Cleaning Messy Data with Python: Fix Real World Datasets Fast
🎁 This is a free preview. Get the full course hereBad data produces bad models, and you can know every machine learning algorithm in existence and still get useless results if the data going in is messy, which real data almost always is.This course teaches you the five most common data quality problems you will encounter in actual data science work and exactly how to handle each one in Python, and after every topic there is a dedicated exercise notebook where you work through the same problems on real datasets before moving on, so nothing is left as just theory.By the end of this course you will have: Identified and handled missing values without breaking your analysis, then practiced the exact same techniques in a dedicated exercise notebook immediately after the tutorial Scaled and normalized data so your models can compare values fairly across different ranges, followed by a hands-on exercise notebook to apply what you just learned Parsed dates stored as messy text strings into proper datetime objects that Python can actually work with, with a dedicated exercise notebook after the tutorial to reinforce the skill Fixed character encoding errors that cause files to crash silently on import, then applied the fix yourself in a practical exercise notebook using real data Used fuzzy matching to automatically standardize inconsistent text entries that would otherwise break your grouping and aggregation, with a final exercise notebook to bring everything together Completed a dedicated exercise notebook after every single topic across the entire course, working through real messy datasets rather than sanitized examples Basic Python and pandas knowledge is recommended before starting this course, and everything runs offline on your own machine with no subscriptions, no cloud setup, and no hidden costs.This is not a course about clean data. It is about learning to work with the kind of data that actually exists in the real world.
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