Data Science vs. AI vs. Machine Learning: Which Career Path is Best for You in Canada?

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.

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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.

Also Read: Why Data Science is One of the Most In-Demand Careers in Canada

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

Q: What is the difference between data science and machine learning?
Ans: 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.

Q: Is data science better than machine learning as a career in Canada?
Ans: 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.

Q: Which has higher salary potential in Canada: data science or machine learning?
Ans: In Canada, data science and artificial intelligence professionals receive higher annual average base payments than machine learning professionals.

Q: Which industries in Canada hire more data scientists than machine learning engineers?
Ans: The following industries in Canada hire more data scientists than machine learning engineers:

• Technology.
• Financial Services.
• Healthcare.
• Retail and E-Commerce.
• Government.
• Artificial Intelligence.

Q: Can I learn both data science and machine learning with a single course?
Ans: Yes, students can learn both data science and machine learning in a single course, as there are many comprehensive courses that cover both disciplines.