How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (2022)

How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (1)

Image by Author | Elements by Free Vector | Statistics concept illustration

As a beginner, I had many questions about how do I start? How do I learn, or where do I get ideas to work on projects. So, after a long search, I found a project on data analysis. It took me 3 days just to write code, and I was happy with my first try, but then there was this big question of how do I share it with the world? I simply did not have good coding skills or documentation skills to showcase my work, so I stored it in the cloud and forgot about it. After a month, I was randomly looking for more projects on GitHub and found this amazing profile that motivated me to create my portfolio. That was the best decision I made as it put me on the map of the developer community, and soon after, I started to get emails from the recruiters and beginners about my projects.

Getting a job is usually the main reason for building a portfolio. Sometimes, it’s necessary if we don’t have the relevant education or experience (eugeneyan.com). In this modern world, employers are skeptical about hiring new graduates, so how do you convince them that you are best for the job? You display your skills by showing the work you have done in a previous project. The stronger your online portfolio, the higher chance you have of getting hired for your dream job.

"The portfolios are extremely critical to have because when you’re in the interview, it shows your real-world experience, so you can explain to an employer from A to Z the entire data science workflow."

— David Yakobovitch.

The other motivation is to create your personal project that satisfies your curiosity about learning new things. When we learn a new skill, we want to experiment and eventually build a working product that can be used in the real world.

In this article, we will learn the ways you can showcase your work as a data science beginner. You will learn about some new platform that makes your life easy and tips on building strong portfolios.


Let me just clear the misconception among data scientists. Yes, GitHub is necessary, and we all should learn git. As a data scientist, I use Github daily, where I look for interesting data sets and projects. This is the most popular platform among developers, and to be honest, the recruiter does check your GitHub profile before calling you for an interview.


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (2)

Image By Author |github

GitHub is a global collaborative platform where people share and collaborate on projects. As you can see in my profile below how I have contributed to other people’s projects and also worked on my own projects too.


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (3)

(Video) One trick to find almost any dataset for Data Science project -Free Datasets | Search FREE Datasets

Image by Author | kingabzpro

Tips for creating a solid profile:

  1. Create your profile page, and for a complete tutorial, check outSarah Hart’sblog.
  2. Document every project with links, cover images, and detailed descriptions.
  3. Fork the project that you like the most and send your first pull request (freecodecamp.org).
  4. Be active on this platform by contributing, bug reporting, and pushing your current projects.


Deepnote is much simpler than GitHub, and it's beginner-friendly too. If you are familiar with Jupyter notebook then it will be a piece of cake for you to publish your first project. My experience with Deepnote is absolutely amazing as the platform provides you all the qualities of GitHub but is much simpler and focused on the data scientist’s community.


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (4)

Image by Author | Pakistan Vaccination Progress

Recently, they introduced a Deepnote profile that will showcase all the notebooks you publish with your information and profile picture.


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (5)

Image by Author |Deepnote

Just like GitHub Gist, you can share a snippet of your code with your team or the public in general. I used Deepnote cell on all the Medium Publication and social media platforms. You can check my previous article to understand how to implement a Deepnote cell. Using snippets of code with output gives you the ability to share your projects on multiple platforms.

The reason I prefer Deepnote embedded cell over GitHub Gist is that it comes with output, not just static output but with interactive features.

You can use Plotly and display your chart in a Medium article:


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (6)

Tips for creating a solid profile:

(Video) How to Become a Freelance Data Scientist or Data Analyst

  1. Update your bio, profile photo, and contact information.
  2. Always add detailed descriptions about your project by using markdown cell.
  3. Use the cover photo to make your project stand out.
  4. Use App features in Deepnote to create Interactive webapp.
  5. Keep posting your old project or even reposting notebooks from GitHub.


DAGsHub is new to this world, and it’s making its name quickly by providing a one-stop solution for machine learning practitioners and data engineers. DAGsHub comes with a DVC server, MLflow, Visualizing pipeline, and GitHub Synchronization. We won’t be going deep into features but will focus on the features that make it stands out.

