It is tempting to begin a data science project by reaching for a machine-learning model. I think a better first step is smaller: take a CSV file and answer one question you can explain in a sentence. That gives you room to practise the parts that matter before a model enters the picture.
Start with a question
Choose a dataset with a handful of understandable columns, such as date, category, and amount, or create a small sample yourself. Ask something specific: which category appears most often, or how does a value change over time? If you use personal records, remove identifying details; for a public project, check the dataset’s terms and cite its source.
Inspect before calculating
In a Python notebook, pandas can load a file with read_csv, then head and info give you an early look at rows, column types, and missing values. Check for inconsistent labels such as “Food” and “food” before grouping anything, because small differences can quietly split one category into two. Write down any cleaning choice that changes how the result should be read.
Make the work easy to revisit
Keep the notebook in a simple sequence: the question, the data source, inspection, cleaning, and a result with a short explanation. A clear chart can help, but it should answer the original question rather than decorate the page. When sharing the project, include the notebook and a README that explains how to run it and what you would improve next.
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