Description:
Recent advances in artificial intelligence, machine learning, and computing power have accelerated the use of data-driven technologies across industries such as healthcare, finance, agriculture, manufacturing, and government. Organizations increasingly rely on professionals who can transform data into insight and build intelligent systems that support better decisions and automation.
In the Applied Data Science and Artificial Intelligence program, students develop the technical and analytical skills needed to design and implement practical AI and data solutions. Through hands-on learning, students build a strong foundation in programming, mathematics, analytics, and machine learning while working with modern AI tools, automation techniques, and generative AI systems. The program also emphasizes responsible and secure AI development, including privacy-aware design, data governance, and ethical deployment of AI systems.
In this program, you will:
Graduate Profile:
Graduates of the Applied Data Science and Artificial Intelligence program are prepared to:
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Internationally Educated Applicants - visit www.rrc.ca/credentials for credential assessment information.
However, if you apply within 6 weeks of the program start date, admission requirements are due within 5 days of applying.
Regular Admission Requirements
Mature Student Admission Requirements
If you are 19 years of age or older and have been out of high school for a minimum of one year at time of application, and you do not meet the regular admission requirements, you may apply under the Mature Student admission requirements.
| Location | Start Date | Apply Link |
|---|---|---|
| Manitou a bi Bii daziigae | Sep 01, 2026 | Apply Now |
Students may apply for financial assistance through the Manitoba Student Aid program. For general information on applying please call 204-945-6321 or 1-800-204-1685, or visit their website at www.manitobastudentaid.ca, which also includes an online application. For detailed information, please visit one of the RRC Polytech Student Service Centres or call 204-632-2327. Applicants requiring financial assistance should complete their student loan applications well in advance of the class start date.
Everyone communicates, but are they doing it well? Communicative competence takes practice and self-awareness. In this foundational course, students will learn through discovery and project-based activities to practice approaching situations critically and collaboratively. By developing their communication skills, students will improve their interpersonal ability, intercultural competence, and digital fluency to prepare for success in the workplace and beyond. The strategies students will gain in this course will be useful throughout their program and in their chosen industry.
This foundational course focuses on essential communication skills for entering and advancing in industry. Students will develop skills for effective resumes, cover letters, and job interviews that are tailored to the specific needs of prospective employers. Additionally, students will enhance their interpersonal skills and digital fluency while applying speaking, writing, and collaboration techniques crucial for job searching, adapting to new roles, and achieving long-term career goals. Students will also develop strategies for continuous learning to remain competitive in an ever-changing job market.
In this information technology (IT) sector course, students will reinforce and build on the communication skills they practiced in COMM-1173 Communication Strategies. Students will develop their ability to think at a systems level by analyzing problems to come up with innovative solutions. Learners will manage, analyze, and communicate information to various audiences across different channels both individually and collaboratively. They will practice active listening, networking, planning, and presenting in an IT context. This course focuses on communication strategies and enhancing practical skills, preparing learners to integrate and apply their skills in their industry.
This course is intended to serve as an introduction to programming concepts. Students will be introduced to high-level modeling and common numeral systems used by computer programmers. Boolean operations will be explored with importance placed on the student’s ability to analyze, interpret and re-write word problems as Boolean expressions. Students will explore other core concepts such as assignment, sequence, iteration, decision, modular abstraction, arrays, and strings.
This is a data-focused course to develop confidence with quick data handling, parsing, structuring, and manipulating datasets for various database types. By viewing, understanding, and normalizing datasets, students will produce Entity Relationship Diagrams (ERDs) and other visual data schemas. Students will learn basic Structured Query Language (SQL) and NoSQL (not only SQL) data types, key-value pairs, and document stores. Students will develop basic to advanced commands including complex JOINs, advanced mathematical and string functions, and full-text search indexing functions. Students will tune the performance and execution times of queries using common practices of indexing and de-normalization.
In this course, students will be introduced to the fields of Data Science and Machine Learning (DSML) and how they are used in real business applications. Students will get an introduction to the industry standard tools and technologies used in this field and learn definitions and meanings of common terms. They will analyze real case studies of how industry has applied the tools of DSML to improve their performance. By the end of this course, students will be able to contrast how DSML tools have impacted performance metrics in industry, compared to conventionally used methods.
