Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion is an introduction to the Python programming language for library and information professionals with little or no prior experience. As opposed to the many Python books available today that focus on the language only from a general sense, this book is designed specifically for information professionals who are seeking to advance their career prospects or challenge themselves in new ways by acquiring skills within the rapidly expanding field of data science.
Readers of Python for Information Professionals will learn to:
Develop Python applications for the retrieval, cleaning, and analysis of large datasets.
Design applications to support traditional library functions and create new opportunities to maximize library value.
Consider data security and privacy relevant to data analysis when using the Python language.
Brady Lund, Ph.D., is an assistant professor of information science at the University of North Texas. He has published four books related to technology in libraries and educational institutions – including Casting Light on the Dark Web and Creating Accessible Online Instruction Using Universal Design Principles, both for Rowman and Littlefield Publishing – and nearly 100 articles, editorials, and opinion papers. His work often combines data analytics principles with library and information science research topics.
Daniel Agbaji is a Ph.D. student in information science at the University of North Texas, with a major in Data Science-Artificial Intelligence and Machine Learning. As an experienced researcher and software developer, he has written scholarly publications and book chapters with notable publishers. Daniel has published articles in the information science and library field. As a software developer, Daniel has written thousands of lines of code for fortune 500 companies which are not publicly available due to company policies.
Kossi Dodzi Bissadu is a Ph.D. student in the computer science at the University of North Texas. He currently works as a software engineer at Zenner USA where he leads various products, software, applications, and systems development projects. He is also a US Air Force veteran, very talented and dedicated professional who has more than ten-year professional record achievements, and demonstrated success leading, managing, and working in Technology and Sciences. Kossi has several industry certifications including certified blockchain developer, AWS certified cloud practitioner, and CompTIA Security+.
Haihua Chen, Ph.D., is an assistant professor of information science at the University of North Texas. He has more than ten years of experience in Python and five years of experience in teaching technical courses for information science and data science students using Python. Dr. Chen has published nearly 40 articles on natural language processing, machine learning, data quality, information retrieval, digital libraries, and applied data science. He is the editor of The Electronic Library and the leading guest editor of Frontiers in Big Data and Information Discovery & Delivery special issues. He is also serving as the reviewer/ PC member for more than 20 peer-review journals/ conferences in information science and computer science.
Preface
Part I: Python: The Basics
Chapter 1 – The Python Workspace
Chapter 2 – Object Oriented Programming
Chapter 3 – Data Types, Structures, Sets and Algorithms
Chapter 4 – Functions: Code that Puts Our Data to Work
Chapter 5 – Importing, Creating, and Maintaining Data Files
Chapter 6 – Testing and Troubleshooting
Part II: Further Applications of Python in Information Organizations
Chapter 7 – Library Management and Usage Data
Chapter 8 – Library Research Data Management
Chapter 9 – Text Analysis
Chapter 10 – Library and Information Science Research
Chapter 11 – Artificial Intelligence Applications
Part III: Practical and Ethical Considerations for Using Python
Chapter 12 – Data Explosion, Big Data, and Data Literacy
Chapter 13 – Data Ethics
Chapter 14 – Knowledge and Data Economy
Chapter 15 – Further Resources for Advancing Your Python Mastery
