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data science portfolio

As a beginner in data science, one of the hardest things is to land their first job and to build an impressive portfolio. We are all aware of the vicious cycle of not getting a job because of no experience, and no experience because of no job.

Most of us get stuck in this cycle either when we are starting our careers or when we are transitioning into another career. A career in data science is no different, but the question arises of how to break through this cycle and land your first job.

To answer this, Data Science Dojo collaborated with Avery Smith to conduct a webinar for every beginner in data science who is stepping into the real world. He discussed some useful tips to help data scientists build a data science portfolio.

Avery’s secret to breaking into the data science industry is through “Projects”, which you can create to show off your skills and knowledge in your next interview. In this session, Avery took us through the best practices for creating a project that makes you stand out and helps you land your dream job.  

create best projects - data science portfolio
Learn the 5 useful tips to create best data science projects

5 tips to create the best projects to improve portfolio


1. Choose the right topic 

Choosing a topic that you can write passionately about is very important because that is the only way you will feel motivated to finish the project. If you are wondering where passion comes from, it could be something out of your hobbies or your next/dream job. The fun trick taught by Avery is to think about any hobby or industry you are passionate about.


Read more about data science portfolio


Next, go to your LinkedIn job section and search for data-related roles in the fields you are interested in. After that, find a job or company that you would like to work in, and scroll down to look for the qualifications required for that job.

For instance, if the job requires SQL, Python, and Tableau skills, you should create a project that involves these three. You will also look at what the company does and its job requirements, to make your project as relevant as possible.  


2. Get good data 

If you have successfully decided on a topic to work on, now you must be thinking about where to find relevant data. There are four main ways of gathering data, as Avery pointed out: 



Gathering data
Gathering data in four steps


  • Download CSV 
  • Using an API 
  • Web scraping 
  • Collecting your data 


These four ways are mentioned in order to increase the difficulty to get each and the more unique it is. Although downloading a CSV is easy, it’s not overly impressive. Collecting your own data is exceedingly difficult but is unique and will make a larger impact in showing off your skillset.  


3. Decide on the type of project 

Type of project
Types of projects


There are three types of projects: 

  • Skillshare- a few steps in Python or a SQL query or a graph in the dashboard. It’s not like a whole project but a section of the project.  
  • Data Story- a whole paragraph with multiple lines of codes, and multiple graphs which is more like a complete article.  
  • Product- a tool or app that you can give to someone, and they can use it.  

The types are in order of increasing difficulty and impressiveness, skill share is easiest to do but not very impressive while on the other hand product is very difficult but highly impressive. In the webinar, Avery explained these using examples for each type of project. 

4. Focus on visualization 

 Visualization is one of the easiest to do, looks impressive, and you can start it today. For beginners who feel like they are not ready to work on a big project, data visualization is something you can start working on day one. There are several tools and software available which are easy to learn and can help in creating amazing projects, you can learn more about visualization tips and techniques.




5. The best project is the one you can finish 

Many data scientists have several projects that they started but never got the chance to finish. A very little-known fact is that these projects can become their marketeers by attracting recruiters and helping them land the right job. For that you need to get these projects out there, nothing is going to happen if keep them restricted to your computer.

For this reason, we need to finish and publish these projects. Avery’s advice has been to avoid the scenario where you have several unfinished projects and you decide to start another, the goal is to have published projects. To better understand it, Avery introduced us to the concept of Modular Projects.  


What are modular projects 

Avery explained the concept of modular projects with marathons. People who run a marathon don’t do it all at once. First, they run 5k, then 10k, maybe a half marathon, and probably then they can run a full marathon. Similarly for a project, don’t go for a marathon project off the start. Instead, start with 5k.

You can always imagine a marathon, but try to reach a 5k first, publish, and then move ahead for a 10k. The idea of a modular project is to pick a low finish line and work your way up.  


In nutshell, Avery provided all beginners with a starting point to enter their careers and prove themselves.  This is your sign to start building a project right away, considering all the tips and tricks given in the webinar.  

December 19, 2022

A data science portfolio is a great way to show off your skills and talents to potential employers. It can be difficult to stand out in the competitive data science job market, but with a strong data science portfolio, you will have an edge over the competition.

In this post, we will discuss three easy ways to make your data science portfolio stand out. Let’s get started!

Data science portfolio infographic

What does a data science portfolio include?