DAGsHub allows you to share your GitHub repository and create your data science project with the ability to visualize machine learning and data pipelines. It also has a hidden feature README.ipynb as your project description file, which is best for beginners who are not used to markdown and data scientists who love working on Jupyter Notebook. It is similar to GitHub, which means you need to learn both Git and DVC to use this platform properly.

"What I’ve seen other users enjoy is the ability to visualize their project structure via the pipeline, as well as the ability to see their data and models as an integral part of the project. Also, the fact that we are based on open-source tools instead of reinventing existing solutions is something people like."

— Dean

How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (7)

Image by Dean |dagshub

My profile is quite new, but I love this platform as they provide me with a complete machine learning ecosystem. I think I prefer it more than GitHub in terms of features and UI simplicity.


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (8)

Image by Author |DAGsHub

Tips for creating a solid profile:

  1. Learn DVC, Git, and MLflow to take full advantage.
  2. Add project description to your notebook and README.
  3. Update your profile by adding bio, avatar, and contact information.
  4. Try to add dvc.yaml and dvc.lock in your project to display data pipelines. For more information, check out Defining the Pipeline.
  5. Keep an active profile by contributing to open-source projects and by pushing your personal project. You can use fds cli to make your life easy and avoid mistakes.
  6. Takes full use of DVC by uploading your data and model on a remote server. Recruiters are interested in candidates that know the complete data science cycle from data ingestion to dashboards.


If you want to get noticed faster in the world of data science, you should create a Kaggle account and start contributing to competitions, datasets, notebooks, and discussions. When you become a grandmaster, people respect you and offer you better career opportunities. If you ask me, I suggest you create a Kaggle profile while learning the basics. Learn from experts and discover your niche. I am a huge fan of this platform as it provides support for a beginner to compete and develop innovative solutions for various industries. It is the backbone of AI research.


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (9)

Image by Author |Kaggle

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You can check out my profile below, as from the start, I have been contributing in various categories to gain ranks. Currently, I am an Expert, but with one gold and silver medal in the competition, I will become a Master, which is not easy, and honestly, I respect Grandmasters as they have proven that they are the best among other data practitioners.


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (10)

Image by Author |Kaggle

Tips for creating a solid profile:

  1. Be active on the platform by using new datasets and creating data analysis or machine learning models.
  2. Participate in discussion, learn from experts, and ask for help.
  3. Use web scraping to publish a new dataset.
  4. Participate in most competitions to learn several types of machine learning problems and to earn badges.
  5. Focus on publishing your best work with detailed descriptions and high-quality code.
  6. Write about yourself in bio and add contact details.


Writing blogs are the next step after creating your project on the above platforms. If you want to expand your audience, I will highly suggest you start with Medium. Writing a blog is not necessary, but you get more traction from various fields. The Medium platform allows you to create your profile and let you publish your articles under various publications such as Towards Data Science and Towards AI. You can develop your blogging site or use another similar platform such as Analytics Vidhya.


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (11)

Image by Author |Medium

Tips for creating a solid profile:

  1. Write blogs about the project you personally worked on.
  2. Create blogs on an emerging technology or on new data science applications.
  3. Do proper research while writing blogs and add citations to avoid platform rules violations.
  4. Use attractive cover photos for every blog.
  5. Always write about what you learn from your experience while developing data science projects.
  6. Don’t follow the trend, and focus on the things you are good at.


You can also display your project on a personal website, and if you are not a web developer, there are some simple tools available to make the process quite easy. You can check out How to Build a Data Science Portfolio Website with Hugo & GitHub Pages and Hugo for various templates.

My portfolio website has a project from all the platforms with short descriptions and subcategories. It took me three days to create the entire website and deploy it on GitHub pages.


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (12)

Image by Author | Portfolio

Tips for creating a solid portfolio website:

(Video) What Makes a GREAT Data Scientist?

  1. Add your skill, bio, and CV.
  2. Display your experience and a
  3. Showcase your projects with links to your GitHub or Deepnote projects.
  4. Make your website minimal and interactive so that the recruiter has an easy time scrolling through your entire portfolio.
  5. Keep your portfolio website up to date with the latest project you are working on.


