Praise for the First Edition ‎"‎‎.‎‎.‎‎.‎a well‎-written book on data analysis and data mining that provides an excellent foundation‎.‎‎.‎‎.‎‎"‎ ‎-CHOICE ‎"‎This is a must‎-read book for learning practical statistics and data analysis‎.‎‎.‎‎.‎‎"‎ ‎-Computing Reviews‎.‎com A proven go‎-to guide for data analysis‎,‎ Making Sense of Data I‎:‎ A Practical Guide to Exploratory Data Analysis and Data Mining‎,‎ Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects‎.‎ Based on the authors‎'‎ practical experience in implementing data analysis and data mining‎,‎ the new edition provides clear explanations that guide readers from almost every field of study‎.‎ In order to facilitate the needed steps when handling a data analysis or data mining project‎,‎ a step‎-by‎-step approach aids professionals in carefully analyzing data and implementing results‎,‎ leading to the development of smarter business decisions‎.‎ The tools to summarize and interpret data in order to master data analysis are integrated throughout‎,‎ and the Second Edition also features‎:‎ * Updated exercises for both manual and computer‎-aided implementation with accompanying worked examples * New appendices with coverage on the freely available Traceis‎(‎TM‎)‎ software‎,‎ including tutorials using data from a variety of disciplines such as the social sciences‎,‎ engineering‎,‎ and finance * New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches * Additional real‎-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I‎:‎ A Practical Guide to Exploratory Data Analysis and Data Mining‎,‎ Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data‎.‎ The Second Edition is also an ideal textbook for undergraduate and graduate‎-level courses in data analysis and data mining and is appropriate for cross‎-disciplinary courses found within computer science and engineering departments‎.‎