A comprehensive guide to data cleaning that covers ten essential skills required for effective data preparation, from basic exploration to advanced techniques like normalization and standardization. T
he article provides practical examples, code snippets, and real-world applications while emphasizing how these skills directly impact career prospects in data science.
Reasons to Read -- Learn:
ten fundamental data cleaning skills that occupy 80% of a data scientist's time, with practical Python code examples and real-world applications that you can immediately implement in your work.
how to handle specific data challenges like missing values, anomalies, and inconsistent formats using proven techniques and tools, illustrated with examples from actual datasets like food_survey.csv.
professional data cleaning best practices and common pitfalls to avoid, including detailed explanations of normalization, standardization, and data validation techniques that are essential for accurate analysis.
publisher: Learn Data Science, Python, and AI skills – FASTER!
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