I usually use Weight & Biases for machine learning experimentation and logging performance metrics of my models, but that changed with the introduction of the W&B profile. You can write a blog about your current project by using embedded links and graph integration. It is quite similar to other portfolio platforms I mentioned, but it comes with the perk of direct integration with Python libraries.

The Ayush profile has impressed me the most as he has been contributing to other organizations while writing blogs about machine learning.


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (13)

Image by Ayush | Weights &Biases

The W&B project has model performance metrics, as shown below.


How to Build Strong Data Science Portfolio as a Beginner - KDnuggets (14)

Image by Author | kaggle-seti

Tips for creating a solid profile:

  1. Join other data science organizations and participate in group projects.
  2. Use W&B API to display your machine learning project results.
  3. Write a blog using W&B metrics integration.
  4. Add a bio, profile picture, contact information.
  5. Try to engage in community discussion and always look for a new interesting project.


W&B is a wildcard as it is famous for logging experiments and not for portfolios, but the introduction of interactive blogs has given us the unique advantage of displaying your project and create a strong portfolio.

If you are a beginner, I will suggest you start with Deepnote, as it’s free for teams and give your beginner-friendly tools to get started. If you are looking to get noticed by the data science community, try creating your profile on GitHub and Kaggle. If you are into creating your brand, then start with blogging sites or create your website.

In the end, I want you all to create your profile on all the platforms I mentioned above, as they all come with unique advantages in impressing your potential employer. I know it’s quite overwhelming at the start, but once you get used to documenting and showcasing your projects, it will get easy.



Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.


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FAQs

How do you build a strong data science portfolio for beginners? ›

A data science portfolio should feature a combination of your code and documentation and some writing samples showing your ability to communicate effectively about data. They also help visualize your process and how you think about problem-solving.

How do you make an impressive data science portfolio? ›

Tips for creating a solid portfolio website:

Add your skill, bio, and CV. Display your experience and a. Showcase your projects with links to your GitHub or Deepnote projects. Make your website minimal and interactive so that the recruiter has an easy time scrolling through your entire portfolio.

How can I start data science with no experience? ›

Part-time jobs and internships can be found on sites like Upwork and Fiverr, as well as through social media and job postings. Make sure you practice solving coding challenges on LeetCode before your interview and research probable data science interview questions.

Should I put kaggle on my resume? ›

Kaggle have also just released a new dataset feature, which makes even more data accessible to hack around with. However, when it comes to what to put on your resume to showcase your project work, don't rely on Kaggle as evidence of your commitment or credentials. Here's why: Its hard to stand out..

How many projects are in portfolio data science? ›

atleast one project in each of these three important domains: Exploratory Data Analysis and Visualization. Classical Machine Learning on Tabular Data. Deep Learning (Computer Vision/NLP)

What should be included in a data portfolio? ›

A simple portfolio should include at least two sections, an “About me” section and data analytics projects.
...
About me
  1. How you got started in data analysis.
  2. What about data interests you most.
  3. Where your passions lie in relation to data analytics.
13 Jul 2022

Who is the father of data science? ›

The modern conception of data science as an independent discipline is sometimes attributed to William S. Cleveland. In a 2001 paper, he advocated an expansion of statistics beyond theory into technical areas; because this would significantly change the field, it warranted a new name.

Is Python enough for data science? ›

Python is a high-level, general-purpose programming language known for its intuitive syntax that mimics natural language. You can use Python code for a wide variety of tasks, but three popular applications include: Data science and data analysis. Web application development.

What is a data scientist salary? ›

Despite a recent influx of early-career professionals, the median starting salary for a data scientist remains high at $95,000. Mid-level data scientist salary. The median salary for a mid-level data scientist is $130,000. If this data scientist is also in a managerial role, the median salary rises to $195,000.

How can I be a self taught data scientist? ›

Master the art through practice, the way most data scientists learn even though they complete a full-term course. Join data science online communities to find projects to work on and collaborate with others on the platform to not only enhance skills but also to keep up with motivation.

Is IT easy to get data analyst job with no experience? ›

2. Is it Hard to Become a Data Analyst? Not if you have the background in mathematics and computer science data analyst jobs require. If you already have a solid background and some relevant experience, switching can be relatively easy.

