Imagine Maria, a brand-new data analyst at a university’s Institutional Research office. She’s excited but overwhelmed by the variety of tools—PS Query Manager, R, Python, SQL, Power BI, Tableau—and unsure how to fit them into her daily work. To make things easier, Maria’s team launches a structured coaching plan rooted in adult learning theory.
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On her first week, Maria sits down with her coach, Sam. Instead of jumping straight into technical lessons, Sam asks about the data challenges Maria faces. Together, they pinpoint that cleaning large enrollment datasets is her biggest pain point. They create a SMART goal: “By the end of month one, automate 80% of routine data-cleaning tasks with Python.” Sam also sets the expectation that Maria should regularly reflect and adapt her workflow as new challenges arise. |
| During weeks two and three, Maria gets hands-on. Sam demonstrates Python and SQL directly inside Maria’s existing projects. They walk through Power BI use cases relevant to student retention analysis. Maria’s encouraged to think out loud—“Where would a dashboard or automation help me most?”—and to record ideas as they arise. |
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In weeks three to four, it’s Maria’s turn to try. She receives a small project analyzing retention rates with Python and Power BI. Sam checks in after each milestone. When Maria gets stuck choosing between Tableau and Power BI, Sam offers a simple decision flowchart. After each project, they hold a reflection session: “What worked well? What was tricky?” This builds Maria’s confidence and critical thinking. |
| As the plan moves into weeks four and five, Maria’s tasked with building her own “tech toolkit”—a living document of preferred tools, use cases, and guiding ethical considerations. Sam introduces the SAMR framework for thinking about how tools transform her workflow (Substitution, Augmentation, Modification, Redefinition). They even role-play a scenario where Maria has to pitch her chosen tool to a skeptical colleague. |
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By week six, Maria’s workflow is transformed. She’s not just using new tools; she’s actively reflecting, sharing what she’s learned with peers, and staying curious about new tech. Sam remains available for check-ins, but Maria’s developed the habit of continual learning. The plan encourages her to keep updating her toolkit, seek feedback, and help others—solidifying her confidence as a data analyst. |
Final Thoughts & Next Steps
The intention of this blog was to illustrate a story that you might be able to connect with either as person who coaches a colleague or someone who wants guidance and support from a supervisor or even a peer. This coaching approach emphasizes real-world relevance, gradual skill-building, guided practice, and lifelong learning. New analysts like Maria don’t just need to learn tools—they need to develop the mindset to adapt, reflect, and thrive as technology changes.
If you are interested in a high-level plan for coaching someone on how to integrate technology into their workflow, make sure to download the Scaffolded Data Coaching Plan for Analysts from the Technical library. If you want to chat offline about how to draft a detailed coaching plan, let's connect!
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