000945193 000__ 03407cam\a2200445Mi\4500
000945193 001__ 945193
000945193 005__ 20230306152506.0
000945193 006__ m\\\\\o\\d\\\\\\\\
000945193 007__ cr\nn\nnnunnun
000945193 008__ 201001s2020\\\\cau\\\\\o\\\\\|||\0\eng\d
000945193 019__ $$a1199055978$$a1202468863$$a1206402827$$a1223091320
000945193 020__ $$a9781484261033
000945193 020__ $$a1484261038
000945193 020__ $$z9781484261033
000945193 020__ $$z148426102X
000945193 020__ $$z9781484261026
000945193 035__ $$aSP(OCoLC)on1204059991
000945193 035__ $$aSP(OCoLC)1204059991$$z(OCoLC)1199055978$$z(OCoLC)1202468863$$z(OCoLC)1206402827$$z(OCoLC)1223091320
000945193 040__ $$aDCT$$beng$$cDCT$$dOCLCO$$dEBLCP$$dYDX
000945193 049__ $$aISEA
000945193 050_4 $$aQA75.5-76.95
000945193 08204 $$a005.7$$223
000945193 1001_ $$aFoxwell, Harry J.,$$eauthor.
000945193 24510 $$aCreating Good Data :$$bA Guide to Dataset Structure and Data Representation /$$cby Harry J. Foxwell.
000945193 250__ $$a1st ed. 2020.
000945193 264_1 $$aBerkeley, CA :$$bApress :$$bImprint: Apress,$$c2020.
000945193 300__ $$a1 online resource (XV, 105 pages) :$$billustrations.
000945193 336__ $$atext$$btxt$$2rdacontent
000945193 337__ $$acomputer$$bc$$2rdamedia
000945193 338__ $$aonline resource$$bcr$$2rdacarrier
000945193 347__ $$atext file$$bPDF$$2rda
000945193 5050_ $$aChapter 1: The Need for Good Data -- Chapter 2: Basic Data Types and When to Use Them -- Chapter 3: Representing Quantitative Data -- Chapter 4: Planning Your Data Collection and Analysis -- Chapter 5: Good Datasets -- Chapter 6: Good Data Collection -- Chapter 7: Dataset Examples and Use Cases -- Chapter 8: Cleaning your Data -- Chapter 9: Good Data Anayltics -- Appendix A: Recommended Reading.
000945193 506__ $$aAccess limited to authorized users.
000945193 520__ $$aCreate good data from the start, rather than fixing it after it is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data. Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results. Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed. This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is why certain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of how to analyze a specific data type once it has been selected. You will: Be aware of the principles of creating and collecting data Know the basic data types and representations Select data types, anticipating analysis goals Understand dataset structures and practices for analyzing and sharing Be guided by examples and use cases (good and bad) Use cleaning tools and methods to create good data.
000945193 650_0 $$aBig data.
000945193 77608 $$iPrint version: $$z9781484261040
000945193 852__ $$bebk
000945193 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-1-4842-6103-3$$zOnline Access$$91397441.1
000945193 909CO $$ooai:library.usi.edu:945193$$pGLOBAL_SET
000945193 980__ $$aEBOOK
000945193 980__ $$aBIB
000945193 982__ $$aEbook
000945193 983__ $$aOnline
000945193 994__ $$a92$$bISE