Data Cleaning Techniques with Python and Pandas (Intermediate)
In this quest, you will dive deep into the world of data cleaning using Python and the Pandas library. You'll explore common data quality issues such as missing values, duplicates, and inconsistencies. Through practical exercises and real-world datasets, you'll learn how to implement various data cleaning techniques including handling null values, normalizing data, and transforming data types. By the end of this quest, you will be equipped with the skills to prepare your datasets for analysis, ensuring accurate and reliable outcomes. You'll also discover best practices in data cleaning and gain insights into optimizing your data processing workflow.
• Understand common data quality issues and their impact on analysis.
• Learn to handle missing data effectively using various techniques.
• Implement data transformations and normalization using Pandas.
• Gain insights into best practices for efficient data cleaning.
