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10 Data Cleaning Skills You Need to Know – Dataquest
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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