The Data Science Co-Op Diploma course is designed to equip students with the skills necessary to build a career in Canada. Students will learn the fundamentals of data, how to leverage tools, methods of analysis, big data concepts, and machine learning, allowing them to acquire practical skills.
Academic Education (Part-time Work Permit)
Paid Co-op (Full-time work permit)
In this course, students will learn how to use Python to collect, analyze and communicate large data sets with industry-standard tools. Students will also learn some of the challenges associated with working with datasets that are becoming ever larger and faster moving.
Our data science diploma courses will help students to master the following skills below:
Why is Data Science a Good Career?
Data Science jobs are some of the fastest-growing, most in-demand in technology. Since 2012, Data Scientist roles have increased by 650%, and this rise shows no sign of stopping. The U.S. Bureau of Labor Statistics predicts that the demand for data science skills will increase another 27.9% by 2026.
Average Junior Data Scientist Salaries in Canada: $74,713/ year
The professional route that students take will depend on their area of specialization, as well as their interests and comfort levels with various programming languages and frameworks.
Cornerstone will work with each student to discuss their strengths and experience in order to guide them to find a job in the Canadian data science industry.
This course will equip students with a comprehensive understanding of Data Science techniques, databases, and data visualization.
Gain a comprehensive overview of the Python language and its ecosystem. It covers topics such as testing and debugging code, Git and version control, and interactive data visualization using Python.
We will offer you an introduction to SQL and Relational Databases. Students will concentrate on setting up, aggregating, and grouping data.
Acquire an in-depth understanding of Big Data, including its definition and its profound impact on the industry.
This course will give students a basic understanding of data science analytics. You’ll learn about statistics, AB Testing, and how to tackle common data science challenges.
This topic offers students a comprehensive introduction to Machine Learning techniques, covering data modeling, dataset preparation, linear regression, and model evaluation. By the end, students will have a solid grasp of the Machine Learning field and the ability to apply basic models for running their own algorithms
This is the chance for students to apply the skills acquired in the Data Science Program, enabling them to create data visualization tools for presenting their project findings.
Data Analyst: This role involves analyzing data to extract insights, typically using tools like Excel, SQL, and possibly some programming in Python or R. Data visualization tools like Tableau or PowerBI may also be employed.
Junior Data Scientist: Some companies might be open to hiring candidates with a 2-year diploma, especially if the curriculum was rigorous and the candidate has strong foundational knowledge.
Business Intelligence Analyst: Focused more on business metrics and KPIs, this role often uses tools like SQL and BI platforms to create dashboards and reports.
Data Engineer (Entry Level): If the diploma covers data engineering concepts, diploma holders can look into junior roles dealing with databases, ETL processes, and data pipelines.
Machine Learning Engineer (Entry Level): For diplomas that dive deep into machine learning, candidates might find roles that focus specifically on building and deploying ML models.
Data Visualization Specialist: For those with a knack for visual design and data presentation.
** Students are required to have their personal computer
Check what our student’s squad say about us
| SEASON | START DATES |
| Spring | Apr 29th, 2024 |
| Summer | September 3rd, 2024 |
| Winter | January 2nd, 2024 |
Data Science and AI (Artificial Intelligence) are closely related disciplines.
There is an overlap in Tools and Techniques that they use. Machine Learning (ML) is a bridge and a subset of AI that focuses on developing algorithms that allow machines to learn from the act of data.
While there’s a significant overlap, especially through machine learning, AI has a broader scope. AI also includes areas like robotics, expert systems, natural language processing, and cognitive computing etc.
