The Canadian tech industry is experiencing a surge in demand for skilled tech professionals, particularly in areas such as artificial intelligence (AI), machine learning, and data science. The other major areas in this context are cybersecurity and cloud computing. This demand is a significant reason why these professionals earn such handsome salaries over here. For example, a machine learning professional earns an average base salary of CAD 100,000 a year, and for data science and AI professionals, this figure stands at CAD 124,530. In this context, aspirants seeking to excel in these sectors must understand the fundamental differences between AI, ML, and data science, enabling them to make informed career decisions.
This blog will explore the fundamental differences between data science, machine learning, and AI. It will also offer examples of real-world applications of these disciplines.
Understanding Data Science, Machine Learning, and AI
Data Science: Data Science is the practice of collecting, analyzing, and interpreting large amounts of data to find meaningful insights. It helps businesses and organizations make informed decisions by uncovering patterns, trends, and relationships in data.
Machine Learning (ML): Machine Learning is a branch of artificial intelligence where computers learn from data and improve their performance over time without being explicitly programmed. It is used in applications like recommendation systems, fraud detection, and predictive analytics.
Artificial Intelligence (AI): Artificial Intelligence is the broader field of making machines smart. AI enables computers to perform tasks that usually require human intelligence, such as understanding language, recognizing images, making decisions, and solving problems.
Data science, ML, and AI are related fields, but they differ in specific ways. AI is a broad concept whereby machines mimic human intelligence, data science is the overall process where you extract insights from data, and ML is a subset of AI that learns from data.
Data science acts as the fuel for both ML and AI. It collects, analyzes, and processes data to find patterns. ML uses these patterns and builds algorithms for automation and prediction. AI uses these models and creates intelligent systems that perform various tasks.
Data Science vs. Machine Learning vs. AI: Understanding the Core Differences
When comparing data science, AI, and ML, one must first acknowledge that these fields are interconnected, yet distinct. They differ in terms of their scope, objective, methods, and skills, as will be evident now.
Scope
Data science encompasses the entire data lifecycle, from storage and collection to visualization and analysis. Machine learning is a subset of artificial intelligence (AI) that focuses on creating algorithms that learn from data. AI aims to develop intelligent systems that can perform tasks that typically require human intelligence. The most prominent examples of such tasks are understanding natural language, making decisions, and recognizing objects.
Objective
Data science aims to uncover patterns, insights, and trends hidden in data to inform decision-making. Machine learning aims to help systems perform better and make predictions based on data without the need for explicit programming. AI aims to simulate human intelligence and create machines that can think, adapt, and learn. In the context of data science vs. ML vs. AI, this is a significant area of difference.
Methods
Data science employs various statistical techniques, data visualization tools, and machine learning (ML) algorithms. Machine learning utilizes algorithms such as linear regression, neural networks, and decision trees. In AI, the most prominent techniques are machine learning, robotics, and natural language processing.
Skills
Skills are also a significant area of difference between data science, machine learning, and AI. Data science calls for a strong foundation in statistics, computer science, math, and programming. To be good at machine learning, one must be an expert in algorithm development, model evaluation, and data preprocessing. The most essential requisite skills in AI are computer science, specialized AI techniques, and math.
Examples
The most prominent examples of data science usage are:
- Analyzing Customer Data to Identify Trends.
- Analyzing Customer Data to Predict Sales.
- Using Data to Detect Fraud.
Here are some key examples of how machine learning is being used:
- Building Recommendation Systems.
- Recognizing Images.
- Forecasting Stock Prices.
These are some prominent examples of areas that function principally on AI:
- Chatbots.
- Autonomous Vehicles.
- Virtual Assistants.
Also Read: Top Data Science Skills You’ll Learn in a Course
Real-World Applications of Data Science, Machine Learning, and AI in Canada
Data science, AI, and ML have numerous real-world applications across various industries in Canada. There is no significant difference between the work done by machine learning engineers and data scientists in these sectors.
| Domain | Main Technology Used | Areas of Application |
| Healthcare | Data Science |
|
| Finance | ML |
|
| Transportation | Data Science and ML |
|
| Natural Language Processing | AI |
|
| Robotics | AI and ML |
|
Also Read: Why Data Science is One of the Most In-Demand Careers in Canada
Overview of Salaries and Canada Job Market
The following table shows the annual average salary ranges of data science, machine learning, and AI professionals in Canada:
| Job Role | Annual Average Salary |
| AI | CAD 77,000 – CAD 122,000 |
| ML | CAD 82,000 – CAD 100,000 |
| Data Science | CAD 76,000 – CAD 100,000 |
Source: Glassdoor
The job market for data science, AI, and ML in Canada is growing rapidly, with attractive salaries and strong demand driven by the all-encompassing digital transformation across the North American country.
