Description:
Recent advancements in computer hardware and machine learning algorithms have driven a rapid growth in the use of data science and machine learning across all economic sectors, with applications in robotics and automation, healthcare, finance, and government. Because of this, there is now a huge demand for developers and data analysts with skills and experience in these fields. In the Data Science and Machine Learning program, you will:
Graduate Profile:
By the end of the program a Data Science and Machine Learning graduate should be able to:
DOCUMENT SUBMISSION
Upload Through Your Future Student Account
If you do not have a Future Student Account or require assistance, please contact our Student Service Centre at 204-632-2327.
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.
| English Language Assessment | Minimum Required Levels | |||
|---|---|---|---|---|
| L - Listening, S - Speaking, R - Reading, W - Writing | L | S | R | W |
| AEPUCE
(Academic English Program of University and College Entrance )
Requirement: Submission of a parchment (certificate) indicating successful completion of the AEPUCE program, including language levels achieved if available. | 8 | 8 | 8 | 7 |
| CAEL and CAEL Online (Canadian Academic English Language) | 60 | 60 | 60 | 60 |
| CLB (LINC)
(Canadian Language Benchmark - Language Instruction for Newcomers to Canada)
Canadian Citizens: LINC programs are not available. | 8 | 8 | 8 | 7 |
| Duolingo
(Duolingo English Test)
* MINIMUM OVERAL SCORE OF 115 REQUIRED. There are no minimum required levels for L,S,R,W. Only Duolingo English Test scores that have been verified through the Duolingo English Test Portal will be accepted. | 0* | 0* | 0* | 0* |
| IELTS - Academic
(International English Language Testing System)
Please Note: 3 year expiry date for Nursing Program Applicants | 6.5 | 6.5 | 6.5 | 6.0 |
| LSI (Language Studies International) | 6.5 | 6.5 | 6.5 | 6.5 |
| PTE - Academic Online Assessment (Pearson Test of English) | 58 | 58 | 58 | 50 |
| Password Skills
(An in person English Language Assessment hosted by RRC Polytech)
This in-person, computer-based test is composed of four test modules: reading, writing, listening and speaking. The test takes 3 hours and 5 minutes to complete and is conducted in two parts. The first part assesses reading, listening, and writing, and the second part assesses speaking in a separate room.
Password Skills is hosted by the RRC Polytech Testing and Assessment Centre: E440, Manitou a bi Bii Daziigae building, on the fourth floor.
If you wish to do Password Skills remotely (not in-person), Password Skills Plus can be taken online. RRC Polytech does not offer Password Skills Plus, but we do accept the results for entry into program.
| 6.5 | 6.5 | 6.5 | 6.0 |
| Password Skills Plus (Password Skills Plus is an online assessment that can be taken instead of Password Skills. ) | 6.5 | 6.5 | 6.5 | 6.0 |
| TOEFL-iBT
(Test of English as a Foreign Language - internet Based Test)
To meet the needs of students who are unable to take the TOEFL iBT® test at a test center due to public health concerns, ETS is temporarily offering the TOEFL iBT Special Home Edition test in selected areas. | 20 | 20 | 19 | 21 |
| Location | Start Date | Apply Link |
|---|---|---|
| Manitou a bi Bii daziigae | Aug 25, 2025 | 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.
Students will build on the skills they practiced in Communication Strategies by focusing on the information technology sector. Students will develop their ability to think at a systems level by analyzing problems to come up with innovative solutions. Learners will collaborate to manage, analyze, and communicate information to various audiences across different channels. This collaboration will involve active listening, networking, and persuasion strategies in an information technology context.
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.
This course is an introduction to some of the basic techniques and algorithms of bioinformatics through coding challenges in an industry standard programming language. Topics covered include locating ori-C in small genomes, finding regulatory motifs in small genomes, graph algorithms, and the genome reconstruction problem.
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.
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.
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.
Artificial intelligence (AI) is the ability of computers to learn from data and make decisions by running code. In this course, you will learn the role of logic and probability in AI algorithms, and how statistical machine learning and neural networks are used. These tools will be applied in the completion of course projects where you will develop code for important AI use cases.
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.
A critical component of your education is the opportunity to integrate course theory with real life learning. One term of your program will be dedicated to a Work Integrated Learning (WIL) experience, either a Co-op Education work experience or an industry project at the ACE Project Space. This is normally done after students have completed all courses in the first three terms of the program.
Co-operative Education (Co-op Ed) integrates related on-the-job experience with classroom theory by incorporating a term of paid employment within the academic program. The employer, the student and the College form a partnership to extend the learning process beyond the College into the professional business world. It is a proven training system where everyone shares in the benefits. A limited number of co-op positions are assigned through a competitive process each term. A Co-op tuition fee is charged to all students registered in a Co-op work term to cover work placement development, pre-employment instruction, and employment-related monitoring.
Industry Project provides students with experience working as a team to create a real-world IT solution in the ACE Project Space. Students may be assigned to work with an Entrepreneur in Residence to develop a product for a startup company or an application for a small business or non-profit organization. Students work in cross-functional teams using the Agile methodology for project management. This means students work closely with customers and need to meet deadlines. A fee is charged to all students registered in an industry project term.
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 Data Science and Machine Learning, all students must complete a total of 14.5 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.