FULL-TIME | WINNIPEG LOCATIONS DATSF-DP Data Science and Machine Learning

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  • 20-month diploma program
  • August entry date
  • Innovation Centre/Exchange District Campus, Winnipeg
  • Mandatory Co-op work experience or industry project
  • Laptop delivery program
  • International applicants please visit Academic Program, Dates and Fees for a listing of programs for international students, current availability and online application instructions.

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 to name just a few. 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:

  • Study the fundamental concepts in mathematics and statistics that make these technologies possible.
  • Gain the skills to collect / organize data and use analytics to inform decisions.
  • Implement current machine learning algorithms to address common needs in industry.
  • Develop the skills to effectively communicate technical ideas with other developers as well as those without technical knowledge.
  • Experience working with industry to develop code for real applications in data science and machine learning.

Graduates leaving this program will be well prepared to immediately enter a career in this exciting, important and rapidly growing field.

Admission Requirements

Your Academic History
If your academic history includes any of the following, please visit My Education for important information: post-secondary studies at an institution other than Red River College Polytechnic; Modified (M), English as an Additional Language (E), or GED high school courses; or home schooling.


Upload Through Your Future Student Account (preferred method)

  • Scan your document(s) and save the file. Ensure you keep your original documents as the College may request to see them at any time.
  • Go to apply.rrc.ca and log in.
  • Click on your application, then Supplemental Items & Documents.

If you do not have a Future Student Account or require assistance, please contact our Student Service Centre at 204-632-2327.

Mail or In-Person: Student Service Centre Click here for address and hours of service

E-mail: register@rrc.ca

Fax: 204-697-0584

Internationally Educated Applicants - visit www.rrc.ca/credentials for credential assessment information.

Submission of required documentation indicating proof of completion of admission requirements is due within 30 days of applying unless otherwise noted in the program's admission requirements.

However, if you apply within 6 weeks of the program start date, admission requirements are due within 5 days of applying.

Regular Admission Requirements

  1. Grade 12
    • Submit proof of graduation from or enrolment in Grade 12, including one credit in each of the following:
      • Grade 12 English 
      • Grade 12 Math (excluding Accounting 40S)
    • If you provide proof of enrolment at time of application, your official final grades indicating successful completion must be submitted by July 15 for fall enrolment or by the deadline specified in your admission letter.
    • If you are required to complete an English language assessment, do not submit your transcripts until requested to do so.  See English Language Requirements (ELRs) for more information. 
  2. English Language Requirements (ELRs)
    • Answer this question to determine if you meet this program’s ELRs:
      Have I successfully completed 3 years of full-time high school (secondary) education in Canada, the United States, or an ELR exempt country where English was the language of instruction?
      • If YES, you meet English language requirements.  Apply and then submit your transcripts* for review
      • If NO, submit proof of meeting an ELRs option.  If you choose the English language assessment option, review this program's approved assessments and required levels.
      • If you completed all of your education in Canada, the United States, or an ELR exempt country in English but you did not graduate high school, submit your transcripts* for review. 
    • * If your transcripts are from the USA or an ELR exempt country, we will assess an International Credentials Assessment Fee to be paid before your transcripts will be reviewed.

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.

  1. Academic Requirement
    • High school graduation is not required, but you must have successfully completed or be enrolled in one credit in each of the following:
      • Grade 12 English
      • Grade 12 Math (excluding Accounting 40S)
    • If you provide proof of enrolment at time of application, your official final grades indicating successful completion must be submitted by July 15 for fall enrolment or by the deadline specified in your admission letter.
    • If you are required to complete an English language assessment, do not submit your transcripts until requested to do so.  See English Language Requirements for more information. 
  2. Meet Regular Admission Requirement 2

English Language Assessments

English Language AssessmentMinimum Required Levels
L - Listening, S - Speaking, R - Reading, W - WritingLSRW
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.
CAEL and CAEL Online (Canadian Academic English Language)
CLB (LINC) (Canadian Language Benchmark - Language Instruction for Newcomers to Canada)
Canadian Citizens: LINC programs are not available.
CanTEST (Canadian Test of English for Scholars and Trainees)
As of October 20, 2021 the RRC CanTEST is no longer offered at RRC Polytechnic. RRC Institutional CanTEST results dated within 2 years of your application date will still be accepted to meet English language proficiency requirements. Please note the Medical Laboratory Sciences (MLS)program requires the Official CanTEST (The RRC Institutional CanTEST will not be accepted)
Duolingo (Duolingo English Test)


There are no minimum required levels for L,S,R,W.

