Summary Statistics and Data Imputation with Pandas and PySpark

Raw data is rarely clean, complete, or ready for analysis. To make accurate data-driven decisions or build reliable predictive models, you must first understand your dataset's distribution and handle missing values systematically. This course provides a clear, text-based guide to exploring datasets and resolving gaps using Pandas and PySpark. You will transition from a beginner to a confident data practitioner capable of preparing messy datasets for production-ready pipelines. By studying practical examples, you will learn how to calculate key summary statistics, identify patterns in missing data, and apply modern imputation strategies. What you'll learn: - Understand foundational statistics and how to calculate mean, median, mode, variance, and standard deviation. - Identify and analyze patterns of missing data in large datasets. - Apply Pandas methods to impute missing values in tabular datasets. - Scale your data cleaning workflows using PySpark for large-scale data processing. - Implement modern data validation practices to ensure pipeline integrity. - Choose the right imputation strategy, from simple mean replacement to advanced conditional fills. This course begins with core definitions and statistical concepts before guiding you through step-by-step code implementations in both Pandas and PySpark. You will learn how to handle missing records in structured datasets, such as user reviews and voting metrics, using industry-standard workflows. This course is designed for beginner data analysts, aspiring data scientists, and developers who want to build a solid foundation in data preprocessing. No prior experience with PySpark or advanced statistics is required. Start mastering data preparation and build cleaner, more reliable data pipelines today.