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

Index

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• Análise de dados : geral
• Captura e Análise de Dados
• Gestão de bibliotecas, arquivos e informação
• Mineração de Dados

business analytics;big data;data mining;data librarian