Handling Missing Values and Outliers in Data

Managing data quality involves mastering techniques to address missing values and outliers to make datasets clean and reliable. This process includes utilizing strategies such as multivariate imputation, outlier detection, and visualization.In this course, learn the fundamentals of data preparation, identify missing values and outliers in datasets, and explore strategies to detect and manage them effectively. Next, discover how to load and prepare data for cleaning, address missing data by dropping records or imputing values, and implement multivariate imputation to achieve more reliable datasets. Finally, explore methods to identify and visualize outliers using box plots and utilize the interquartile range (IQR) and z-score techniques to detect and cap outliers.After taking this course, you will be able to handle missing values and outliers in data.