What do you need to get started with the Top Data Science Projects course?
Here’s what you’ll need:
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A working laptop/desktop with 4 GB RAM
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A working Internet connection
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Knowledge of Python/R will come in handy
This is a very common question interviewers ask in data science interviews. We have conducted hundreds of these interviews for both data analyst and data scientist roles and this is quite often the jackpot question. This is especially true if you’re a fresher or a relative newcomer to data science.
Just doing courses or attaining certifications isn’t good enough. Almost everyone we know holds certifications in various aspects of data science. It adds no value to your resume if you don’t combine it with practical experience.
And that’s where open-source data science projects play such a key role!
A collection of open source data science projects, ranging from fields like Computer Vision, NLP, Machine Learning and even Data Engineering Projectas
From beginners to advanced data science folks, there are data science projects for professionals of all levels here
Interviewers love applicants who come up with Projects and their solutions which shows curiosity, passion, and enthusiasm for the field
Adding data science projects to your resume will prop up your chances of getting hired.
It would be helpful if you have a working knowledge of at least the basic machine learning algorithms. While this course caters to both beginners and advanced users in data science, your existing knowledge will help you approach these projects with a clearer understanding of how to solve the problems.
We suggest taking the ‘Introduction to Data Science’ or ‘Applied Machine Learning’ courses first before tackling these projects if you don’t have any prior ML knowledge.
This course is free of cost.
The idea behind this course is to expand and hone your existing data science skills. The more effort and time you put into these projects, the more you’ll learn. Take at least an hour or two every day and work on the projects that you are interested in.
Practice! Think of this as a never-ending journey - if you complete one project, take up another that caters to your domain or interests. The more your practice, the better you’ll become as a data science professional.
We also recommend writing about these projects. Once you finish one project, write down your approach, your learning, etc. and put that in a blog article. This will help you solidify what you learned and also help the community when they work on the same project. It’s a win-win for everyone!
Yes, all the projects are open source and we have provided the links to download them in the course itself. So help yourself and happy learning!