Willis College
Data Analytics and Artificial Intelligence
Turn Complex Data Into Powerful AI Solutions
Online (Remote Classes)
Program Length:
76 weeks
Program Hours:
1760
Credentials:
- ON Diploma
Study Options:
- Online
Start Dates
Online
August 24, 2026
November 23, 2026
January 11, 2027
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Turn Complex Data Into Powerful AI Solutions
In just 76 weeks, this comprehensive online Data Analytics and Artificial Intelligence (DAAI) program combines the core skills of a Data Analyst and Business Intelligence Analyst with advanced training in machine learning and AI.
You’ll start with SQL, Excel, Python, and statistics, then progress into data wrangling, visualization, ETL, and the full analysis process. From there, you’ll dive into supervised and unsupervised learning, neural networks, NLP, computer vision, and MLOps, preparing you for the evolving world of AI.
Willis College offers two distinct learning paths tailored to your goals* – whether you’re looking for hands-on workplace exposure or focused, resume-building project experience.
- DAAI-Applied program: includes a practicum, giving you direct, real-world workplace experience
- DAAI-Capstone program: features a capstone project that allows you to apply your skills to a comprehensive, portfolio-ready initiative
Hands-On Learning
Whichever path you choose, you’ll build and refine your skills through hands-on learning. Throughout the program, you’ll complete AI project case studies that apply machine learning to real-world challenges.
This program will also introduce you to the essential technologies used by today’s data professionals:
- Python
- Pandas
- NumPy
- Scikit-learn
- TensorFlow
- Keras
- PyTorch
- XGBoost
- NLTK
- spaCy
- OpenCV
- Flask
- Docker
- Kubernetes
- MLflow
- DVC
- Airflow
- Git
- MongoDB
$40.87
Average Wage/HR$61.03
High Wage/HRNational Occupation Classification (NOC) for this program: 2172/21223
Source: noc.esdc.gc.ca & www.jobbank.gc.ca
Program Outline
Please click on the course title to see the course description.
This course will cover the hardware and software foundations utilized within a computing environment for Data Analysts.
Databases are used to store data in a structured way, and most organizations today capture their data using one or more databases. This course will introduce students to the concepts of relational database and how to apply fundamental knowledge of Structured Query Language (SQL) for storing, retrieving, and performing complex database operations in order to generate business insight.
MS Excel is the most widely used spreadsheet application in the market. An easy but powerful tool for extracting actionable insights from data, MS Excel skills are highly valued by employers. In this course, students would learn how to use MS Excel for Data entry, calculation, data analysis, and visualization.
Statistics is concerned with the systematic collection, analysis, interpretation, and presentation of data. It provides us with a set of techniques to transform data into information. This course will introduce the students to statistical thinking and techniques needed for data analysis.
Python is a general-purpose programming language that is easy to learn and versatile with rich library for data analysis. In this course, students will learn how to solve a broad set of data analysis problems effectively using the Python programming language.
The Data Analytics Process provides a framework that helps us to effectively explore data in order to provide valuable and actionable information. This course will introduce students to the main steps in the data analytic process.
This course will give a detailed understanding of various aspects of data. Students will learn the types and sources of data and how to read, write, and store data for both structured and unstructured data. Students will also identify the issues involved with storage and usage of data.
During this course, the student will learn to apply visualization principles to the data analysis process. We will explore data visually at multiple levels to find insights and create a compelling story using the data.
This course will introduce the foundations of creating pipelines to efficiently extract, transform, and load data from multiple sources and into commonly used systems and formats.
The aim of this course is to introduce learners to the underlying mathematics related to Machine Learning. Topics will include Linear Algebra, Probability, and Calculus. This course will serve as a refresher to the basics in mathematics. At the end of the course, the student will have gained the prerequisite knowledge required for more advanced-level knowledge in Machine Learning.
This course aims to introduce students to the key topics underpinning AI. It will explain the historical development and modern applications of AI. The course will investigate various technologies and how they use AI in modern society. It will also highlight the main differences between AI and Machine Learning. In addition, the course will look at Data Science, its relationship with AI, and various types of Machine Learning.
