Chad Readey Explains Why Communication Skills Matter in Data Science

Chad Readey, a data science and computer science student at Northwestern University, understands that success in data science requires more than technical talent. Communication—clear, confident, and strategic—is what transforms raw data into meaningful results. It's this blend of analytics and articulation that helps data science drive real-world impact.

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Turning Insight Into Action

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Data science has immense potential to influence decisions in sports, business, healthcare, and beyond. But the power of a model lies not just in its accuracy—it lies in its ability to be understood.

Chad’s work on an MLB pitch prediction project offered a sharp example. While logistic regression helped predict outcomes effectively, the value wasn’t in the algorithm alone. It was in making the insights digestible to teammates and coaches unfamiliar with technical terms. Sharing results in relatable, visual, and non-technical ways ensured the findings could be applied on the field.

This is where communication becomes a data scientist’s greatest ally. Without it, even the best insights remain unused.

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Mastering the Art of Data Storytelling

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Behind every impactful dataset is a story waiting to be told. Data storytelling is more than just creating dashboards—it’s about structuring information in a way that makes it relevant, memorable, and compelling.

Chad often draws from real-world examples to build that bridge. Whether it’s comparing pitch data across innings or using player trends to recommend strategies, his focus is always on clarity. Through storytelling, he transforms tables and models into messages—messages that inspire decisions.

Strong data storytelling skills also help build trust with stakeholders, from professors to project collaborators. They show not just what the data says, but why it matters.

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Working Across Teams and Disciplines

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One of the most important traits in any data science career is the ability to collaborate across different teams. Data scientists are often the link between raw data and strategic outcomes, working alongside engineers, designers, analysts, and executives.

Chad has experienced this firsthand through cross-departmental projects and teamwork in both sports and academic settings. Explaining a model’s results to a marketing student or coach isn’t the same as explaining it to another data scientist. Adjusting vocabulary, anticipating questions, and respecting different levels of expertise are part of effective collaboration.

This adaptability in communication is essential for ensuring alignment and buy-in—two things that can make or break the impact of a data science initiative.

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Presenting Technical Work with Confidence

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Communicating results through presentations, reports, and discussions is a core expectation in both academic and professional data science. Chad believes that clear presentation skills signal both competence and confidence.

Whether it’s defending model choices in front of peers or explaining a concept to a class, being able to walk others through the technical process builds credibility. It also encourages thoughtful feedback and ensures transparency in methodology.

Great communicators don’t just explain what they did—they help others understand why it was the right approach, what it means, and what should come next.

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Promoting Ethical and Transparent Practices

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Effective communication also plays a vital role in upholding ethical data science. When decisions are made based on models, the stakes are high. Miscommunication can lead to overpromising results, hiding limitations, or unintentionally misleading stakeholders.

Chad emphasizes the importance of transparency. That includes clearly stating assumptions, sharing potential biases in data, and discussing the reliability of results. Ethical data science starts with honest, responsible dialogue—and that begins with communication.

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More Than a Soft Skill

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For Chad and many in the data science field, communication is not a soft skill—it’s a core competency. It’s what ensures that a model doesn’t just work in theory, but leads to action in practice.

Those who can blend analytical thinking with strong communication are better equipped to lead projects, shape strategies, and deliver results that resonate across industries. In today’s data-driven world, the ability to explain, engage, and inspire is just as important as the ability to code.

That’s why communication isn’t just important in data science—it’s essential to making it matter.

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