In 2025, data science continues to be one of the most in-demand fields in Canada, with growing opportunities across various industries. The job market is experiencing a notable surge in data science and AI roles, particularly in areas like machine learning and AI ethics. Nucamp says that from 2018, there has been a 37% increase in demand for core AI skills in Canada by 2025. Also, data scientists earn excellent salaries of CAD 136,351 each year in tech hubs like Quebec.
In such a context, aspiring professionals in this field must have the best data science skills to land the finest jobs. This blog will discuss the critical skills that students will learn in data science programs and courses in Canada.
Source: Nucamp
The Most In-Demand Data Science Skills Taught in Courses
The growth forecast for the global market for big data is predicted to be CAD 379.9 billion by 2026. Compared to 2018, this represents over 200% growth, establishing big data as a big business. Despite the growing demand, there is a shortage of qualified data professionals worldwide, and this is where learning data scientist technical skills can help students progress far in their careers.
| Skill | Importance |
| Python | One of the most popular programming languages in the world. |
| R | Extremely important for computing and statistical analysis. |
| Math and Statistics | Necessary for career development. |
| SQL | Important for communicating with and managing relational databases. |
| NoSQL | Essential for working with unstructured data. |
Python
Python is one of the world’s most prominent programming languages, making it the most important of all data science skills required. The language ranks first on several popularity indexes, such as the PYPL Index and the TIOBE Index. A significant reason why Python is so popular is its suitability for data analysis work. An interesting thing about Python is that even though it was not created for data science, it has evolved over the years and become the king of the industry.
R
R is the queen of data science and the king of Python. Since its inception in 1992, R has been an open-source programming language specializing in computing and statistical analysis. It is widely used in academia, scientific research, and sectors like business and finance. A significant benefit of R is that it permits various data analyses.
Also Read: Python vs R for Data Science in Canada
Math and Statistics
Having a mathematical background is not one of the most essential data scientist skills. However, students must be familiar with a few statistical and mathematical concepts to progress well in this domain. They must also have a firm grip on statistics when selecting and applying the various data techniques. Such skills also help with constructing strong data models and understanding data properly.
SQL
SQL has existed since the 1960s, and data scientists still need to learn it to do their work properly. SQL is a standard industry tool for communicating with and managing relational databases, which lets structured data be stored in tables related to some common columns.
NoSQL
NoSQL helps deal with unstructured data, making it one of the most critical data science skills. The ability to deal with unstructured data is essential because coping with unstructured data can be significantly more complex than dealing with structured data. Most of the data generated these days is unstructured and comes from sources such as:
- Audio
- Video
- Satellite images
- Web server logs
The unstructured nature of such data makes it hard to process and store using conventional relational models.
Also Read: Best Data Science Certifications for Professionals in Canada
Why Are Data Science Skills Important?
The various data science skills required for data scientist freshers are extremely important for developing a strong foundation in the discipline among students. These skills also offer holistic value, allowing students to develop full-stack thinking and not just technical knowledge.
These skills can also be used in the job market, especially in prominent tech, healthcare, and finance sectors. Learning these skills also prepares learners to excel in lucrative and prestigious roles such as Data Analyst, Data Scientist, and Machine Learning Engineer.
Also Read: Top 10 Online Data Science Courses & Certifications in Canada for 2025
Master Data Science Online through upGrad
The Data Science and Analytics courses through upGrad can help students achieve the finest data scientist skill set. These courses teach students the latest languages and tools in machine learning and data science, thus setting them up for long-term success in these domains. With these courses, students can truly learn how to harness the potential of data for their work.
- 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 Skills You’ll Learn in a Course
Q: What are the core skills taught in a data science course?
Ans: The core skills taught in a data science course are:
- Python
- R
- Math and Statistics
- SQL
- NoSQL
- Data Visualization
- AI and Machine Learning
Q: Why is programming important in data science?
Ans: Programming is crucial in data science because it lets data scientists perform essential tasks like:
- Data collection
- Data cleaning
- Data analysis
- Data visualization
- Building machine learning models
Q: Which programming languages will I learn in a data science course?
Ans: In most data science courses, you’ll learn key programming languages like Python for data analysis and machine learning, SQL for database management, and sometimes R for statistical computing and visualization.
Q: How is data visualization used in real-world data science roles?
Ans: In real-world data science, data visualization calls for simplifying complex data into visual representations that make it easier to understand, communicate insights, and analyze.
Q: What kind of math or statistics is required in data science?
Ans: Students need to possess a strong foundation in the following key areas of Math and Statistics to be successful in data science:
- Linear algebra
- Calculus
- Statistics
- Probability
