How to Build a Strong Data Science Portfolio

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...

Data Science Portfolio

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Consider, therefore, public portfolio as the skeleton of your skills.


How to Build a Data Science Portfolio

Looking for an entry-level data science job can be a defeating experience because you need experience to land a job, but you also need a job to get experience.


It can get puzzling for the beginners


Well, To get a job in Data Science, you need to show expertise through real-world projects.


The easier it is for people to find these projects, the easier it becomes for hiring managers to evaluate your skills.


To get a job in Data Science, you need to show expertise through real-world projects.


Data Science projects push you learn programming, performing statistical analysis, deploying solutions, and creating data visualizations to communicate results meaningfully.

key reasons


>>> Hands-on experience

>>> Data Community

>>> Contributions

>>> Internships

>>> Jobs

working on data science projects is worth your time and creating a portfolio will boost your career prospects in many ways.

>>> Let's Dig Deeper

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Creating Projects

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.

Projects portfolio and documentation

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.

3 TIPS for Building a Strong Data Science Portfolio

These tips will help you persuade potential employers that you are uniquely qualified for a position.


>>> Kaggle


>>> Writing

>>> Let's Dig Deeper

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Join Kaggle

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.

>>> Work on Datasets

>>> Join Competitions


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.

Closing Note

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.