However, as per recent market trends, there is a greater tendency to hire experienced and specialized professionals rather than entry-level positions.
Career Path Focus
ML, AI, and data science offer strong career paths in Canada thanks to strong demand across many industries and cities like Toronto, Montreal, and Vancouver that have thriving tech hubs.
Following are the most prominent career paths in these domains:
- Data Analyst
- Data Scientist
- ML Engineer
- AI Engineer
- Data Engineer
- Research Scientist (AI/ML)
The most in-demand skills here are:
- Programming Languages
- Cloud Platforms
- Big Data Technologies
- MLOps
- Specialized Knowledge in NLP, GenAI, and Computer Vision
The standard entry point for these roles is a bachelor’s degree in a quantitative domain like computer science, statistics, mathematics, and engineering.
Popular Tools
The following table enumerates the most prominent AI, ML, and data science tools right now in Canada:
| Domain | Tools |
| Core Programming Tools | Python Libraries RExcel and SQLJulia |
| ML and Deep Learning Frameworks | TensorFlowPyTorchScikit-learnKerasH2O.ai |
| Data Visualization and Business Intelligence Platforms | Tableau Power BISnowflake |
| Cloud and Big Data Platforms | Apache Spark Databricks Anaconda Azure Machine Learning and Amazon SageMaker |
| Firms Focusing on Specialized and Emerging AI Tools | Cohere Deep Genomics Waabi Ada Graphcore PyTorch-based GenAI |
The following initiatives play a major role in the growth, development, and spread of the AI ecosystem in Canada:
- Pan-Canadian AI Strategy – Vector Institute (Toronto), MILA (Montreal), and Amii (Edmonton)
- Canadian AI Sovereign Compute Strategy
- AI Assist Program
How to Choose Courses and Certifications in Canada
When you are trying to select the right AI, ML, and data science courses and certification programs in Canada focus on the following factors:
- Determining your goals and current skill level.
- Evaluating the curriculum and practical application.
- Researching the provider and certifications.
For example, if you are a beginner, look for introductory programs with no strict prerequisites. Look for online courses and certificate programs that cover fundamentals like data basics and Python.
Internship and Networking Tips
If you want to land an AI, ML, and data science internship in Canada, you should keep the following suggestions in mind:
- Build a project portfolio.
- Refine application materials.
- Use multiple channels to secure opportunities.
In your portfolio, showcase your work properly. Create a well-organized GitHub profile with a couple of interesting, easily accessible projects.
The most important networking tips in this scenario are:
- Join AI/ML communities in Canada.
- Use LinkedIn properly.
- Demonstrate your expertise.
- Master soft skills.
In terms of joining AI and ML communities in Canada, you can attend leading tech conferences like AI Toronto, Montreal AI Symposium, and CIFAR.
upGrad’s Data Science, AI, and ML Programs: Your Path to a Rewarding Career
For students seeking to work as data scientists or machine learning engineers in Canada, the online data science and analytics, as well as AI and ML programs available through upGrad, are the best options. These programs are offered by some of the world’s top universities, guaranteeing that candidates receive the best education.
- Master of Science in Data Science, Liverpool John Moores University
- Post Graduate Diploma in Data Science (E-Learning), upGrad Institute
- Executive Diploma in Data Science and AI, IIIT Bangalore
- Post Graduate Certificate in Data Science & AI (Executive), IIIT Bangalore
FAQs on Data Science vs. AI vs. Machine Learning
Data science is a broader field that focuses on extracting knowledge and insights from data. Machine learning, on the contrary, is a specific subset of AI that looks to create algorithms that help computers learn from data.
The answer to this question depends on the specific skills, long-term goals, and interests of individual candidates and, as such, may vary from person to person.
In Canada, data science and artificial intelligence professionals receive higher annual average base payments than machine learning professionals.
The following industries in Canada hire more data scientists than machine learning engineers:
– Technology.
– Financial Services.
– Healthcare.
– Retail and E-Commerce.
– Government.
– Artificial Intelligence.
Yes, students can learn both data science and machine learning in a single course, as there are many comprehensive courses that cover both disciplines.