Is IT possible to become a data scientist as a fresher? ›

Data science communities can be a good stepping stone for freshers. You can discover new ideas, showcase your projects, learn from experts, and even find new job opportunities.

How can I make a strong data science portfolio for free? ›

There are some key points I would suggest you write on your website:
  1. Your photo and a brief presentation about yourself on the Home page.
  2. a section with your Data Science projects. ...
  3. Put the link to your profiles on websites, like LinkedIn and Github.
  4. You can also put sections for your education path and experiences done.

Do I need a portfolio for data science? ›

A portfolio is often a key tool in the data science hiring process. Technical hiring managers and data scientists that interview you will look through it in order to gauge your skills, experience, and interests, and may ask you questions about it.

Do Kaggle winners get jobs? ›

So Will Kaggle Help You Get a job? All in all, Kaggle is a very useful tool in finding a machine learning job. An excellent Kaggle profile will definitely result in a lot of exposure from recruiters which will help you in getting a job!

Is Kaggle enough for data science? ›

The short answer is: Yes, and Yes! The data science frameworks used for Kaggle competitions are surprisingly effective for similar real life problems. Sometimes they even work for highly dissimilar problems! Best of all, the simple solutions that you can easily find under public Notebooks are already super effective.

Is Kaggle good for beginners? ›

Despite the differences between Kaggle and typical data science, Kaggle can still be a great learning tool for beginners. Each competition is self-contained. You don't need to scope your own project and collect data, which frees you up to focus on other skills.

Where can I host a data science portfolio? ›

Best Platforms for Hosting Your Data Science Portfolio
  • Social Media Platforms. As a data scientist, you can significantly benefit from social media that is not just limited to regular activities. ...
  • DataCamp Workspace. ...
  • GitHub. ...
  • Kaggle. ...
  • Set Up a Personal Website.
15 Jul 2022

How do you make a data science project from scratch? ›

  1. Step 1: Start small, with the basics. ...
  2. Step 2: Take an online certification for a defined approach. ...
  3. Step 3: Work through the Data Science lifecycle. ...
  4. Step 4: Create a diverse portfolio of projects. ...
  5. Step 5: Create visualizations & work on storytelling.
6 Jun 2022

What projects should I do for data science? ›

1. Beginner Level | Data Science Project Ideas
  • 1.1 Climate Change Impacts on the Global Food Supply.
  • 1.2 Fake News Detection.
  • 1.3 Human Action Recognition.
  • 1.4 Forest Fire Prediction.
  • 1.5 Road Lane Line Detection.
  • 2.1 Recognition of Speech Emotion.
  • 2.2 Gender and Age Detection with Data Science.
21 Sept 2022

How do I create a machine learning portfolio? ›

The type of machine learning projects to include in your portfolio
  1. Keep it relatively small. Your portfolio should include several projects, so try not to spend too much time on a single one.
  2. Make it a complete project. ...
  3. Choose an interesting topic. ...
  4. Use publicly available data sets.
25 Aug 2022

How many projects should I have in my portfolio computer science? ›

A coding portfolio should contain 4-10 projects. Make sure that you regularly update the projects you feature on your website. Regular updates demonstrate a strong work ethic. You should also choose projects that demonstrate the breadth and depth of your coding knowledge.

How do you build a data analyst portfolio that will get you hired? ›

Skills to Showcase On Your Data Analyst Portfolio
  1. 2) Math, Probability, and Statistics. ...
  2. 3) Programming Languages. ...
  3. 4) Data Visualization. ...
  4. 5) Critical Thinking. ...
  5. 1) An 'About Me' Section. ...
  6. 2) Your Data Analytics Projects. ...
  7. 3) Blogs, Testimonials (Clients, Previous Employers, etc.)
26 Sept 2022

What is the first step in creating a digital portfolio? ›

5 Steps to Creating Your Digital Portfolio
  1. Step 1: Choose a Platform for Your Digital Portfolio. ...
  2. Step 2: Setup Your Website. ...
  3. Step 3: Create Your Website Pages. ...
  4. Step 4: Add Content to Your Pages. ...
  5. Step 5: Share Your Digital Portfolio!
8 Jun 2022

How do you showcase a data science project? ›

Key takeaways

The components of your project description that you need on your resume include the objective/goal of the data analysis, your role in the project, a description of the data you used, a list of the models and tools you used, a link to your code repository, and a short discussion of the analysis results.

