How to Build a Strong Data Science Portfolio: 5-Step Guide

This is a guest post written by Author Austin Chia from AnyInstructor.com

So you want to be a data scientist? Great choice!

Data scientists are still the hottest jobs around, especially with the latest innovations like self-driving cars, generating AI voices, and AI-generated art.

But before you can start applying for data science jobs, you need to build a strong data science portfolio. A data science portfolio is a collection of your best data science projects that demonstrate your skills and abilities.

In this blog post, I’ll provide a 5-step guide on how to build a strong data science portfolio.

Read on to find out what these steps are and how they will help!

Step #1: Identify Relevant Skills and Tools

If you’re planning to create a data science project for the first time, it’s important to first identify the skills and tools you’ll need. This way, you can make sure that your project is feasible and that you have the necessary skills to complete it.

To do this, take a look at data science job postings and see what skills and tools are required for the role you’re interested in. You can also look at data science articles and blog posts to get an idea of the skills and tools that are commonly used in data science.

Some of the skills you may need for data science projects include:

  • Data wrangling
  • Data visualization
  • Machine learning
  • Statistical analysis
  • Deep learning
  • Big data processing
  • Mathematics
  • Programming

 

Although these skills are not a must for all data science roles, do pick a few to work on to boost your data science portfolio.

If you’re going for a data scientist position, for example, mathematics, deep learning and machine learning would be essential skills to have. And if you’re targeting a data analyst role, data visualization and data wrangling might be more relevant for you.

Also if you’re pursuing a general data science job, having some basic knowledge in each of these skills will put you in a good position to apply to as many jobs as possible.

Some of the tools you’ll need will depend on the programming language you choose. For Python data science projects, for instance,

If you’re heading into the data science field, you can consider researching these common tools:

 

Do ensure that you learn a good mix of programming, database and data visualization tools. You may also consider using AI code completion tools to assist you in learning to code.

However, you may want to consider learning according to the industry you’re intending to join. That’s because each company has different needs with a varied data stack. This is sometimes very industry-reliant.

Therefore, you’re going to want to learn relevant technologies for each industry.

Here are some examples:

  • Research: You should learn R programming or MATLAB for more statistical and scientific computing.
  • Big Tech: You should learn big data technologies such as Apache Spark to handle large datasets.
  • Marketing: You can consider learning about Google Analytics, Tableau or data visualization tools.

 

One useful tip is to look for data scientists in your dream company and search for them on LinkedIn. Have a look at their profiles to understand the industry’s need and its required data tech stack. If you’re feeling daring, you can also connect with them to hear more about the tools they use.

Therefore, it’s highly important to research and understand your industry—so you can learn and develop the right skill sets.

Step #2: Learn from Online Resources

Now that you’ve identified the relevant skills and tools needed for data science, it’s time to start learning! There are plenty of online resources that can help get you started.

Some types of resources you can choose from include:

  • Self-paced boot camps
  • Instructor-led boot camps
  • YouTube video tutorials
  • Blog posts
  • Data science e-books

 

Now, this can be slightly overwhelming if you’re learning for the first time. That’s why you can consider these two simple criteria when choosing a resource:

  1. Does it give me practical experience?
  2. Is it simple enough for beginners?

 

With these two simple questions in mind, look for courses and resources you might benefit from.

I’d recommend having a good mix of learning resources in your data science portfolio. This means that you should include a combination of blog posts, online courses, boot camps, and books to strengthen your knowledge holistically.

Step #3: Do Unique Projects

Once you’ve gone through some online resources and feel confident enough to start working on some projects, it’s time to find some unique project ideas.

Why do I mention unique?

Many data science projects I see online come from reused datasets with a very similar project scope. To really build a strong portfolio, you’ll need to come up with ideas that are unique.

Doing unique projects is important for several reasons: It helps you stand out among all the boring, common projects. You get to learn new things while doing the project. And lastly, it builds self-confidence in the tools you learn.

Some ideas for unique data science projects include:

  • Creating a data visualization of your favorite music artist’s world tour
  • Designing a recommender system to predict movie box office results
  • Building a machine learning model to identify plagiarism in online articles

 

Project ideas can also vary according to industry:

  • Finance: Analyzing finance data for trading—using data science to trade stock
  • Health & Fitness: Tracking your own health with data from fitness trackers
  • E-commerce: Creating a data visualization of customer behavior on your favorite online shopping website

 

I still remember my first unique data science project to this day—a song genre recommender. I was working with several others to put together a simple algorithm in Python as a recommendation model. I had to import data from Spotify’s API onto my local PC hardware, clean them, and finally select features we wanted to include in our model.

