06 Oct 2022

How To Build A Good Data Science Portfolio

Ekeeda Moderator

Works at Ekeeda

Building a data science portfolio is an excellent way to show off your data science skills and talents to prospective companies & potential employers. It is quite difficult to stand out in this competitive data science jobs market; however, with a strong data science portfolio, you will have the edge and can easily beat the competition.

Do you want to build a strong data science portfolio? Join Ekeeda Data Science Program today! Learn with industry experts, and build real-life projects, GitHub profiles, and more. Clear all your doubts with 1:1 mentor experts from fortune 500+ companies. In this blog, we will share with you three simple ways to make your data science portfolio and stand out from the rest.

So, let’s get started and beat the competition to head for a great data science career.

Firstly, let’s understand what is data science portfolio is and what does it include? A data science portfolio will be the collection of all your work especially contributions to real-world projects. Your data science portfolio will include scripts of coding that you’ve done on various data science projects, data wrangling, data visualization, and write-ups on personal projects that you’ve completed. When you apply for data science jobs, your potential employer will look for your data science portfolio. Employers will go through the portfolio to evaluate the candidate’s skills and abilities, and thus it's important you have a well-crafted data science portfolio that showcases the best of your work; before you step in for an interview in any of the Data Science companies India.

What Is The Importance Of Data Science Portfolio?

Data Scientists jobs are favourite amongst the Gen Z workforce, but the competition for data science job roles is also intensifying on parallel grounds. With thousands of individuals pursuing career in data science, you will have to find ways to create an impression on potential employers.

Thus, having a creative data science portfolio becomes very important. Three reasons to have a strong data science portfolio:

Now that you have understood what is Data Science Portfolio and its importance, let's figure out some ways to make a strong Data Science Portfolio.

Three Easy Ways To Make A Good Data Science Portfolio

Your data science portfolio should reflect your skills and experience. So, these are three easy ways to make your data science portfolio strong and stand out:

Are you curious to learn more about data science? Read: Why Is Data Science A Good Career Option

Make Your Data Science Portfolio Visual

The portfolio is one of the major things hiring managers will look at before starting the interview process for data science. Just like your resume, managers will take a quick glance at your data science portfolio to check your skills, efforts, and problem-solving abilities. Therefore, to make a lasting impression, your data science portfolio should have more visuals than just mere numbers and statistics. Data visualization is the best way to tell a story on real-life projects that you’ve worked on. It’s eye-catching and quite impressive. A good data visualization will communicate complex pieces of information in a rather easy-to-understand form. Not everyone is a tech wizard and sometimes things have to be put across in layman’s terms and this is where data visualization comes into the picture.

Here are a few guidelines to follow:

  • Make sure it is visually appealing and has easy navigation

  • Try and include as many screenshots, graphs, and charts as possible to make your data science portfolio appealing and interesting.

  • Lastly, give a clear and simple explanation of any insights that you’ve derived from the data visualizations

Data visualizations will help you stand out from the competition and efficiently communicate your data science skills.

Looking to build an awesome Data Science Portfolio? Watch Video:

Source: Kaggle

Since data visualizations are a big part of data science, it's recommended to show charts & dashboards that you will create. If you’ve used Python in any of your data science certifications, do include line charts, bar graphs, and plots that you may have created using Matplotlib or Seaborn in your data science portfolio.

If you’ve created dashboards in Tableau, then don’t forget to publish them and link them to your portfolio. If you’re using Power BI, do take snapshots /GIFs of the dashboard in use & include them in your data science portfolio.

Visuals make your work look great and create a huge impact. This will help you stand out from the rest of the crowd and put an impressive presentation in front of hiring managers. They will get to know your skills, and a presentation based on which they will try to understand your communication skills. It’s just one instance of how to make your data science portfolio look great and interactive with visuals.

Let’s move on.

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A good data science portfolio will always include links to popular data science platforms. By including links in your portfolio, the hiring managers will look upon you as having higher credibility. This credibility will be created due to your experience and skills since many data science hiring managers also use these platforms by themselves.

Some popular platforms to display your data science work include:

If you have several data science and machine learning projects done in Python, do upload them to your personal GitHub account so that viewers can read your code. By linking your Github links to your portfolio, hiring managers can take a glimpse at your coding quality and proficiency!

It’s better to include a Readme file also in your GitHub profile and customize it that will help you display your data science skills and command on programming languages that you’ve learned.

Add Blog Posts To Complement Your Projects

Another thing that will help you create excellent data science portfolio will be documenting your projects in writing – through blog posts.

With comprehensive and concise blog posts on your data science portfolio, managers would be able to understand your thought process and how you approach the project and give solution to it. This is a great way to demonstrate your problem-solving skills and how you can solve business-related problems through predictive analytics, in front of the hiring manager. If you’re interested to publish data science content to boost your data science portfolio, do consider platforms like Medium towards data science, WordPress, etc.

Read More: How to Build Strong Data Science Portfolio as a Beginner

By writing blog posts, you’re able to offer more context and justification for every data science project. As a result, hiring managers will be able to appreciate your hard work and efforts behind the project.
Many individuals start personal blogs, that explains their learning in data science vs data analytics.

On A Concluding Note –

Data Science is now hailed as the highest-paid job in the world & millions of aspirants are earning lakhs through this amazing field. If you search for future careers in demand, Data science will ace the list. The Data Science career path is simply astonishing and shows no signs of slowing down in near future. Data science will continue to shape an individual's career, and influence businesses and companies can book high profits.

But you cannot sit and watch YouTube videos and learn data science; for this, you need the best data science course online designed by industry experts. A course that provides sufficient assignments, real-world projects, and 1:1 mentorship to clear your doubts. Experts who will help you build a strong data science portfolio, prepare for mock sessions and boost your confidence.  And also, by following these easy tips you can make your data science portfolio look impressive to beat the competition. We hope these tips will help you to create the perfect portfolio and wish you all the best for your career in data science. Upskill and grab job opportunities in top data science companies in India now!

Sign up Ekeeda Data Science course and take a head start for a great data science career!

Thanks and happy reading

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