Data Science Projects: Building a Strong Portfolio

In your data science career, you'll find that a lot of your background and reputation is based on the technical skills and soft skills that you can demonstrate in real-world situations. Potential employers will want to know that you have a proven track record of doing what your resume purports that you do: data analysis, data visualizations, programming languages, deep learning, and other relevant skills. One of the best ways to demonstrate how you can use those skills is through a data science portfolio.

With an estimated 17,700 data science job openings through 2023, more and more companies need to hire data scientists who can positively contribute to business decisions.¹ During the application process, a strong data science portfolio can get a hiring manager’s attention and put your resume at the top of the pile. The projects you include can help answer important questions: Can you be successful in a data science role at their company? Do you understand the business problems that data science can help solve and how to approach them?

Whether you’re looking for your first job or dream job in data science, portfolios matter. Regardless of your niche—artificial intelligence, big data, or machine learning—choosing a variety of interesting projects for your portfolio can make you stand out from the crowd.

In this post, we guide you through the process of creating an impressive data science portfolio, including planning projects, communicating their value and impact, and showcasing them on the right online platforms.

Selecting Relevant Data Science Projects

Compiling your data science portfolio is your chance to display how you have applied data science sources, tools, and techniques to solve real-world problems. If you’re finishing a master’s or Ph.D. in data science, including capstone projects or a thesis is entirely accepted and encouraged. However, any projects you include should tell a compelling yet concise story about your value to an organization. Make sure to select your best portfolio projects to showcase.

Rojesh Shikhrakar, who helps data science students upskill in their career path, advises selecting data science portfolio projects that closely align with your target industry and desired role.² For example, if you want to work in e-commerce and you’re applying to be a data analyst, include projects that show your ability to analyze customer behavior, product performance, or sales trends.²

William Chen, a Data Science Manager at Quora, adds that the most important criteria is whether the project details have interesting datasets and results.² Besides the results you achieved, it’s equally important to display how you have worked with teams to address challenges or incorporated feedback from mentors and industry experts.²

Project types to include:^2,3

• Code-based: Replicate real-world data science projects by taking a dataset and solving a problem around it. This could involve scraping and analyzing a dataset, training a model, or analyzing data on a trending topic, such as a news story
• Content-based: Demonstrate your communication and writing skills. Write blog posts or record podcasts where you break down data science topics for non-technical audiences
• Capstone: Integrate, synthesize, and display all your data science knowledge. Some options include analyzing satellite images or historical weather data for storm patterns, as well as exploring solutions to fake news and misinformation

Project Planning and Scope

Crafting a well-defined and impactful idea is the first step in any industry-level data science project. Experts recommend choosing a domain of interest where you have some prior experience.⁴ This will simplify the process of identifying a business problem or task you want to address using data science techniques and tools.⁴

To define the project area, conduct market research, review case studies, and talk with experts in the field to determine what challenges industry leaders face. If you align your data science project with these challenges, then you will learn more about the technology stack used by these companies and boost your chances of employment.⁴

Data Collection: Methods and Considerations

Once the project idea is defined, it’s time to collect real-world data to answer the business questions and solve the problem at hand. First, identify real-world datasets that align with your business/research needs. Avoid well-known datasets, such as Titanic or Iris, commonly used in beginner-level or educational projects. Second, it’s important to ensure the accuracy of the data, either as it's collected or as part of data preparation.⁵

To locate diverse datasets, these resources are a good place to start:⁴

• Awesome Public Datasets GitHub Repo
• Google Datasets
• Data.gov
• Kaggle Competitions
• Reddit R Datasets
• UCI Machine Learning Repository

Exploratory Data Analysis (EDA)

Exploratory Data Analysis is a technique used to improve the reliability and performance of machine learning models. By using this approach, you can identify data quality issues, such as missing values, outliers, and other problems with your data.⁶ For example, if you’re dealing with an emotional stock like Tesla and wish to build a model that can predict the stock market direction, you might want to consider as many data sources as possible.⁷ Exploring data from Google Analytics and Twitter Insights could reveal hidden patterns and relationships, which can ultimately drive better business decisions.⁷

Applying Data Science Techniques and Tools

According to the Data Science Institute, organizations who have failed to invest in data science roles now realize that this expertise can do wonders for their business.⁸ From the use of algorithms to data visualization, data scientists know how to leverage tools and processes to collect, extract, and analyze data in a way that leads to greater business efficiency and innovation.

Techniques you can showcase include:

• Feature Engineering: Transform raw data into features that can be used in supervised learning. This could involve designing and training better features, which can range from the color of an object to sounds⁹
• Machine Learning and Modeling: Use algorithms to identify patterns or make predictions on unseen datasets
• Statistical Analysis: Draw meaningful conclusions from raw and unstructured data, which often facilitate business-decision making
• Data Visualization: Effectively communicate insights from your data to different stakeholders using tools, such as Tableau or Power BI¹⁰

Real-World Problem Solving

Link your projects to real-world applications within specific industries. Showcase your ability to address challenges and solve problems that resonate with your target audience. Then, demonstrate the practical impact of your projects by providing actionable recommendations. Communicate how your work contributes to informed decision-making.

Collaboration and Open-Source Contributions

Contributing to open-source projects can make transitioning from academia to industry easier. This will help you connect with other top data engineers and scientists in the data science community. Take the case of David Robinson, Chief Data Scientist at Data Camp. As he completed a Ph.D. program, he worked on open-source development and regularly contributed to the programming site Stack Overflow, which provided evidence of his expertise.² An engineer found Robinson’s concise explanation of beta distribution online and was so impressed that he sent a job lead to Robinson via Twitter.² A few interviews later, Robinson was hired.²

Presentation and Portfolio Building

Before publicly sharing your work, take a look at the portfolio website of other data scientists to draw inspiration and see how they approach the process. You'll likely notice that they craft a compelling story and engaging description for each of their projects. You should do this as well by clearly articulating the business problem you were given, your unique approach, and the impact you made. Use data visualization and storytelling techniques to make your projects memorable.

Also, be sure to share some personal anecdotes or information that explain why and how you began your data science journey. With all of the technical skill and expertise that you'll bring, hiring managers will also want to know that you have a passion for the job and can work well with others.

Then, choose the right platforms to start building a presence. DagsHub, which caters to data scientists who want to host machine learning projects, allows uploads of Jupyter notebooks, Python code, and other documentation.⁴ GitHub is ideal for contributing to open-source data science projects and you can link to it from your resume, portfolio, and LinkedIn profile. Communities such as LinkedIn, Quora, and Medium provide spaces for sharing data science knowledge and expertise with others.^2,4 You may even create a personal website on a site like Wordpress or SquareSpace.

Whatever platforms you choose, make it easy for hiring managers to find and explore your work by adding links to your projects. Creating projects and updating your portfolio takes time, but the increased visibility and potential career opportunities are worth the effort.

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