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

• Two year diploma program
• August entry date
• Exchange District Campus, Winnipeg
• Mandatory Work Integrated Learning term (Co-op work experience or industry project)
• Classes take place between 8 am and 6 pm
• Classes are held on campus on Mondays, Tuesdays, and Wednesday mornings and online on Wednesday afternoons, Thursdays, and Fridays
• You need to provide your own laptop computer that meets the requirements for the program
• International applicants please visit Academic Program, Dates and Fees for a listing of programs for international students, current availability and online application instructions.

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:

• 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.

Graduate Profile:

By the end of the program a Data Science and Machine Learning graduate should be able to:

• Determine appropriate machine learning techniques based on the problem domain data and identified goals.
• Determine programming language appropriate to the goal or project.
• Conduct research by completing a literature review, collaborating with others and using other research techniques as required to acquire data, domain knowledge and summarize existing approaches and techniques in a domain area.
• Train industry standard machine learning models to establish predictive relationships between data inputs and desired outputs that remain effective and accurate when presented with new unseen data.
• Prepare data for use in machine learning models through data revision and quality improvement in order to interpret and draw conclusions from statistical analysis of visualized and contextualized data.
• Create a software product that uses machine learning and software development skills appropriate to identified goals.
• Verify and validate a software product to ensure that it meets specifications and fulfills its intended purpose.
• Communicate effectively in all interactions by using active listening as well as written, verbal and non-verbal communication skills (power skills), reading technical literature and documentation of processes.
• Manage data in compliance with regulatory standards through continuous learning from regulatory bodies and ethical behavior.
• Demonstrate professionalism, personal integrity and accountability in all roles and responsibilities, maintaining professional and ethical standards and accreditation as necessary.