메세지가 없습니다
상세정보
- 검색상세정보
Dark data : why what you don't know matters
Hand, D. J.
- 책이미지
- 서평쓰기
- 서평쓰기
- 책이미지
-
QRcode
- QRcode 사용방법
-
QRcode 사용방법
- QR코드 정의
- 사각형의 가로세로 격자무늬에 다양한 정보를 담고 있는 2차원(매트릭스)형식의 코드로 스마트폰으로 QR코드를 스캔하면 각종 정보를 제공 받을 수 있습니다.
- QR코드 인식프로그램
- 스마트폰 마켓에 들어가면 'QR코드 스캔 프로그램'을 검색해 무료 어플을 다운받아 사용하시면 됩니다.
- 도서정보 QR코드 이용하기
- 도서 상세정보에서 QR코드를 스캔하면, 모바일 웹페이지로 연결되어 해당 도서의 상세정보 및 소장정보 등을 확인 할 수 있습니다.
| 자료유형 | E-BOOK |
|---|---|
| 서명/저자사항 | Dark data : why what you don't know matters/ David J. Hand. |
| 개인저자 | Hand, D. J.(David J.), 1950- author. |
| 발행사항 | Princeton: Princeton University Press, [2020]. |
| 형태사항 | 1 online resource. |
| 기타형태 저록 | Print version: Hand, D. J. (David J.), 1950- Dark data Princeton : Princeton University Press, [2020] 9780691182377 |
| ISBN | 0691198853 9780691198859 |
| 서지주기 | Includes bibliographical references and index. |
| 내용주기 | Cover; Contents; Preface; Part 1: Dark Data: Their Origins and Consequences; Chapter 1: Dark Data: What We Don't See Shapes Our World; The Ghost of Data; So You Think You Have All the Data?; Nothing Happened, So We Ignored It; The Power of Dark Data; All around Us; Chapter 2: Discovering Dark Data: What We Collect and What We Don't; Dark Data on All Sides; Data Exhaust, Selection, and Self-Selection; From the Few to the Many; Experimental Data; Beware Human Frailties; Chapter 3: Definitions and Dark Data: What Do You Want to Know?; Different Definitions and Measuring the Wrong Thing You Can't Measure EverythingScreening; Selection on the Basis of Past Performance; Chapter 4: Unintentional Dark Data: Saying One Thing, Doing Another; The Big Picture; Summarizing; Human Error; Instrument Limitations; Linking Data Sets; Chapter 5: Strategic Dark Data: Gaming, Feedback, and Information Asymmetry; Gaming; Feedback; Information Asymmetry; Adverse Selection and Algorithms; Chapter 6: Intentional Dark Data: Fraud and Deception; Fraud; Identity Theft and Internet Fraud; Personal Financial Fraud; Financial Market Fraud and Insider Trading; Insurance Fraud; And More Chapter 7: Science and Dark Data: The Nature of DiscoveryThe Nature of Science; If Only I'd Known That; Tripping over Dark Data; Dark Data and the Big Picture; Hiding the Facts; Retraction; Provenance and Trustworthiness: Who Told You That?; Part II: Illuminating and Using Dark Data; Chapter 8: Dealing with Dark Data: Shining a Light; Hope!; Linking Observed and Missing Data; Identifying the Missing Data Mechanism; Working with the Data We Have; Going Beyond the Data: What If You Die First?; Going Beyond the Data: Imputation; Iteration; Wrong Number! Chapter 9: Benefiting from Dark Data: Reframing the QuestionHiding Data; Hiding Data from Ourselves: Randomized Controlled Trials; What Might Have Been; Replicated Data; Imaginary Data: The Bayesian Prior; Privacy and Confidentiality Preservation; Collecting Data in the Dark; Chapter 10: Classifying Dark Data: A Route through the Maze; A Taxonomy of Dark Data; Illumination; Notes; Index |
| 요약 | "Data describe and represent the world. However, no matter how big they may be, data sets don't - indeed cannot - capture everything. Data are measurements - and, as such, they represent only what has been measured. They don't necessarily capture all the information that is relevant to the questions we may want to ask. If we do not take into account what may be missing/unknown in the data we have, we may find ourselves unwittingly asking questions that our data cannot actually address, come to mistaken conclusions, and make disastrous decisions. In this book, David Hand looks at the ubiquitous phenomenon of "missing data." He calls this "dark data" (making a comparison to "dark matter" - i.e., matter in the universe that we know is there, but which is invisible to direct measurement). He reveals how we can detect when data is missing, the types of settings in which missing data are likely to be found, and what to do about it. It can arise for many reasons, which themselves may not be obvious - for example, asymmetric information in wars; time delays in financial trading; dropouts in clinical trials; deliberate selection to enhance apparent performance in hospitals, policing, and schools; etc. What becomes clear is that measuring and collecting more and more data (big data) will not necessarily lead us to better understanding or to better decisions. We need to be vigilant to what is missing or unknown in our data, so that we can try to control for it. How do we do that? We can be alert to the causes of dark data, design better data-collection strategies that sidestep some of these causes - and, we can ask better questions of our data, which will lead us to deeper insights and better decisions"-- |
| 해제 | Provided by publisher. |
| 일반주제명 | Missing observations (Statistics) Big data. Big data. Missing observations (Statistics) COMPUTERS / Database Management / Data Mining |
| 언어 | 영어 |
| 바로가기 | URL |
소장정보
청구기호 Browsing
이 분야 인기자료
-
- 면역학의 기본
- 마쓰모토 켄지
- BM성안당, 2022
-
- (그림으로 쉽게 공부하는)핵심해부학 컬러링북
- 메디컬아카데미아. 해부학연구회
- 아카데미아, 2023
-
- 포토샵+일러스트레이터 CC 2024 무작정 따라하기
- 민지영
- 길벗, 2024
-
- (Do it!)스케치업 with 엔스케이프 : 8가지 ...
- 최주영
- 이지스, 2024
-
- 언어재활 현장실무 = Clinical practices...
- 김성태
- 학지사, 2019
태그가 저장되었습니다. 
태그저장 중 오류가 발생하였습니다. 재시도 하십시오. 
개인정보처리방침
주소 : 부산광역시 남구 신선로 428 동명대학교 중앙도서관전화번호 : 051-629-3101~8
COPYRIGHT ⓒ 2016 TONGMYONG UNIVERSITY LIBRARY. ALL RIGHTS RESERVED.
서평 (0 건)
*주제와 무관한 내용의 서평은 삭제될 수 있습니다.
서평추가