Technology

Data Analysis and AI Specialist (DAAS)

The Data Analysis and AI Specialist diploma prepares you to succeed in a rapidly growing field. Over 61 weeks plus an 8-week internship, you will build technical expertise and hands-on skills to turn data into insights valued across business, finance, technology, healthcare, and government sectors.

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Type

Diploma

Duration

61 Weeks

Internship

8 Weeks

Program Outline

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About Our Program

In today’s digital economy, data drives decision-making in every sector, from healthcare and finance to marketing and government. The Data Analysis and AI Specialist program at triOS College equips you with a strong technical foundation and applied skills to obtain, clean, analyze, and interpret information, as well as design predictive models and build AI-driven solutions.

Throughout the program, you will work with Python, SQL databases, machine learning, computer vision, and predictive analytics, applying these tools to real-world scenarios. Two capstone projects give you the opportunity to demonstrate your ability to transform raw data into practical solutions that organizations can use.

In addition to your technical training, you will also build essential career-ready skills in digital literacy, professional networking, ethics, and communication, helping you graduate confidently and workforce-ready.

As a graduate of this program, you will also be eligible to pursue the CompTIA DataX certification, an industry-recognized credential that enhances your professional profile and career opportunities.

Click here to learn more about AI and how it is used in our technology programs

Career Opportunities

As a graduate of the Data Analysis and AI Specialist program, you may qualify for positions such as:

Data Analyst
Business Intelligence Analyst
AI Specialist
Data Analyst / Database Administrator
Data Engineer
Machine Learning Engineer

Employers Who Have Hired triOS Grads

Graduates of triOS College have been hired by leading employers, including:

Nexxus
Siemens IT
TD Canada Trust
JD Irving
RICOH
Bell
IBM

Salary

$ 47

Average Wage/HR**

$ 79

High Wage/HR**

**Employment Rate based on 2023 contactable triOS Business Faculty graduates employed in a related field within 12 months.

Source: workingincanada.gc.ca

NOC Code: 11202- **Wage data rounded down to the nearest dollar. Average wage doesn't reflect the starting salary but represents the middle value between lowest to highest wages. Local (or regional) income may vary. Last updated in March 2025.

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Program Details

The Data Analysis and AI Specialist program combines technical training with applied projects to prepare you for a competitive career in data and AI. You will complete 61 weeks of guided learning plus an 8-week internship, attending 20 hours per week with 12 hours in class and 8 hours of asynchronous lab time. This structure gives you the classroom foundation and professional experience employers are looking for.

Key program highlights include:

Training in Python and SQL for data analysis
Data visualization and analytics to turn complex information into insights
Machine learning and predictive analytics to forecast outcomes and support decision-making
Data analysis and AI capstone project, where you apply skills to real-world challenges
Professional development in digital literacy, career planning, ethics, and communication in AI

This balanced approach ensures that you graduate with both technical expertise and a portfolio of completed projects that showcase your abilities to future employers.

Course Listings

Data Analysis and AI Specialist + Internship

The 200-hour, 8-week internship offers hands-on experience in a professional setting, enabling you to apply the knowledge and skills gained in the Data Analysis and AI Specialist program. During the internship, you will gain industry experience, work on practical data analysis and AI-related tasks, and engage with professionals in the field. This practicum bridges the gap between classroom learning and career readiness.

AI Capstone

The AI Capstone is the final course in the AI-focused curriculum, designed to provide you with hands-on experience in solving real-world problems using artificial intelligence. You will apply your knowledge of AI and machine learning techniques to a project that involves the design, development, and deployment of an AI-based solution. At the end of the course, you will have a project that you can showcase to faculty, peers, and industry professionals.

Computer Vision

This course provides an in-depth introduction to computer vision, focusing on the use of machine learning techniques to interpret and analyze visual data. More specifically, this course covers foundational and advanced topics in computer vision, from image classification and object detection to cutting-edge applications like image generation and captioning. You will gain hands-on experience with tools like Keras for building machine learning models and will explore the latest techniques in deep learning and transformer architecture for vision tasks.

Career Planning and Preparation - Level 2

This module builds on concepts and skills introduced in the Career Planning and Preparation Level 1 module. In this subsequent module, you will update and refine your résumé and LinkedIn Profile. You will continue writing cover letters and learn the value of customizing cover letters to specific job postings. You will have the opportunity to apply this knowledge as you conduct a job search and write a cover letter tailored to an ideal job post. Through research, you will create a list of top employers and target current industry opportunities. You will learn about current methods for applying to job postings using technology. You will also gain an understanding of the job interview process, typical interview questions and possible responses, and expectations of both the interviewer and interviewee. In addition, you will engage in practical application of the interview process through role-play. Topics such as negotiating salary, self-management, and on-the-job success for placements and post-graduate employment will be also covered.

Data Analysis and Visualization Capstone

This capstone course is designed to provide you an opportunity to apply your skills in data analysis, statistical methods, and data visualization to a substantial, real-world project. You will tackle a complex data problem, using advanced analysis techniques to uncover insights, build predictive models, and communicate findings through compelling visualizations. The focus will be on turning data into actionable insights for decision-making, and you will be expected to design and implement your analysis with careful attention to ethical issues, data integrity, and clarity in communication. At the end of the course, you will have a project that you can showcase to faculty, peers, and industry professionals.

