One of the most important steps to take when planning how to become a Data Scientist is deciding how you will show your skills, accomplishments & knowledge.
Your goal should be to stand out and be one of a kind, not one of many.
Portfolio Strengthens your efforts of data science job hunting...
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Consider, therefore, public portfolio as the skeleton of your skills.
It can get puzzling for the beginners
The easier it is for people to find these projects, the easier it becomes for hiring managers to evaluate your skills.
Data Science projects push you learn programming, performing statistical analysis, deploying solutions, and creating data visualizations to communicate results meaningfully.
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Projects are not substitutes for your work experience, but you can with projects show the expertise that most people gain through work experience.
As a beginner, you can start with easy projects and observe how your peers create well-documented projects and communicate the quality of analyses.
Portfolio projects which capture the most attention are those that are well documented.
Documentation will make or break the success of your projects and your portfolio overall.
Code quality is of paramount importance for relevance and clarity. If your work is not simple, it's not exceptional.
You could configure a local Jupyter environment with GitHub or Deepnote to publish your projects
The single document approach with Jupyter Notebook makes life easy to develop, visualize and add information, and formulas that make work more understandable, repeatable, and shareable.
Learn the best practices to write your programs more effectively.
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Kaggle is the largest, most trusted online community for data scientists and machine learning enthusiasts.
It is good for learning machine learning.
It's completely free, all datasets, participation in competitions, and discussions.
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One of the best ways to highlight your skills is to have an active presence on GitHub.
You can host both code-based and content-based projects on GitHub
You can easily customize your GitHub profile page, add links to your articles and showcase your projects. It's best is to link the GitHub, LinkedIn, and Kaggle profile.
Data science blogs can be a fantastic way to hone your communication skills, present your analysis, uncover unique insights, and publish data visualizations.
While it's true that you show your expertise via projects, but you should start writing tutorials as you grow. You'll build readership if you write high-quality tutorials.
There is no portfolio format that works best. The common denominator, though, is that you should focus on the speciality, skills and notable accomplishments.
Your portfolio should have an intriguing description that drives people to check your projects, tutorials, articles, etc.