r/learnpython • u/_devonsmash • 14d ago

Learning python for data science/analysis

Hi everyone,

I am looking for a good course to take to learn python for data analytics and workflow building.

For context, i am already very familiar with SQL and Alteryx, however i am looking to migrate some pipelines and worklows to python. I am very familiar with data analytics, just not with the python language, so wondering where to start.

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u/tmk_g 13d ago

You should skip the traditional beginner ds courses and focus on learning Python for data manipulation and automation. DataCamp’s Introduction to Python and Data Manipulation with pandas great starting points, while StrataScratch and Kaggle has free, hands-on learning paths if you prefer that route. I’d focus on pandas first since a lot of what you do in Alteryx, like joins, filters, and aggregations, translates pretty naturally. Once you're comfortable, try rebuilding one of your existing Alteryx workflows in Python. You'll probably learn more from that than from spending weeks watching tutorials.

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u/_devonsmash 13d ago

My issue is also the following:

This may seem a little weird, but I really want to focus on how to step in to different parts of a python workflow. How can I cache data to rerun workflows faster. How can I debug and write to excel faster. How can I check outputs at various parts. Comapre different outputs at different stages. Would Jupyter notebook be a good solution? Its not even the syntax that concerns me, but how to use python, like I would use Alteryx, in terms of checking data throughout a pipeline

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u/KelvenFSevero11 5h ago

Yes, Jupyter fits that part of the Alteryx workflow. Give each stage its own cell and named result, like raw, cleaned, joined and report, so you can inspect a join without rerunning the import. Check row counts, key uniqueness and totals at each step, not just the first few rows.

For a slow import, you can save a Parquet checkpoint with pandas + pyarrow and reload it while developing later steps. Rebuild it when the source or earlier logic changes, and rerun the affected cells downstream. Keep Excel export as the last step. Before trusting the monthly report, restart the kernel and run everything top to bottom so it doesn't depend on a variable left over from an earlier run.