Do I need a portfolio for data science? ›

A portfolio is often a key tool in the data science hiring process. Technical hiring managers and data scientists that interview you will look through it in order to gauge your skills, experience, and interests, and may ask you questions about it.

How do you create a science portfolio? ›

Ways to build a data science portfolio
  1. Use R to clean, analyse, and visualise data.
  2. Navigate the entire data science pipeline from data acquisition to publication.
  3. Use GitHub to manage data science projects.
  4. Perform regression analysis, least squares and inference using regression models.
1 Mar 2022

What will be included in the portfolio of science? ›

There are three aspects of science (Figure 1): (1) scientific knowledge: what we know about the natural world, which would include crosscutting concepts; (2) scientific practices: skills and knowledge necessary for building scientific knowledge; and (3) nature of science (NOS): how science works (Bell et al.

How many projects are in portfolio data science? ›

atleast one project in each of these three important domains: Exploratory Data Analysis and Visualization. Classical Machine Learning on Tabular Data. Deep Learning (Computer Vision/NLP)

Where can I host a data science portfolio? ›

Best Platforms for Hosting Your Data Science Portfolio
  • Social Media Platforms. As a data scientist, you can significantly benefit from social media that is not just limited to regular activities. ...
  • DataCamp Workspace. ...
  • GitHub. ...
  • Kaggle. ...
  • Set Up a Personal Website.
15 Jul 2022

What are examples of student portfolios? ›

That means a portfolio could include anything from samples of writing the child has done, tests the student has completed, pictures of the child in the classroom, notes from a teacher about things the child has said or accomplished, self-assessments by a student, and more.

How can I become a good data scientist? ›

To become a data scientist, you will need to have strong analytical and mathematical skills. You should be able to understand and work with complex data sets. Additionally, you should be able to use statistical software packages and be familiar with programming languages such as Python or R.

How do I create a data science portfolio on GitHub? ›

Let's get started!
  1. Step 1: Create a GitHub Account. First, we need to sign up a GitHub account at https://github.com/. ...
  2. Step 2: Create a Repository Named user-name.github.io. ...
  3. Step 3: Customize Our Portfolio. ...
  4. Step 4: Upload Our Projects.

What is the first step in creating a digital portfolio? ›

5 Steps to Creating Your Digital Portfolio
  1. Step 1: Choose a Platform for Your Digital Portfolio. ...
  2. Step 2: Setup Your Website. ...
  3. Step 3: Create Your Website Pages. ...
  4. Step 4: Add Content to Your Pages. ...
  5. Step 5: Share Your Digital Portfolio!
8 Jun 2022

What are skills required for data analyst? ›

While data analysts should have a foundational knowledge of statistics and mathematics, much of their work can be done without complex mathematics. Generally, though, data analysts should have a grasp of statistics, linear algebra, and calculus.

How do I make a kaggle portfolio? ›

There are a couple of ways to build your portfolio on Kaggle:
  1. Through Discussions: Share your knowledge and experience on Kaggle and help others out. ...
  2. Through Notebooks: Craft notebooks on Kaggle that portray your code profile. ...
  3. Through Datasets: Create good-quality datasets that you and others can use for their analysis.

How do I write a student portfolio? ›

How to write a student portfolio?
  1. Categorise the portfolio to demonstrate the student's progress according to early learning goals or standards identified by your curriculum. ...
  2. Highlight the learning areas/domains that are involved in the lesson.

What are the contents of your portfolio answer? ›

As you begin to create your portfolio, there are several different categories that you should consider: Personal Information, Values, Personal Goals and History, Accomplishments and Job History, Skills and Attributes, Education and Training as well as Testimonials and Recommendations.

What are types of portfolio? ›

4 Common Types of Portfolio
  • Conservative portfolio. This type is also called a defensive portfolio or a capital preservation portfolio. ...
  • Aggressive portfolio. Also known as a capital appreciation portfolio. ...
  • Income portfolio. ...
  • Socially responsible portfolio.
25 Jul 2022

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