How To Build a Data Science Portfolio That Can Help You Get Hired?
Learn everything about building an amazing portfolio
Introduction
A good resume is just not enough for a career in data science!
It is very important to have a good portfolio to get hired as a data scientist. A portfolio is proof of one’s capability. A good portfolio not only increases your chances of getting hired. The process of building a portfolio would transform you into a better data scientist. Building a data science portfolio might not be easy for many. Especially for people looking to start a career in data science.
It is understandable without real work experience what to add to your portfolio. Many times even people who are already into a career in data science find it hard to build a portfolio. If you are struggling with ideas, this article is for you.
Why a portfolio is important?
Before getting into creating a portfolio. It is crucial to understand its importance. With the increase in the popularity of data science. Many people are getting into the field. It is not always possible for the recruiter to interview all potential candidates. To make a better decision recruiter doesn’t just rely on the resume. They also look for evidence that supports the candidate’s skill set and knowledge. By having a good portfolio website you make it easier for the recruiter.
Apart from increasing your employability. By building a portfolio you get to acquire some critical skills that will help you in your career. Like, it helps in improving your communication skills and becoming a better storyteller.
What could your portfolio include?
Many people think a data science portfolio is just a collection of projects. Projects are just one aspect of a portfolio. Many other things can be included in a data science portfolio. Let us see about each one of them in detail.
Blogs
The purpose of a portfolio is to showcase skills. Blogs can be effectively used to showcase your skills. Many people think that blogs are reserved only for experienced professionals and writers. But the truth is, anyone can write a blog. There are different kinds of blogs like,
- Tutorial Blogs — Here you share a project or your solution to a specific problem along with the technical details and code
- Experience Blogs — Here you can share your experience of using a tool or a product or a course
- Informative Blogs — Here you can share your knowledge about a concept or a topic
The good thing about data science is that the community is much diverse. Almost anything you decide to write could be helpful to someone out there. Most importantly, blogs are a very good technique to build your eminence in data science. Also, writing helps in sharpening your knowledge on a topic.
One biggest mistake many do while writing a blog is waiting to make it perfect. Do not try to make your first blog perfect. Perfection will be achieved over time. Here are some tips to get started with a data science blog
Git Repository — Create a great profile page
Many recruiters would ask you to share the link to your Git repository. It is one easy way to validate the knowledge of an individual.
One important aspect to remember here is. Your contribution frequencies or the number of repositories are not important. The recruiter or your potential boss doesn’t have the time to go through your scripts. Your recruiter or your potential boss would glance at your profile in most cases. Hence you need to make your git profile appealing. Here is an amazing article on building a stunning git profile page.
For all your repositories include a readme page. Add all the key details about the project like,
- Purpose and Objective
- Assumptions
- Dependencies
- References
Many times people ignore the need for documentation and do not give the due importance. When you work with a team, documentation becomes key to ensuring better collaboration. The other aspect to it is, the recruiters are generally from a non-tech background and hence having a simpler explanation and better documentation would help in creating a greater impact.
Kaggle
Kaggle is not only an amazing platform to learn data science. It is a good place to build a reputation for yourself. Participating in the competition is just one way to contribute to the platform. There are different ways to engage on the Kaggle platform. Like,
- Sharing an interesting dataset
- Sharing your scripts/solutions using Notebooks
- Helping others in the discussion forums.
- Sharing challenges with fellow members
If you are completely new to data science. This platform has some amazing short tutorial courses that will help you to learn key topics. Generally speaking, most platforms cater best to a particular category of users. Some platforms are good for beginners or for people with experience. Kaggle is one of the few platforms that are very helpful for absolute beginners to seasoned data scientists.
If you want to make the best use of Kaggle towards building your career in data science. Here is one of my popular articles.
Mentoring
Mentoring helps in building your leadership and communication skills. There are no minimum criteria to become a mentor. You could be a student who is trying to get a job in data science. Still, you could help someone who is just getting started in data science.
Once you gain some experience in data science then there are a lot of ways you could become an official mentor. Some universities offer platforms to enroll you as a mentor. These platforms would bring structure to the mentoring program. They also train on key aspects of becoming a successful mentor.
Training the junior resources on a project and coaching them does come under mentoring. Here is an article sharing the benefits of becoming a mentor,
Research Papers
Having a research paper published is strong proof of your knowledge about a particular topic. A research paper can be about a concept, findings, survey, or could be experimental. To get some idea about the wide range of research papers, check below
When you get a chance to publish your paper, your credibility increases. If you are interested in learning about writing and publishing research papers, check the below article
Technical reviewer
A technical reviewer is a person who reviews and provides feedback on the original work of the author. Many publications make use of technical reviewers to get a feel of the content produced by their authors.
It is a great opportunity to learn from an experienced professional. Also, it will certainly bring more people to your professional network. I have authored 2 books in data science. My first opportunity to write a book came from the book I had reviewed. Reviewing books provides great exposure to writing. It can be very helpful if you have plans of becoming an author. If you are interested in becoming a technical reviewer, here are some links.
Do’s and Don’ts for your portfolio
While Blogs, Git repositories, Kaggle, authoring research papers, mentoring, and being a tech reviewer, are some of the best ways to build your eminence. There are some strict don’t dos when it comes to projects. never go for the default projects. One classic example is solving the Titanic problem. Do not choose problems that are considered as 101 of data science.
With the extent of open-source data, there is no limit on the problems to be solved. Hence be more creative and stay away from popular data science projects. Your portfolio can’t contain infinite projects. It needs to be small and hence spend enough time thinking through the problems.
In the case of blogs, be original. Always think from a reader’s point of view and think if you are addressing the need of the reader. Do not wait for your article to become a perfect version. Just push it out and learn along the way. In the case of kaggle, always remember to read the ground rules and follow them.
How to create a better visibility
Your efforts should not just stop at building a good portfolio. Sharing your work with your professional network is essential, because
- You learn to articulate your work better
- It is a good means to get feedback from your network
- It helps in building your eminence
- It helps in growing your network
- You get to motivate more people to follow you
Some good places to share your work are Twitter, LinkedIn, and Reddit. Having a strong professional profile will help in your career. It could help in getting a better job opportunity.
Build an impressive resume
Now, it is time to update your resume to include your portfolio. Include links to all of your portfolios. Your resume plays a very important role in your career. Your resume will generally be the first point of contact with your recruiter. It should be impressive enough to bring them to your portfolio.
If you are interested in learning about creating an impressive resume, check my article below.
Building a Portfolio Website
A portfolio website is a digital resume. Many people I have spoken to think creating a portfolio website is tedious or expensive. The truth is your can build your portfolio website for free in just a few hours. I have my website created using GitHub Pages. GitHub pages are free and if you don’t bother having a custom URL then it’s completely free.
If you are interested in building a portfolio website for free, check the article below.
Conclusion
Don’t wait for the best project or the best blog to start your portfolio. Just get it started and make it better as you progress. Your career success in data science will be unstoppable if,
- You embrace continuous learning — It is a lifelong journey
- You could build an interesting portfolio
- Share your work with your network
- Build an impressive resume with links to your portfolio
- Create a portfolio website
To stay connected
- If you like this article and are interested in similar ones, follow me on Medium. Become a Medium member for access to thousands of articles related to career, money, and much more.
- I teach and talk about various data science topics on my YouTube Channel. Subscribe to my channel here.
- Sign up to my email list here for more data science tips and to stay connected with my work
