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  • Writer's pictureKelly Adams

The Power of Collaborative Projects in Data Analytics

Three professionals engaged in a collaborative work session around a table. They are focused on reviewing and discussing printed wireframes and digital interface designs laid out before them. The person in the foreground is holding a smartphone, presumably to compare the digital version with the printed layouts, while the other two, including a man in a blue shirt and a person in glasses, are using tablets to view additional information. The setting suggests a creative, tech-oriented workspace with a blurred background featuring a wall with sticky notes, indicative of brainstorming activities

Many aspiring data analysts, myself included, initially focus only on personal portfolio projects to improve their skills and showcase their capabilities. These are a great way to learn and work on projects that interest you. But while these projects are foundational, actually working as a data analyst made me realize a vital component I had overlooked in my portfolio projects: collaboration. Real-world data analytics teams thrive on collaboration and provides great learning opportunities that solo projects can't. This post delves into the importance of collaborative projects for aspiring data analysts, outlining the benefits and guiding you on where to find them.

What are Collaborative Projects

Collaborative projects are when you work with others to work on a project. It has the added flexibility that they don’t necessarily require in-person interaction, they can be done remotely.

Why is it Important?

Collaborative projects help you gain skills you wouldn't get just working with yourself. A few key skills are:

  • Effective communication - when working with others you have to clearly explain ideas and actively listening to team members, ensuring that all opinions are heard and understood. This skill is crucial for coordinating efforts and keeping everyone on the same page. Being able to convey your ideas clearly and concisely is an important trait for any analyst. Since you'll be doing a lot of communication with stakeholders, your team members and anyone else in your company.

  • Project management - it requires organizing tasks, setting deadlines, and tracking the progress of the project. Which needs structured approach to ensure timely completion and the ability to adapt to changes. Even as a junior data analyst you need a way to be able to manage the different projects you're working on. This is a great way to start practicing how to manage different tasks and be able to complete them on time.

  • Conflict resolution - identifying and addressing disagreements within the team, using problem-solving skills and negotiation to find solutions that satisfy all parties. Which helps maintain a productive team environment. Not everyone has the same opinions and there may be some disagreement. As a data analyst you're expected to be the subject matter expert and sometimes you may disagree with a stakeholder on what elements need to be in a dashboard. It's good to be able to resolve conflict and disagreements in a professional way. This is a great way to get practice in. 

Benefits of Collaborative Projects

Besides getting practice working with others, learning how to communicate, and resolve conflicts. All skills you need to have as an effective data analyst. There are some additional benefits like:

  • Get experience working with others - Collaborative projects get you in a team setting, teaching you how to effectively collaborate, coordinate tasks, and work towards a common goal, similar to the dynamics of professional data work.

  • Learn from others - By working alongside people with diverse skills and perspectives, you gain insights and knowledge you might not have encountered on your own, broadening your understanding and approach to data analytics.

  • Teach others -Sharing your expertise with team members not only helps them learn but also reinforces your own understanding and improves your ability to communicate complex concepts clearly and effectively. The Richard Feynman technique at work. 

  • Stay Accountable - Collaborative projects encourage you to meet deadlines and contribute your best work, knowing that others are relying on you for the project's success. This accountability can help you complete projects more efficiently and with greater dedication.

Where to Find Projects

Networking with Professionals

Reach out to your professional network to find others interested in working on a project. This lets you and your collaborators pick topics that interest you, and keeps you all engaged. You can reach out either to previous coworkers, your professional network on LinkedIn, or go to local data analytics meetups.

Join Online Communities 

You could also join a community where they give you projects and challenges to work on. I’ve been particularly enjoying Discord communities, a platform which was originally used for gaming, but now it’s expanded to all different types of hobbies. It’s a great way to find and network with other people with similar interests. 

I have two recommendations for discord communities: 

  1. DataFrenchy Community which is run by Chris French. He host bi-monthly community projects and weekly data challenges to work on. The focus is  solving a data focused problem using any tool you want. You can work alone or with other people. This is also a great way to just meet people in general and connect with others who may be interested in a collaborative project that isn't run by Chris.

  2. The Break into Data, for example, is actively looking for contributors for various projects, including building out their Discord bot. You can also volunteer your time to technical contributions or become a mentor. My own contribution included adding documentation and providing product feedback.

Contribute to Open Source Projects

Explore GitHub for repositories that align with your interests and offer your contributions. This is a great way to gain practical experience with git/GitHub, and become familiar with essential practices like making commits, pushing and pulling code, and managing branches. Though not common in every analytics role, these skills are valuable for those who may want to move to a more technical role like data engineer or data scientist.

Join a Hackathon 

Joining a hackathon is another fun way to work on collaborative projects. Typically these are done in small groups but it depends on the event. I have a connection who regularly participates and he loves it. It's a great way to really enhance your technical ability, since it provides hands-on experience tackling real world problems. But it does take up a lot of time so it's definitely not for everyone’s schedule. But it's a great experience if you can do it.


While personal projects are the base of your analytical expertise, collaborative projects are good for connecting you to the broader data analytics community. They help with developing diverse perspectives, mutual learning, and accountability. By working on collaborative projects, whether through professional connections, online communities, or contributing to open-source projects, you're not only improving your analytical skills but vital soft skills like communication and teamwork.


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