This project aims to demonstrate an understanding of the Data Analytics and AI program. The student will research and frame a data problem and then apply a machine-learning solution to this problem. In addition, the student will manage the workflow from end to end using knowledge gained from the course. A technical report that explains the work done and delivered will be submitted alongside the code developed in Python. The code may be submitted either as a text document or committed to a git repository
This course introduces the concept of Machine learning compared to traditional software development for providing data-driven solutions to real-life problems. It also includes a high-level view of the Machine learning project life cycle with some insights on how to conceptualize/frame a Machine Learning problem.
Supervised Learning Algorithms are the most common machine learning algorithms in the real world. This course provides an in-depth look into the practical application of supervised learning for model development to solve regression (Predicting values) and classification (predicting classes) problems. It also features a mini project that will provide hands-on experience to students.
Unsupervised Learning Algorithms for solving complex machine learning problems with unlabelled data.
This course provides useful ideas of practical application of neural network and deep learning.
Most business data will involve the use of sequential time series data. This course provides hands-on experience on how to use sequential data to forecast or predicting future values.
This course will provide hands-on experience on how to use artificial intelligence to enable machines read and to interpret natural language.
This course covers the aspects of artificial intelligence that help machines process images by transforming it from one form to another (image processing); and help computers understand and/or extract meaning from images or videos (computer vision).
Group work and presentation on a major machine learning and AI solution in any field of interest. The goal is to show evidence of an understanding of the problems identified in the case and how the ML solution solves the problem. A detailed report or presentation of the various steps in the machine learning project life cycle will be required.
This course focuses on providing hands-on experience with regards to the automation and monitoring machine learning deployments in production.
Our Career Management course will introduce students to and teach them how to apply the most effective strategies to achieve employment in their chosen career. Topics that are covered: effective job search strategies, cover letter and strategic resume writing, interview preparation, and effective interviewing skills.
Practicum or CapstoneBased on the path selected, the student will either complete a 16-week practicum placement or a capstone project.PracticumThe 16-week practicum placement is designed as the culmination of the program. The Willis Career Services team will support you in securing a suitable placement with a business, public, or non-profit organization that operates according to standard Canadian business practices.
During your practicum, you may engage in hands-on work, job shadowing, or a combination of both, allowing you to apply the skills and knowledge gained throughout your program. While not all placements will provide opportunities to practice every skill learned, students are encouraged to observe and apply their learning wherever possible.CapstoneIn your capstone project, you’ll apply your AI and data analytics knowledge to solve a real-world challenge from start to finish. You’ll define a problem, collect and prepare data, build machine learning models, and deploy a final solution—gaining valuable hands-on experience throughout the full AI project lifecycle.You’ll also develop practical skills in MLOps and machine learning operations, learning how to manage, maintain, and operationalize models to support scalable, data-driven decision-making in professional environments.
Job Market
Graduates will be prepared for high-demand careers across industries, including technology, finance, healthcare, government, and more. Successful graduates from the Data Analytics and Artificial Intelligence program may choose from positions such as:
- Data Analyst
- Business Intelligence Analyst (BI Analyst)
- Reporting Analyst
- Database Analyst
- Machine Learning Specialist
- Predictive Analytics Specialist
- Data Visualization Specialist
- Data Scientist
- Machine Learning Engineer
- AI Engineer
Salary Information
Median Wage ($/hr) – $40.87
High Wage ($/hr) – $61.03
Related National Occupation Classification (NOC) for this program: 2172/21223
Source: noc.esdc.gc.ca & www.jobbank.gc.ca
Updated: 2026-04
Admissions Requirements
Academic Admission Requirements:
Canadian High School diploma or equivalent. Willis College’s Admissions Team uses the Wonderlic assessment tool.
Academic Admission Requirements:
Canadian High School diploma or equivalent. Willis College’s Admissions Team uses the Wonderlic assessment tool.