Learn the fundamentals of Python programming and data analytics. Starting with the fundamental building blocks, this course will focus on teaching Python programming fundamentals before moving to more comprehensive examples. The course will also introduce students to data science and machine learning as they are used in business applications. Using tools such as the Jupyter Notebook, NumPy, Pandas, Matplotlib and Seaborn, you will learn about the basics of interpreting and preparing data for analysis.
This course covers steps to manipulate and manage data from raw source formats to functional structures where it can be exploited more readily as a valuable information asset. Students will learn industry standard techniques to inspect and visualize data for statistical, aggregate, and design pattern characteristics, and then manipulate the data into suitable representations within relevant data genre models that include relational, document, and network databases. Students will also learn methods to maintain data security using encryption, anonymization, sanitization, roles access, and walled infrastructures. Furthermore, learners will acquire competencies in maintaining data integrity through versioning, backups, archiving, and restoration approaches at various stages of an established data pipeline.
In this course, students will explore the principles and practices of secure, privacy-first artificial intelligence (AI) and data systems. Focusing on applied techniques rather than underlying theory, students will learn to design, deploy, and manage AI systems that prioritize data sovereignty, cybersecurity, and ethical considerations. Key topics include secure AI model development, privacy-preserving tools, threat mitigation, and compliance with regulatory frameworks. Students will apply these concepts using industry-relevant tools to solve real-world challenges in data science and machine learning. This course reinforces foundational knowledge from prior program courses while introducing culminating skills for professional practice in secure AI deployment.
Supervised machine learning is a subfield of machine learning where algorithms are trained on labelled data to classify items or predict outcomes. This course builds upon concepts to describe how supervised learning algorithms are constructed and coded. Students will use Python to develop the code for supervised learning algorithms including polynomial regression, support vector machines and decision trees; data will be used to train, validate and test these models for common use cases in business and data science.
This course bridges theoretical artificial intelligence concepts with real-world applications in data science and industry. Students explore applied AI techniques, including generative AI, large language models (LLMs), and agentic workflows. Learners examine industry uses of AI across sectors such as manufacturing, retail, and healthcare while considering responsible AI practices, including privacy-preserving methods and data sovereignty. Through hands-on projects, students design AI pipelines, integrate generative AI tools with coding agents, and deploy open source AI solutions. By the end of the course, students will be prepared to implement practical AI systems that meet organizational needs while addressing ethical, operational, and regulatory considerations.
In this course, students will create blueprints for data management systems, identify potential data sources (internal and external), and create a plan to integrate, centralize, protect and maintain information and data.
Deep learning is one of the most important recent advancements in machine learning, with an ever-growing list of applications that include finance, medicine, computer vision, and language processing. The course first introduces the perceptron as a fundamental building block before moving onto more complicated neural network architectures. Students learn how leading architectures are constructed from tools in linear algebra and how to develop, train and test these networks using code.
Unsupervised machine learning is a subfield of machine learning where models are trained to identify clusters and find relationships in unlabelled data. This course builds upon concepts from previous courses to describe how unsupervised learning algorithms work, as well as how they are constructed and coded. Students will use Python to develop the code for clustering models, Autoencoders and topic models; real data will be used to train, validate and test these models for common use cases in business and data science.
This course is an introduction to the exciting field of robotics and automation. Working with Robot Operating System (ROS2) locally and cloud services like AWS RoboMaker, students will gain experience with important concepts such as vision, motion control and processing sensor data. Students will learn how Robot Operating System interacts with and controls physical hardware.
An understanding of statistics is fundamental in the study of data science and machine learning. This course is designed to familiarize students with sampling methods and estimations, presenting and describing data, probabilities and hypothesis testing.
Recognition of Prior Learning (RPL) is a process which documents and compares an individual's prior learning gained from prior education, work and life experiences and personal study to the learning outcomes in College courses/programs. For more information, please visit www.rrc.ca/rpl.
To graduate, students need to meet these requirements:
• A minimum overall program GPA of 2.0 (as per RRC Policy A12)
• A minimum passing course grade requirement of D (50%)
• Students need to complete all compulsory courses
To graduate from Applied Data Science and Artificial Intelligence, all students must complete a total of 13.5 full-course equivalents and one term of Work Integrated Learning for a total of 87 credit hours within six years of the date of your initial enrolment. You are responsible for ensuring you take the appropriate courses to meet the requirements for graduation.