000			02657nam a2200241 4500
005			20250801182316.0
008			250801b2014|||||||| |||| 00| 0 eng d
020			_a9781118422014
041			_aEnglish
082			_a006.3
100			_aMyatt, Glenn J. _eAuthor _97072
100			_aJohnson, Wayne P. _eCo-Author _97073
245			_aMaking sense of data I: A practical guide to exploratory data analysis and data mining
250			_a2nd ed.
260			_aNew Jersey: _bWiley Data and Cybersecurity, _c2014.
300			_axi, 235p.
500			_aA 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™ 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.
650			_a Understanding Relationships _97074
650			_aIdentifying and Understanding Groups _97075
650			_a Building Models from Data _97076
856			_uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=10066956
942			_cEB
999			_c1746 _d1746