What this book covers
Chapter 1, Database Introduction, is where you will discover the fundamentals of databases and their role in data wrangling, equipping you with a solid foundation to leverage SQL for efficient data manipulation and analysis.
Chapter 2, Data Profiling and Preparation before Data Wrangling, is where you will master the art of data profiling and preparation, empowering you to assess data quality, handle missing values, address outliers, and ensure data integrity before diving into the data-wrangling process using SQL.
Chapter 3, Data Wrangling on String Data Types, explores the ins and outs of manipulating and transforming string data using SQL, enabling you to clean, format, extract, and combine textual information efficiently in your data-wrangling workflows.
Chapter 4, Data Wrangling on the DATE Data Type, unlocks the power of SQL to handle date data effectively, covering techniques for date formatting, extraction, manipulation, and calculations, allowing you to wrangle temporal data with precision and accuracy in your analysis.
Chapter 5, Handling NULL Values, navigates the complexities of NULL values in datasets and teaches you SQL techniques to identify, handle, and manage null values effectively, ensuring data integrity and enabling seamless data wrangling for accurate analysis.
Chapter 6, Pivoting Data Using SQL, will help you master the art of transforming row-based data into a structured columnar format using SQL, enabling you to pivot and reshape data for enhanced analysis and reporting capabilities in your data-wrangling endeavors.
Chapter 7, Subqueries and CTEs, dives into the world of subqueries and Common Table Expressions (CTEs) in SQL, mastering the art of structuring complex queries, enhancing data-wrangling capabilities, and simplifying your data analysis workflows for optimal efficiency and clarity.
Chapter 8, Aggregate Functions, unleashes the power of aggregate functions in SQL, empowering you to perform powerful calculations and summarizations on your data, enabling effective data wrangling for extracting insightful statistics and metrics in your analysis workflows.
Chapter 9, SQL Window Functions, unlocks the advanced capabilities of SQL window functions, enabling you to perform complex calculations and analyses over customized subsets of data, revolutionizing your data-wrangling techniques for insightful data partitions, rankings, and aggregations.
Chapter 10, Optimizing Query Performance, helps you master the art of optimizing SQL queries, exploring techniques and strategies to enhance query performance, minimize execution time, and maximize efficiency in your data-wrangling workflows, ensuring faster and more effective data analysis.
Chapter 11, Descriptive Statistics with SQL, shows you how to harness the power of SQL to perform descriptive statistical analysis on your data, exploring SQL functions and techniques to extract key insights, summarize data distributions, and uncover patterns, enabling data wrangling for robust exploratory data analysis.
Chapter 12, Time Series with SQL, unleashes the potential of SQL for time-series analysis, exploring techniques for manipulating, aggregating, and extracting valuable insights from temporal data, empowering you to conduct effective data wrangling and uncover trends and patterns in your time series datasets.
Chapter 13, Outlier Detection, helps you master the art of identifying and handling outliers in your data using SQL, equipping you with techniques and strategies to detect, analyze, and manage outliers effectively in your data-wrangling workflows, ensuring data integrity and accurate analysis.