A data science portfolio is a collection of your work that demonstrates your skills and abilities in data science. These profiles typically include a mix of scripts of code from data science projects you’ve worked on, data visualizations you made, and write-ups on personal projects you’ve completed.

When applying for data science positions, your potential employer will want to see your data science achievements. Employers use portfolios as a way to evaluate candidates, so it is important that your data science portfolio is well-crafted and showcases your best work.

Why is it important for your data science portfolio to stand out?

With data scientist jobs being highly favored among the Gen Z workforce, the competition for such data science roles is starting to heat up. With many pursuing careers in data science, you’ll need to find ways to stand out among the crowd.

Having an excellent portfolio is important for 3 main reasons:

  1. It acts as an extension of your resume
  2. It shows expertise in using certain tools
  3. It demonstrates your problem-solving approaches

Now let me go through some ways you can make your data science portfolio stronger than most others.

What are 3 easy ways for your data science portfolio to stand out?

Your data science profile should be a reflection of your skills and experience.

With that said, here are three easy ways to make your data science portfolio stand out:

  1. Make it visual
  2. Include links to popular data science platforms
  3. Write blog posts to complement your projects

1. Make it visual

Portfolios are one of the major component’s employers look at before starting the interview process in data science.

Much like your resume, employers are likely to spend less than one minute looking at your data science portfolio. To make an impression, your data science documentation should be heavily focused on visuals.

Some data scientists’ portfolios I love are those that use data visualizations to tell a story. Great data visualization can communicate complex information in an easily digestible format.

Here are some guidelines you can follow:

  • Ensure it is visually appealing and easy to navigate
  • Include screenshots, graphs, and charts to make your data science portfolio pop
  • Explain any insights found in the visualizations

Including data visualizations in your profile will help you stand out from the competition and communicate your skills effectively.

Since data visualizations are a big part of data science work, I’d recommend showing off some charts and dashboards you’ve created. If you’ve used Python in any of your data analytics certificates, do include any line charts, bar graphs, and plots you have created using Plotly/Seaborn in your data science portfolio.

If you’ve created some dashboards in Tableau, do publish them on Tableau Public and link that up to your portfolio site. Or if you’re a Power BI user, do take screenshots/GIFs of the dashboard in use and include them in your portfolio.

Source: My Tableau Public profile

Having visuals to represent your work can make a huge impact and will help you stand out from the rest. This is just one example of how you can make your data science portfolio stand out with visuals.

Let’s move on.

2. Include links to popular data science platforms

A strong data science portfolio should include links to popular data science platforms as well. By having links of popular data science tools in your portfolio, your employers would perceive you as having higher credibility.

This credibility comes from the demonstration of your experience and skills since many data science hiring managers use these platforms often themselves.

Some common platforms to link and display your work include:

  • GitHub
  • Kaggle
  • Stack Overflow
  • RPubs
  • Tableau Public

If you’re someone who has had several machine learning projects done in Python, do upload them to your personal GitHub account so others can read your code. By linking your GitHub repo links to your portfolio, employers can take a glimpse at your coding quality and proficiency!

One tip I’d recommend is to include a README file for your GitHub profile and customize it to showcase the data science skills and programming languages you’ve learned.

3. Write blog posts to complement your projects

The last way to create an outstanding data scientist profile is to document your portfolio projects in writing – via blog posts!

Having comprehensive and concise blog posts on your data science portfolio shows employers your thought process and how you approached each project. This is a great way to demonstrate your problem-solving skills and how you can solve business problems through analytics for your employer.

For example, if you’ve written some scripts in R for your data mining project and would like to help your employers understand the steps you took, writing an accompanying blog post would be perfect. In this case, I’d recommend trying to document everything in Rmarkdown as I did here.

If you’re interested to publish more data science content to further boost your LinkedIn profile as data scientist, do consider these platforms:

  • Medium
  • TowardsDataScience
  • WordPress (your own blog site)

By writing blog posts, you’re able to provide more context and explanation for each data science project in your portfolio. As a result, employers would be able to appreciate your work even more.

Source: My analytics blog, AnyInstructor.com


By following these three easy tips, you can make your data science portfolio stand out from the competition. I hope these tips will help you in perfecting your portfolio and I wish you all the best in your data science career

Thanks for reading!

Author bio

Austin Chia is the Founder of Any Instructor, where he writes about tech, analytics & software. After breaking into data science without a degree, he seeks to help others learn about all things data science and tech. He has previously worked as a data scientist at a healthcare research institute and a data analyst at a health-tech startup.

September 2, 2022

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