Data scientists spend more than two-thirds of their time cleaning, preparing, exploring, and visualizing data before it is ready for modeling and mining. This textbook covers the important steps of data preparation and exploration that anyone who deals with data should know. This textbook is an excellent companion text for our other textbook Introduction to Biomedical Data Science. The data preparation and exploration methods we include are spreadsheet and statistics package approaches, as well as the programming languages R and Python. The reader is introduced to the free stat packages Jamovi and BlueSky Statistics. Multiple techniques for data visualization are presented. Medical datasets are used for demonstrations and student exercises. Importantly, chapter content is supplemented with YouTube videos. Chapters are well referenced (100+) and there is a chapter on health data resources so the reader can find data to prepare and explore on their own. Prominent issues such as how to handle missing data and imbalanced datasets are covered along with sections on descriptive statistics, visualization, correlations, handling duplicates and outliers, scaling, standardization, and much more. A downloadable Data Checklist is available on https: //www.informaticseducation.org
Data scientists spend more than two-thirds of their time cleaning, preparing, exploring, and visualizing data before it is ready for modeling and mining. This textbook covers the important steps of data preparation and exploration that anyone who deals with data should know. This textbook is an excellent companion text for our other textbook Introduction to Biomedical Data Science. The data preparation and exploration methods we include are spreadsheet and statistics package approaches, as well as the programming languages R and Python. The reader is introduced to the free stat packages Jamovi and BlueSky Statistics. Multiple techniques for data visualization are presented. Medical datasets are used for demonstrations and student exercises. Importantly, chapter content is supplemented with YouTube videos. Chapters are well referenced (100+) and there is a chapter on health data resources so the reader can find data to prepare and explore on their own. Prominent issues such as how to handle missing data and imbalanced datasets are covered along with sections on descriptive statistics, visualization, correlations, handling duplicates and outliers, scaling, standardization, and much more. A downloadable Data Checklist is available on https: //www.informaticseducation.org
