Jitesh Khurkhuriya

To become a data scientist, you will need a data science portfolio. Even if you have mastered the skills to become a data scientist, not having a portfolio is a mistake. It can prevent you from getting a job, earning over $100k a year, and can result in you losing visibility.  

Why do you need a data science portfolio?

You might be wondering why you need a public profile. After all, isn’t everything on your resume? The short answer is no. These days, recruiters look at more than just resumes while recruiting for a role. The majority of recruiters actively look for your public profile. This includes answers on sites like Stack Overflow, articles on LinkedIn, and projects on GitHub. It is important to build your public profile to get a job or even create a freelance business opportunity.

Data Science 2021 : Complete Data Science & Machine Learning

Last Updated March 2021

  • 282 lectures
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4.6 (1,670)

Machine Learning A-Z, Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science | By Jitesh Khurkhuriya, Python, Data Science & Machine Learning A-Z Team

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Create a standout portfolio

Most of the courses you’ll take to build your data science skillset will need you to create a project. While the tutorials’ data can be interesting, you should test your newfound knowledge and skills with different datasets. 

It helps if you focus on creating your project portfolio while learning every topic. Many online courses follow this practice of building projects while teaching the concepts. There are several resources where you can download data and create a portfolio of projects. One of my favorites is the UCI Machine Learning Repository. It has over 500 datasets from domains like life sciences, engineering, and business. It includes banking, cards, social media, online retail, and many more. 

Another interesting online resource for projects is the Kaggle Competition. Some of the projects from both of these sites that you can try are:

1.  Boston House Price Prediction (Regression)

2.  Bike Demand Prediction (Regression)

3.  Automobile Price Prediction (Regression)

4.  Iris Species (Classification)

5.  Pima Indians Diabetes Dataset (Classification)

6.  Wine Quality Prediction (Classification)

7.  Bank Telemarketing (Classification)

Host your portfolio in the right places

You could build a personal website to show off your abilities, but the most visible places to put your portfolio are places recruiters already look. Here are the three best places to show off your skills:

LinkedIn 

LinkedIn has become a standard for building your professional profile. It is the first place where people check for your public profile. 

To create a strong LinkedIn Profile for data science:         

Stack Overflow

This is another immensely important place to showcase your skills. This one needs some dedication and continuous effort. The only way of building a great profile on Stack Overflow is by providing strong answers to various questions. Many of the AI-based recruiting tools these days give weight to your credits or points on Stack Overflow.

To create a strong Stack Overflow presence:     

GitHub

This is one of the best places to showcase your projects with the entire code and results. Assume a technology person is screening your resume and has access to your GitHub profile. It can give them insight into various aspects of your skills. A GitHub profile is a go-to resource for filtering resumes. 

To create a strong GitHub profile:     

It is essential to build a portfolio for data science. It is also important to host your portfolio in the right places. This will make you more visible to recruiters. Another option is to create a personal website to show off your abilities. You can complete all the reference projects I mentioned in my data science course on Udemy

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