{"id":71151,"date":"2019-10-18T15:24:22","date_gmt":"2019-10-18T09:54:22","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=71151"},"modified":"2025-07-30T21:17:45","modified_gmt":"2025-07-30T15:47:45","slug":"data-science-portfolio","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/data-science-portfolio\/","title":{"rendered":"How to Create a Data Science Portfolio to get hired as Data Scientist"},"content":{"rendered":"

How to become a data scientist?<\/em><\/strong><\/a> A question that arises in the mind of every aspiring data scientist.\u00a0 And, today we will look at an aspect that is often ignored by most of the data science candidates.<\/p>\n

Yes, it is the portfolio of the candidate. The data science portfolio acts as a very essential tool to crack the interview.<\/p>\n

People often get confused between a resume and a portfolio.<\/p>\n

A resume is a brief summary of your life, skills, talents, and experiences in recent years which is usually 1-2 pages long.<\/p>\n

Whereas, a portfolio showcases the collection of samples of your artworks, writings, photos, and activities.<\/em> Link to your online portfolio can also be added to your resume.<\/p>\n

Candidates often go for interviews with just the resume in their hands, and the resumes are filled with irrelevant details that are actually not required. They don\u2019t realize that the advantage of \u201cshow, don\u2019t tell\u201d is much more convincing when it comes to getting hired as a Data Scientist.<\/p>\n

How to Build a Data Science Portfolio?<\/h3>\n

Basically, a data science portfolio consists of a collection of data science projects<\/strong><\/em><\/a> that you have worked on, it showcases about yourself and your data science skills to the managers hiring you for the job.<\/p>\n

So, it is about selling your skills and talents. Your portfolio should speak \u201cThis is me, and this is what I can do for you\u201d.<\/p>\n

\"Data<\/a><\/p>\n

Take sufficient time to build your portfolio, it should create a lasting impression on the managers.<\/p>\n

If you are a fresher then you should know about which projects you can work.<\/p>\n

On a completely different note, this tip might be helpful for you:<\/p>\n

Data scientists and software engineers in the real world are not perfect, they too use Google to get their issues resolved. If these people read your public work (blogs, answers) and have their problems solved, they might think better of you and even reach out to you.<\/p>\n

\u201cA Portfolio is public evidence of your Data Science Skills\u201d, this definition was given by David Robinson, who is a renowned data scientist today. The most effective strategy that worked for him was doing public work.<\/p>\n

He was an active person on the Stack Overflow programming site to answer on topics related to the data science field and once, a CEO of the company was so impressed by his answer that he got in touch with him and David was hired after a few interviews!<\/p>\n

The more work you do, the higher the chance of a strange incident like this.<\/p>\n

For an entry-level job too, you need to have a little bit of real-life experience because that is the demand of most companies.<\/p>\n

\"Swift<\/a><\/p>\n

You might have seen some memes like this on the internet. But the real question is how do you get experience if you need it for your first job? The answer you are looking for is Projects<\/strong>.<\/p>\n

These data science projects for resume building may be internships, thesis, public works that we talked about above, and the take-home projects.<\/p>\n

You must learn about the Top Data Science skills<\/a> before proceeding ahead<\/strong><\/em><\/p>\n

Tips To Create Your Data Science Portfolio<\/h3>\n

Chances of someone finding your portfolio is through your resume. Hiring managers go through your resumes very quickly, and you have just a few minutes to make an impression.<\/p>\n

1. Appropriate Length<\/h4>\n

Although it depends on the work you have done, try to keep it simple. There should be enough space to include all your work in 2-3 pages. Try not to include objectives and conclusions, keep space for your skills, projects, and experiences.<\/p>\n

2. Relevant Coursework<\/h4>\n

It is the work that is performed by trainees for the purpose of learning. List out all the relevant coursework that you think will be applicable to the job description.<\/p>\n

3. Technical Skills<\/h4>\n

List all the technical skills of yours that the job profile mentions. The skills you are best at should be written at the start and the skills that you have but are not the best at should line up afterward.<\/p>\n

Remember, to ALWAYS rate yourself on your skills. Words such as proficient and familiar must be used to give ratings, don\u2019t give yourself numerical ratings.<\/p>\n

Don’t forget to check the Technical & Non-technical skills to become a data scientist<\/a><\/strong><\/em><\/p>\n

4. Work Experience<\/h4>\n

It\u2019s best if you have any experience, but what if you don\u2019t? You have projects, thesis, competitions, and internships that you can include. These are substitutes for work experience if you are a fresher to put into your portfolio.<\/p>\n

5. Related Internships<\/h4>\n

There are many data science-related internships including data analyst, data engineers, business intelligence or analyst, research engineer, and others, not just data scientists. The internship should provide you relevant work and you should learn something from it.<\/p>\n

Some form of data collection<\/a>, analysis, model building, or visualization is preferred for an actual job.<\/p>\n

But why internships? Most importantly because companies want to hire people who can start to work on real stuff with minimum training as it is time-consuming and time is money in the corporate world.<\/p>\n

\u201cDon\u2019t just blabber, show it\u201d. Having internships shows that you are serious and passionate about work and are not another candidate who says, \u201cI am very passionate about data science and want to learn more about it\u201d.<\/p>\n

6. Real-world Projects<\/h4>\n

Projects give you an opportunity to get experience when you cannot get it from internships. Generally speaking, 3 data science projects are enough to cover the common job responsibilities for job profiles you are interested in. Always, write up your projects in a structured manner.<\/p>\n

Kaggle<\/strong><\/p>\n

If you want to show your skills, participate in kaggle competitions, and contribute to discussions. Link your kaggle profile in the data scientist portfolio so that employers can see how many competitions you have participated in.<\/p>\n

Work on Real-time Data Science Projects<\/a> and showcase your skills to recruiters<\/strong><\/em><\/p>\n

7. Social Media<\/h4>\n

Post your work, that is, your writings, articles, answers, etc on social media so that you are recognized. You can also read up about the most recent and greatest developments\/technologies used by experts in the field to expand your knowledge. It is a great way to get in touch and follow the experts.<\/p>\n

There are various websites where you can post your articles such as Medium, Quora.<\/p>\n

Topics on which you can write about:<\/strong><\/p>\n