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Data Analytics · Personal Project
Turning NBA Season Data Into Structured Analysis
A practical analytics project built to strengthen my ability to work with structured data, query information with SQL, and use Python to explore basketball performance in a repeatable way.
PythonSQLData AnalysisGitHub
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DATA01Collect & structure
SQL02Query
PY03Analyze
01 · The Challenge
Move from raw sports data to useful information.
The goal of the project was not to build an advanced prediction platform. It was to practice a core analytics skill: take structured data, ask useful questions, and create a logical process for finding answers.
02 · Analytical Workflow
Ask → Query → Analyze → Interpret
The project reflects the same structured problem-solving approach I use in engineering work.
01Define the questionDecide what comparison or performance question to explore.
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02Structure the dataWork with the information in a form that can be queried.
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03Query with SQLFilter and retrieve the relevant data.
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04Analyze with PythonApply logic and calculations to the selected data.
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05InterpretTurn the output into a useful observation.
03 · Why This Project Matters
The subject is basketball. The skill is analytical thinking.
Using NBA data made the project engaging, but the real value was learning how to break an open-ended question into smaller analytical steps. The same pattern applies to manufacturing data, engineering test results, business metrics, or operational problems.
What am I trying to understand?
A useful analysis starts with a specific question rather than immediately writing code.
What information do I need?
Identify the fields and records needed to answer the question.
How should the data be compared?
Use SQL and Python as tools for applying the analytical logic.
What does the result actually mean?
Code produces an output; analysis requires understanding what the output says about the original question.
DATAnot just code
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Core Idea
Tools matter less than the logic behind them.
Python and SQL were useful because they allowed the analysis to be structured and repeatable. The bigger lesson was learning to decide what to ask, what data matters, and how to move from a question to a defensible answer.
Structured thinkingSQL queryingPython logicData interpretation
Start with the question.
Writing code before defining the problem can create work without producing a useful answer.
Keep the analysis simple.
A straightforward query or calculation is often more useful than unnecessary complexity.
Make the process reusable.
Keeping the work in GitHub makes it easier to revisit, improve, and demonstrate the analytical process.
Project Sequoia Principle
Data becomes useful when it helps answer a clear question.
The project strengthened my ability to move logically from a problem, to the required information, to analysis, and finally to an interpretation that can support a decision.