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Data Science in Canada – Become a Canadian Data Scientist

Data Science In Canada - Become a Canadian Data Scientist

Data Science in Canada – Become a Canadian Data Scientist

Most people who start their journey in data science in Canada do not have a clear idea about the learning path they should take to learn in a structured manner. Therefore, in this article, you’ll receive all the details on how to learn data science in Canada to become a Canadian data scientist.

Go through the following step-by-step complete guide to learn data science in Canada:

1. Get an overview of what Data Science is like

As a beginner, you will be compelled to dive straight into deep waters but you should start by testing the depth of the water. Data Science is still ‘science’ and it may feel overwhelming to learn everything there is to it. It doesn’t matter if you are learning data science in Canada or in any other country in the world, the overall field will still be the same.

Get an overview of what Data Science is like - Data Science in Nepal - Become a data scientist

In general, the complete data science lifecycle constitutes of four major stages with sub-branches within them:

  • Data Engineering
  • Data Analysis
  • Data Modeling
  • Model Deployment and Monitoring

Most people spend about a year learning all of these, however, it is perfectly okay to learn onle one of the four stages and gain specialization in it but you should know how all of these stages fit together.

We suggest you take a basic course that guides you through an overview of all of these topics: Get the Full Stack Data Science Course.

2. Learn Python for Data Science

A common question that will come up at the start of your data science career will be to choose between Python and R-programming for data science. Both of them are great as a programming tool as long as you know their advantages and disadvantages.

The following conversation with Merishna S. Suwal, the CTO at Kharpann, properly discusses the pros and cons of both of these languages for Data Science:

“If you’re looking to become a data analyst then it doesn’t matter if you choose to use either Python or R to perform your analysis. Since both of these programming languages offer a variety of tools and libraries that (are quite similar and) help you to perform transformations and manipulations on data.

Similarly, you can also create informative visualizations using any of these programming languages to put across insights gained from your analysis.  However, when you step into the shoes of a data scientist, your job is not only to analyze and model the data but also to deploy as well as monitor your model’s performance in production.

Python is more oriented towards programmers and developers with a linear learning trajectory in comparison to R, which is more oriented towards statisticians and researchers.”

It’s clear that Python is the language to go for if you look at the entire Data Science spectrum. Here is a list of other reasons on why to choose Python:

  1. Python is an easy-to-use and powerful high-level programming language.
  2. It is versatile, has a large community, and hence makes a great choice for both beginner and expert-level programmers.
  3. The syntax can be understood easily unlike Java or C and is great for those starting their journey in programming.
  4. It requires minimal setup and is easy to implement.

You can start learning Python for FREE from the following in-depth course: Python Programming for Newbies.

3. Enroll in a data science specialization course

Once you are comfortable with Python, it is time to enroll in a data science specialization course. It is entirely upto you if you want to specialize in all four stages of the data science project lifecycle or if you want to specialize in a single field.

Enroll in a data science specialization course

Also, you have the alternative of enrolling in free as well as paid data science courses. Free data science courses often cover the basics of data science and do not go very in-depth but paid data science courses to focus on the depth rather than the width of a topic. So, it is entirely up to you to choose which course you like based on your preference.

4. Start working on Data Science projects

Kaggle is a great place to start working on data science projects and learn from other data science professionals around the globe.

Start working on Data Science projects

Building complete data science projects gives you the edge over your peers as you get the following benefits:

  • Hands-on coding experience
  • Gain complete know-how of the project topic
  • Have a portfolio to showcase your skills to potential employers
  • Collaborate with other data science aspirants to fill in your knowledge gaps
  • and much more.

Always remember that it is better to complete one in-depth project rather than doing multiple projects and leaving them mid-way.

In Conclusion

Now you know how to learn data science in Canada to become a Canadian data scientist. As a tip, always try to find a mentor who can help you when you get stuck in your data science journey.

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