Practical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, providing readers with an overview of data science and a framework for its application. It provides detailed examples and analysis on real data sets to explore the basics of the subject in three principle areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining. As well as highlighting a wealth of user-friendly data science tools, the book also includes some example code in two of the most popular programming languages (R and Python) to demonstrate the ease with which the information professional can move beyond the graphical user interface and achieve significant analysis with just a few lines of code. After reading, readers will understand: · the growing importance of data science · the role of the information professional in data science · some of the most important tools and methods that information professionals can use. Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals will provide a practical introduction to the topic specifically designed for the information community. It will appeal to librarians and information professionals all around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to use the lessons learned within.
A little research tells us the common correlation is called a Pearson coefficient, and the significance test for a Pearson coefficient for normally distributed data is a Student's t-test (with the number of degrees of freedom equal to ...
The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset.Ê WHAT WILL YOU LEARNÊÊ - Understand the multi-disciplinary nature of Data Science - Get familiar with ...
What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge ...
This book will be invaluable to library and information professionals interested in building or expanding data science services.
Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and ...
This text serves as a tool for library and information science students and educators working on data science curriculum design.
What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge ...
Data Management will guide readers through: Understanding data management basics and best practices. Using the reference interview to help with data management Writing data management plans for grants.
This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond.
This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing.