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Practical Data Science for Information Professionals

Contents Figures Tables Boxes Preface 1 What is data science? Data, information, knowledge, wisdom Data everywhere The data deserts Data science The potential of data science From research data services to data science in libraries Programming in libraries Programming in this book The structure of this book 2 Little data, big data Big data Data formats Standalone files Application programming interfaces Unstructured data Data sources Data licences 3 The process of data science Modelling the data science process Frame the problem Collect data Transform and clean data Analyse data Visualise and communicate data Frame a new problem 4 Tools for data analysis Finding tools Software for data science Programming for data science 5 Clustering and social network analysis Network graphs Graph terminology Network matrix Visualisation Network analysis 6 Predictions and forecasts Predictions and forecasts beyond data science Predictions in a world of (limited) data Predicting and forecasting for information professionals Statistical methodologies 7 Text analysis and mining Text analysis and mining, and information professionals Natural language processing Keywords and n-grams 8 The future of data science and information professionals Eight challenges to data science Ten steps to data science librarianship The final word: play References Appendix – Programming concepts for data science Variables, data types and other classes Import libraries Functions and methods Loops and conditionals Final words of advice Further reading

ใส่ตะกร้า
  • ISBN9781783303441
  • ประเภท E-Book
  • ผู้แต่ง David Stuart
  • สำนักพิมพ์ Facet Pub
  • ครั้งที่พิมพ์ 1
  • ปีที่พิมพ์2020
  • ภาษาภาษาอังกฤษ
  • หมวดหมู่เทคโนโลยีสารสนเทศ / บรรณารักษ์
: ข้อมูลหนังสือ

Contents Figures Tables Boxes Preface 1 What is data science? Data, information, knowledge, wisdom Data everywhere The data deserts Data science The potential of data science From research data services to data science in libraries Programming in libraries Programming in this book The structure of this book 2 Little data, big data Big data Data formats Standalone files Application programming interfaces Unstructured data Data sources Data licences 3 The process of data science Modelling the data science process Frame the problem Collect data Transform and clean data Analyse data Visualise and communicate data Frame a new problem 4 Tools for data analysis Finding tools Software for data science Programming for data science 5 Clustering and social network analysis Network graphs Graph terminology Network matrix Visualisation Network analysis 6 Predictions and forecasts Predictions and forecasts beyond data science Predictions in a world of (limited) data Predicting and forecasting for information professionals Statistical methodologies 7 Text analysis and mining Text analysis and mining, and information professionals Natural language processing Keywords and n-grams 8 The future of data science and information professionals Eight challenges to data science Ten steps to data science librarianship The final word: play References Appendix – Programming concepts for data science Variables, data types and other classes Import libraries Functions and methods Loops and conditionals Final words of advice Further reading