• Introduction
  • How is a portfolio important for Data Science aspirants?
  • Essential Steps for Building a Strong Data Science Portfolio
  • Advanced Steps for Building a Portfolio
  • What you need to do is
  • Websites where you can share your work
  • Takeaway



It’s a fact that Data Science is one of the most demanding and competitive fields right now.

Data scientists are always needed.If you do not have the skills yet, then definitely build it.

It is the best way for you to make sure that you don’t get stuck and are able to progress and succeed in your career.

Your Data Science Portfolio is where you show off all the R, Python, and Excel skills and motivate recruiters to invite you for an interview. 


A Data Science portfolio is not just a piece of paper.

It’s your personal “marketing” space.

Remember that Data Science is a very competitive field and companies want to hire the best candidates for the job. For this reason, it is crucial that you enrol in a Data Science Certification course that helps you upskill and advance in your career as a Data Scientist.

A solid portfolio is the most important tool at hand if you want to set yourself apart from your competitors. Building a strong portfolio that highlights your Data science skills and understanding of the field is necessary for any aspirant.

This blog will guide you on how to build a portfolio that will impress interviewers and get you your dream job as a data scientist.

You need to understand what a portfolio is and how it can help you find your dream job.


How is a portfolio important for Data Science aspirants?

Every Data Science Job Profile wants a candidate to have some experience. Your portfolio is an opportunity to show the diversity and depth of your skills and ability to solve complex problems. A good portfolio will make you stand out among other fellow job seekers and showcase your knowledge in Data Science.

As a graduate, you may find the job hunt or career path choosing process mind-boggling. What should you do to stand out? Creating a portfolio of your newly acquired skills can help put you on the right track for landing an entry-level position. 

Building a good portfolio showcases your skills, abilities, and experience.

This portfolio will provide evidence for all of your work in the past.

A strong portfolio will make you stand out from the pack. It will help you stand out in this competitive industry.

A Data Science portfolio is key, and the best way to achieve this is to build and showcase your own projects.

Essential Steps for Building a Strong Data Science Portfolio

Academic Qualification – Information about your graduation or post-graduation diploma. This can be linked to your LinkedIn profile.

Job History – The jobs which you have previously held, along with the organisation name and the job tenure. These should be linked to your LinkedIn profile.

Academic Projects – All academic projects that you have worked on in the past should be documented here. Each project.

Coursework – Coursework or certification courses you have completed that are relevant to the position you are applying for. <Possible CTA: If you want to get a relevant Certification in Data Science, sign up for RISE WPU’s industry-relevant and affordable PG Program in Data Science>

Skills – Instead of using a numerical approach, display your skills in an infographic format.

Resume – Your resume should follow a few general guidelines. Check the internet for some good sample resumes with your desired job title and make sure to personalise it as well.


Advanced Steps for Building a Portfolio

The above-mentioned basic steps are just the necessities of a good portfolio. But to attract the attention of a Hiring personnel or even a Job Portal Algorithm, you will need an extra oomph factor. Get working on some data science-based projects and display them online. Here is how you can achieve it.

Data Projects

Upload your projects on Github. No matter how novice the project is, make sure it is well documented and publicly visible. The project should be well defined so a hiring manager can easily find it. The setup, code, and quirks of the project should be easy to comprehend and detailed out.

Data Cleaning Projects

Find some data sets related to a particular field. The data should be easily available and well structured but might need cleaning. Don’t shy away from this kind of project since they are the best way to display your skills in Python. Come up with some novel insights by doing the analysis.

A showcase of a Data Cleaning Project will allow the hiring manager to know that you can take data clutter head-on and make sense out of it. Moreover, these are the kind of projects that most Data Scientists work on. So, all you need to do is:

  • Find a messy dataset
  • Pick a Query 
  • Clean up the Set
  • Analyse the Data
  • Present Your Results in a meaningful manner
  • Data Storytelling Projects


The Data Science domain needs a storyteller to tell “Data as a story.”

Doing such projects will reflect your ability to deep dive, analyse and showcase insights of data and also narrate it to others in a concise and meaningful way. The impact of this ability is far more crucial than you can imagine. A good storytelling project can help build a great foundation for your career.

Most beginners need to start with a hand-held project which they can easily handle, i.e. analyse data from an eCommerce platform or any other source like a website, sales report, or social media posts on topics of interest and present it in a simple language (Infographics). This will establish you as a data-driven person who can easily analyse the numbers and also report them to others for decision-making purposes.


End-to-End Projects

An end-to-end project is something on the sides of a ‘customer-facing’ system. It involves generating high-efficiency code that will run multiple times on different data sets, presenting different results. These kinds of projects will make you stand out. Many organisations are looking for Data Science Professionals who can take up massive unorganised data and present it in an organised manner. 

What you need to do is:

  • Find an appealing subject
  • Take as much data from it and deep dive
  • Generate predictions 
  • Document your code and keep it clean
  • Upload your project on Github
  • Publish it on Social Media channels

Share your projects on social media. Let the world get a glimpse of your intellect and experience. Letting people see your project is one way of telling hiring managers, “here are my skills.” The majority of the feedback you receive will be negative. However, listening to constructive criticism and incorporating it into your next work can help you grow. 

Here are few sites where you can share your work:

  • Github
  • Kaggle
  • LinkedIn
  • Popular Blog sites, e.g. Medium
  • Data Science Competition 

Data science contests are excellent for mastering skills and improving your knowledge. Competitions can also be seen as a way to demonstrate your technical abilities in Data Science and for employers to find out what they need. The most popular Data Science competition is hosted on Kaggle and DrivenData. You can sign up for a contest and see how far it can take you.



Creating a data science portfolio of your work and projects can seem daunting. But with hard work comes success. In order to be a successful data science professional, you have to do things you are not comfortable with. This list summarises what one needs to do in order for them to get hired as a Data Scientist and become successful at it.  – Learn how to sign up for RISE WPU’s PG program in Data Science and transform your career.

If you have any other insights on different ways to build a successful and eye-catching portfolio for Data Scientist jobs, do drop in your suggestions here. 

We would be glad to hear from you.


References and Sources