of data-driven business intelligence.
- Apply interpersonal, teambuilding, and leadership skills when participating in diverse organizational environments.
- Apply principles and techniques of artificial intelligence, including machine learning, deep learning and generative AI, to analyze real-world datasets, develop predictive models and make data-driven ethical and equitable decisions.
Ontario Secondary School Diploma (OSSD) or equivalent, including six Grade 12 U or M courses with a minimum overall average of 65%, or a mature applicant.
Required courses with minimum final grade of 65% in each:
- English: Grade 12 ENG4U
- Mathematics: any two Grade 12 U (MDM4U and MHF4U are recommended)
International Student Information
International admissions requirements vary by program and in addition to English requirements, programs may require credits in mathematics, biology, and chemistry at a level equivalent to Ontario’s curriculum, or a postsecondary degree or diploma, equivalent to an Ontario university or college. Program-specific pre-requisite courses and credentials are listed with the admission requirements on each program page. To review the academic requirements please visit: Academic Requirements - Seneca, Toronto, Canada (senecapolytechnic.ca).
Advanced entry offers a pathway for graduates from the following Ontario diploma/advanced diploma programs with a GPA of 65% or higher:
Pathways
As a leader in academic pathways, we offer a range of options that will allow you to take your credential further in another Seneca program or a program at a partner institution.
To learn more about your eligibility, visit the Academic Pathways web page.