Due to closures related to COVID-19, RRC is temporarily allowing applicants to provide Duolingo English Test results to meet RRC's English language requirements.

Red River College will accept Duolingo assessments up to December 30, 2021. Beginning December 31, 2021 we will no longer accept Duolingo assessments to meet English language requirements.

IELTS - Academic (International English Language Testing System)
LSI (Language Studies International)
PTE - Academic Online Assessment (Pearson Test of English)
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.

Locations, Dates and Fees

Next Estimated Term 1 Start Date (subject to change)

Location Start Date Apply Link
Innovation Centre Aug 28, 2023 Apply Now

Costs (estimates only; subject to change)

Program/Student Fees
Year 1
Year 2
Books and Supplies
Year 1
Year 2
Program/Student Fees (International)
Year 1
Year 2
1Amount only includes Tuition and is estimated. Auxiliary fees TBD.
2Amount only includes Tuition and is estimated. Auxiliary fees TBD.
3Includes an estimate of $1600 for the purchase of a laptop
4Amount only includes Tuition and is estimated. Auxiliary fees TBD.
5Amount only includes Tuition and is estimated. Auxiliary fees TBD.

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.

Red River College Polytechnic is a participating institution in the HigherEdPoints program. Through this program, students are able to convert Aeroplan® Miles and TD Points into funds to help cover their tuition. Family members and friends can also contribute to a student’s education by converting their loyalty points - anyone can donate their points to an individual student.

Visit the HigherEdPoints website for more information about the program and/or to set up an account to convert your points.

Courses and Descriptions

(Click the course name to view the description of the course)
Recognition of Prior Learning (RPL)
In addition to Transfer of Credit from a recognized post secondary institution, other RPL processes are available for RPL courses. Click here for more information. For courses with no RPL, please check www.rrc.ca/rpl for additional contact information.
COMM-1173Communication Strategies
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Everyone communicates, but are they doing it well? Communicative competence takes practice and self-awareness. By developing their communication skills, the student will improve their interpersonal ability, intercultural competence, and digital fluency to prepare the student for success in the workplace. In Communication Strategies, the student will learn through discovery and project-based activities to practice approaching situations critically and collaboratively. The strategies the student will gain in this course will be useful throughout their program and in their chosen industry.

COMM-2172Communication for the Workplace
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Welcome to an immersive experience that will give students hands-on practice in finding, getting, and keeping the job they want. Students will enter through the "Employment Centre", move to an active "Probation Period", and close with a meaningful "Performance Review". This course is a creative and participatory workplace preparation designed to give students a head start in today's competitive job market.

COMM-2176Communication for Systems and Innovative Thinking
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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. 

COMP-1296Introduction to Programming Logic
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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. 

COMP-1701Transforming Data Into Databases
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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. 

COMP-1701 and COMP-2702 are corequisites
COMP-1702Introduction to Data Science and Machine Learning
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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. 

COMP-2036Introduction to Bioinformatics
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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.  

COMP-2040Python Essentials With Data Analysis
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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.

COMP-2702Data Management
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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.

COMP-2704Supervised Machine Learning
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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.

COMP-3702Information and Data Architecture
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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.

COMP-3703Introduction to Artificial Intelligence
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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.

COMP-3704Neural Networks and Deep Learning
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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. 

COMP-3705Unsupervised Machine Learning
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Students will build on techniques used in previous courses to learn unsupervised machine learning approaches. These approaches are used to find patterns in complex sets of unlabeled data, possibly high dimensional (Unlabeled data is data with no predefined target attributes). Students will learn techniques of component analysis and clustering methods including K-Means clustering along with different practical issues in clustering. Students will use a programming language such as Python to carry out these methods. 

COMP-3706Robotics and Automation
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This course is an introduction to the exciting field of robotics and automation. Students will learn how machine learning is being applied to improve current practices. Working with code, students will gain experience with important concepts such as vision, grasping, motion control and processing sensor data. Students who complete this course will develop an understanding of expert systems and control systems.

