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

Courses and Descriptions

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.

Prerequisites:
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. 

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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. 

Prerequisites:
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.

Prerequisites:
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.

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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.

Prerequisites:
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.

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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.

Prerequisites:
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. 

Prerequisites:
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. 

Prerequisites:
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.

Prerequisites:
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.

Prerequisites:
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.

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