Although our predictions weren’t the best (considering it was my first unique project) it was a fun and exciting project to embark on.

Why Do Data Science Projects?

  1. To learn from common mistakes faster
  2. To understand the business need of projects better
  3. To practice your technical skills

 

Step #4: Upload Your Work To An Online Platform

Now that you’ve completed some data science projects, your next step is to upload your work to an online platform. You’ve already put in all the hard work to create great projects and it’s time for you to showcase them online!

This is important for several reasons: It makes your work easily accessible to potential employers. It allows you to get feedback from other data scientists. It provides you with a reference for all your learnings.

There are a few different ways to do this:

    • Host a website on WordPress
    • Upload projects onto GitHub
    • Make data visualizations using online tools such as Tableau Public or Google Data Studio
    • Use data science portfolios such as Kaggle kernels or Kaggle datasets

 

By having your projects up on an online platform, you’re one step closer to impressing potential employers with your data science portfolio.

I’ve personally had to have several platforms to showcase my projects—Tableau Public, GitHub, my personal website and my data analytics blog (hosted on WordPress).

If you’re keen to go one step further, you can also do the same. Remember, the more you share with your potential employer, the more they can assess your skill.

Step #5: Write Blog Posts to Complement Your Projects

If you want to further improve your data science portfolio, consider writing blog posts about the projects you’ve done.

This has a few benefits: It allows you to go into more depth about your project. It shows potential employers that you can communicate your data findings clearly. And it helps you show your passion and love for a specific area in data science!

If you’re not sure where to start, consider writing a blog post about one of these data science topics:

    • How you collected and cleaned your data
    • The data analysis methods you used
    • The results of your data analysis
    • What challenges you faced during your project
    • What you learned from doing the project

 

I’ve written blog posts on data analytics myself—some of them on the data science projects I completed. In my experience, these were great learning opportunities and also helped me get better feedback from readers.

For example, if you’re learning the R programming language for data science and you’re thinking of presenting your code, do consider using Rmarkdown to document it. RStudio (Posit) also has a new feature in Quarto that provides a blogging platform to publish content with your code.

Many other data scientists also look to Medium to write blog posts for their projects. This platform is free and has a high readership within the data science community. For these reasons, this would be a good option for both students and job seekers alike.

Related Questions

Do You Need a Portfolio for Data Science?

A portfolio is needed for data science to show employers what you are capable of and the impact that your work has had. A data science portfolio can include data visualizations, blog posts, machine learning models, etc.

This applies to different roles in data science as well. For data analysts, a data analytics portfolio can showcase their skills in data wrangling, data visualization and data analysis. For data engineers, a data engineering portfolio can highlight their work in building efficient data pipelines and ETL processes.

Why Should You Build a Data Science Portfolio?

There are several reasons why data scientists should build a portfolio. A data science portfolio can help data scientists stand out from the competition, show their mastery of data science tools and techniques, and demonstrate the impact of their work. A data science portfolio can also be a valuable way for data scientists to keep track of their projects and learnings over time.

What Does a Data Science Portfolio Look Like?

A data science portfolio can take many different forms. It can be a website, blog, GitHub repository or even data visualization. The most important thing is that your data science portfolio showcases your skills and the impact of your work.

What Are Some Tips for Building a Data Science Portfolio?

Here are some tips for building an impressive data science portfolio:

    • Share them publicly with your network on LinkedIn
    • Work on projects that you’re naturally curious about
    • Get all your code up on GitHub and ask for feedback

 

By following these tips, you’ll be well on your way to impressing potential employers with your data science portfolio.

Final Thoughts

A data science portfolio is a great way to show off your skills and impress potential employers. In this article, we’ve gone over a five-step guide on how to build a strong data science portfolio. We hope you found this helpful and that you’ll use this guide to create a data science portfolio that will make you proud.

Thanks for reading and good luck in your data science journey!

Austin Chia Guest Bio

Author Bio

Austin Chia writes about tech, analytics, and software at AnyInstructor.com. After breaking into data science without a degree, he seeks to help others learn more about the data science and analytics field through content. He has previously worked as a data scientist at a healthcare research institute and a data analyst at a health-tech startup.

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There’s never been a better time to start learning new skills. Emerging technologies are revolutionizing the way we work, play, and live. Learning these disciplines deepens your understanding of the world around you and provides a fountain of knowledge to explore new frontiers and technological breakthroughs.

The Data Incubator offers intensive training bootcamps that provide the tools you need to succeed as a data scientist or data engineer. You will gain hands-on experience working on real projects and apply what you’ve learned in our curriculum to solve problems in your work or for clients. Our curriculum includes machine learning, natural language processing, predictive analytics, data visualization, and more.

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