Ethics and Communication for Data Science and AI

In the rapidly evolving fields of data science and artificial intelligence (AI), professionals must not only possess strong communication skills but also navigate complex ethical challenges. This course explores ethics and communication in the practice of data analysis and AI development, equipping you with the tools to engage in responsible, transparent, and impactful work.

Data Analysis with Python

This course is designed to introduce you to the powerful tools and techniques available in Python for data analysis and visualization. You will learn how to collect, clean, and prepare data for analysis. Additionally, you will leverage the core Python libraries and techniques that form the foundation of data analysis, including Pandas for data manipulation, Seaborn for visualization, as well as the tools for building predictive models and presenting insights from data.

Data Analysis with SQL

In this course you will learn the essential skills and techniques required to analyze and manipulate data using SQL, one of the most widely used languages for data management and analysis. More specifically, this course covers the full process of preparing, analyzing, and interpreting data using SQL. You will gain hands-on experience in structuring SQL queries, profiling data, cleaning and reshaping datasets, and performing advanced analytical tasks such as time series analysis, cohort analysis, and anomaly detection.

Statistics for Data Analysis

This course offers a thorough introduction to the statistical techniques essential for analyzing data and drawing meaningful conclusions. You will explore both the statistical methods commonly used in data analysis, including sampling distributions, hypothesis testing, regression analysis, classification, and statistical machine learning.

Data Visualization

In this course, you will learn the principles and techniques of data visualization, a crucial skill for anyone working with data. Data visualization is not only about creating charts, but also about effectively conveying information and insights to audiences through compelling, informative visuals. This course will provide you with a comprehensive understanding of how to visualize data for both exploration and explanation, enabling you to create visuals that tell impactful stories, communicate insights effectively, and support informed decision-making.

Digital Literacy for Professionals

This course introduces the fundamental concepts and principles of learning and working in a digital environment. This course will cover the following elements: using devices and handling information, creating and editing information, communicating and collaborating, and being safe and responsible online.

Introduction to Python Programming

This course offers a comprehensive introduction to Python programming. In addition to core Python programming concepts, you will develop good programming practices, ensuring the programs are robust and reliable. By the end of the course, you will have gained the practical skills and theoretical understanding needed to effectively work with Python in future courses.

Introduction to SQL Databases

This course provides a comprehensive introduction to the fundamentals of SQL databases, focusing on the essential skills required to design, query, and manage relational databases. You will explore key concepts in database design, including how to create and structure tables to ensure efficient data storage and retrieval. By the end of the course, you will have gained the practical skills and theoretical understanding needed to effectively work with SQL data in future courses.

Machine Learning

This course offers a comprehensive introduction to machine learning, equipping you with the fundamental concepts and techniques used to build and deploy machine learning models. You will learn the core principles behind different types of machine learning, including supervised and unsupervised learning, classification, and regression, as well as advanced topics such as neural networks, deep learning, and reinforcement learning. The course emphasizes practical, hands-on experience with popular machine learning libraries like Scikit-Learn and PyTorch, allowing you to implement algorithms, evaluate models, and apply machine learning techniques to real-world problems.

Predictive Analytics

This course introduces you to the field of predictive analytics, focusing on the application of machine learning techniques to solve business problems and generate actionable insights. You will learn how to harness data to make informed predictions, improve decision-making, and optimize business outcomes. The course covers key concepts in machine learning such as the Cross-Industry Standard Process for Data Mining (CRISP-DM) lifecycle, data exploration, and feature design, as well as advanced techniques like deep learning and reinforcement learning.

Student Success Strategies

This course stresses the importance of developing non-technical skills to enhance personal, academic, and career success. The course will address strategies that are important for all adult learners, such as managing finances, maintaining health and wellness, understanding learning styles, setting goals, and honing practical study skills (such as memory, reading, and test-taking techniques). In addition, this course emphasizes strategies needed to succeed in your program, such as navigating technology efficiently, interacting and engaging with peers and facilitators/instructors, and managing learning time and space.

Admission Requirements

Student has an Ontario Secondary School Diploma or equivalent, OR Is 18 years of age or older on or before the program begins AND can pass a qualifying test that has been approved by the Superintendent.
The approved qualifying test for this program is the Wonderlic test. A passing score for this program is 22.

For students who completed high school in a non-English-speaking jurisdiction, or who cannot obtain their high school records from an English-speaking jurisdiction, English language proficiency must be demonstrated through one of the following:

Completion of post-secondary education (college or university) in Canada or another English-speaking jurisdiction,
Academic IELTS: Minimum score of 6.5 with no individual band score lower than 6,
TOEFL: Overall score of 79,
Canadian Academic English Language Assessment (CAEL): Minimum overall band score of 70.

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The fields of data analysis and artificial intelligence are reshaping every industry - from healthcare and finance to education and beyond. Our Data Analyst (DA) and Data Analysis and AI Specialist (DAAS) programs are designed to equip students with the practical skills and strategic thinking required to thrive in these rapidly evolving sectors.

Jason Eckert

Dean of Technology