COOP-4001Data Science and Machine Learning Co-op
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Co-operative education integrates related on-the-job experience with classroom theory by incorporating a term of paid or unpaid employment within the terms of academic study. Students are given the opportunity to practice and apply the skills gained during the academic semesters of their program as productive full-time employees on their work term. Students are provided with an intense 4-week program of job search and resume development workshops to prepare them for the recruitment process. Placement of eligible students occurs in either January or May. Each work placement is a minimum of 16 weeks. Student performance will be monitored and evaluated by both the department and the employer. Each student will participate in a midterm review of their employment midway through the semester.

MATH-1202Statistics for Data Science and Machine Learning
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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. 

MATH-1204Linear Algebra for Data Science and Machine Learning
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This course is a gentle introduction to the topics of linear algebra. Students begin with a review of foundational concepts in algebra and graphing linear equations before moving on to the core topics of geometry, vectors and matrices. By the end of this course, students will understand how vectors can represent data, and how matrix operations and are used to manipulate this information and obtain results. 

PROJ-4001Data Science and Machine Learning Industry Project
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The Industry Project option provides real world experience in applying data science and machine learning skills to a project requiring cross-functional teamwork while meeting client requirements and completing deliverables outlined in the project charter. Project teams will work jointly with industry partners (including Entrepreneurs-in Residence) at the ACE Project Space facility. Each project team will evaluate, analyze, plan, research, model, design, document, develop, test, and manage a project. Project requirements could include new development, applied research, or enhancing the functionality of an existing system. This option also provides practice to further develop soft skills that includes interpersonal, verbal, and written communication through teamwork and collaboration with project stakeholders. All team members will enhance their critical thinking, problem solving, research, independence, and life-long learning skills.


Computer/Laptop Requirements

The use of laptop computers is an integral part of this program. It will enhance your learning and competitiveness in the job market. This universal-access approach to learning is a shared one between students and the College. Laptop and software specifications will be provided to you by the College after you are accepted into the program to ensure the laptop complies with the program requirements.

You will be required to purchase a laptop computer with operating system for use throughout the program. Laptop and software specifications will be provided to you by the College after you are accepted into the program.

Do not purchase a laptop prior to receiving this information to ensure your laptop complies with program requirements.

Laptop Requirements

Please contact our Department IT Specialist for any questions you may have about the laptop requirements:

Mitch Lazarenko:
• 204-232-1019 (call or text)

The Data Science and Machine Learning program will be utilizing a virtual desktop environment to provide students with access to any software packages necessary to complete the program. The College will also provide on-campus access to e-mail, College networks, Internet, and help desk support if you require assistance.

Off-campus access to the Internet is the responsibility of the student.

Please refer to https://www.rrc.ca/future-students/computer-requirements/ for further information on Computer Requirements for Students.

Recognition of Prior Learning

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.

Graduate Employment Report

Red River College Polytechnic surveys its graduates on an annual basis to collect data related to the graduates’ employment status, salary, occupation and skill use. In addition, graduates are asked to indicate their level of satisfaction regarding the education they received at Red River College Polytechnic.

Visit www.rrc.ca/numbers/reports/graduate-satisfaction for graduate satisfaction and employment reports.

Graduation Requirements

For students registered in the Data Science and Machine Learning program with a 2021-2022 Catalogue year, the requirements to graduate are as follows:

• 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 1 work experience for a total of 87 credit hours within five years of the date of your initial enrolment. You are responsible for ensuring you take the appropriate courses to meet the requirements for graduation.

You must submit an application to graduate in your final term.

Contact Information

For general information about this program or how to apply please contact:
Enrolment Services
Tel: 204-632-2327

For international students please contact:
International Education:
Tel: 204-632-2143

For detailed program information, contact:
Applied Computer Education Department

College Support Services

Red River College Polytechnic is committed to student success and provides valuable support services to assist in helping students make the most of their time at RRC Polytech.

Visit www.rrc.ca/supports for more information.

Academic Advising Service
Our academic advising service can provide information about our full-time programs, explain program admission requirements, and help you select the right program to meet your career and academic goals. We can also connect you with helpful people, resources, and supports.
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Red River College Polytechnic endeavours to provide the most current version of all program and course information on this website. Please be advised that classes may be scheduled between 8:00 a.m. and 10:00 p.m. The College reserves the right to modify or cancel any course, program, process, or procedure without notice or prejudice. Fees may change without notice.