Outlier Detection and Data Cleaning with Boxplots

Raw datasets frequently contain anomalous values that distort summaries, skew metrics, and lead to misleading analytical conclusions. Developing the ability to accurately detect and manage these outliers is an essential foundational skill for anyone working with data. Through clear written explanations and practical examples, you will build a solid understanding of exploratory data analysis. You will learn how to examine data distributions, compute statistical boundaries, and apply systematic treatment strategies to prepare datasets for reliable analysis. What you'll learn: - Understand core statistical metrics including quartiles, medians, and the interquartile range (IQR) - Identify anomalies and extreme values using boxplot distribution principles - Apply standardized threshold calculations to distinguish genuine extremes from recording errors - Implement practical data treatment workflows including filtering, winsorization, and robust imputation - Practice data preprocessing steps through guided written walkthroughs and structured exercises - Evaluate the impact of outlier handling on overall dataset integrity and downstream reporting The course begins with foundational statistical terminology, distribution basics, and core definitions before progressing to practical outlier detection techniques and treatment strategies step by step. This course is created for beginners, aspiring data analysts, and professionals seeking to improve their data validation skills. No advanced background in mathematics or statistics is required. Start refining your data preparation skills and ensure the accuracy of your analytical results today.