For Mature Students
Applicants without a high school diploma may qualify under Mature Student Status by meeting the following criteria:
- Age: Must meet provincial minimums (18 in ON; 19 in the rest of Canada), verified via government photo ID.
- Assessment: Achieve minimum cut-off scores on the securely proctored Wonderlic test.
- Prerequisites: Satisfy all program-specific and language proficiency requirements.
For full details, see the Willis College Mature Student Admissions Policy.
Please note that at this time, Willis College can only accept students who are Canadian Citizens, Permanent Residents, or Refugees. We are unable to issue Post-Graduate Work Permits.
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Practicum / Capstone
Willis College offers two distinct learning paths for the Data Analytics and Artificial Intelligence program.
- DAAI-Applied program: includes a 16-week practicum
- DAAI-Capstone program: includes a capstone project
Students must decide their learning path/program of choice prior to enrollment. Once training has started, the student will be unable to change without withdrawing from the initial program, which can carry financial implications.
This program is an excellent fit for:
- Analytical thinkers ready to solve problems with AI and machine learning.
- Students and career starters looking to break into data and AI.
- Professionals switching from finance, IT, or business into advanced tech roles.
- Tech professionals seeking advanced skills in machine learning, deep learning, and AI deployment.
- New Canadians seeking Canadian credentials and practicum experience.
Why choose the Data Analytics and Artificial Intelligence program?
- Learn Data Analytics and AI in One Program: Build a strong analytical foundation before advancing into machine learning, deep learning, and artificial intelligence.
- Develop Skills Beyond Basic Reporting: Move from organizing and visualizing data to building predictive models and intelligent solutions that support better decisions.
- Gain Hands-On Project Experience: Create data and AI projects that demonstrate your ability to frame problems, develop models, manage workflows, and present technical findings.
- Prepare for Advanced Career Pathways: Build skills relevant to roles in data analysis, business intelligence, data science, machine learning, predictive analytics, and AI engineering.
- Gain Real-World Workplace Experience: Complete a practicum placement that allows you to apply your training and build experience in a professional Canadian workplace.
- Study Online with Career-Focused Support: Complete your diploma through flexible online learning while developing your résumé, interview skills, and job search strategy.
What You'll Learn
- Core Data Analytics: Build practical skills in SQL, Excel, Python, statistics, data sourcing, and the complete data analysis process.
- Data Preparation & Visualization: Learn to clean, transform, organize, and visualize structured and unstructured data to uncover insights and communicate clear findings.
- Machine Learning & Predictive Analytics: Develop and evaluate supervised and unsupervised learning models for classification, regression, forecasting, and other real-world applications.
- Advanced Artificial Intelligence: Explore neural networks, deep learning, natural language processing, computer vision, and the technologies behind modern AI systems.
- Data Pipelines & MLOps: Learn ETL processes and the tools used to automate, deploy, monitor, and manage machine learning models in production environments.
- Applied Data & AI Experience: Complete hands-on projects, case studies, and a practicum placement where you can apply your analytics and AI skills to real-world challenges.
Why You'll Love Willis College
- Industry-Aligned Programs: Career-ready curricula designed alongside top employers to ensure your skills match current market demands.
- Instructor-Led, Applied Learning: Hands-on training taught by industry experts who bring real-world knowledge, mentorship, and practical insight into the classroom.
- Flexible Learning Pathways: Tailored study options—in-person, online, or hybrid—that fit your lifestyle and meet you where you are.
- Personalized Small-Class Support: Dedicated guidance and encouragement in smaller group settings where you are a person, not a number.
- Accessible Funding Assistance: Direct support navigating student loans, grants, scholarships, and flexible payment plans to make your education attainable.
- Lifetime Career Services: Continuous job-search support and career counseling to help you transition smoothly from the classroom to the workforce.
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What Our Graduates Are Saying
"The Admissions process was actually something that went really smoothly. The staff answered my questions exactly how I wanted. The next day, I told them I’m joining the program."
Moses Rubuye
Cybersecurity Analyst Graduate