Data Wrangling: Concepts, Applications and Tools
- 5h 9m
- Geetika Dhand, Kavita Sheoran, M. Niranjanamurthy, Prabhjot Kaur
- John Wiley & Sons (US)
Written and edited by some of the world’s top experts in the field, this exciting new volume provides state-of-the-art research and latest technological breakthroughs in data wrangling, its theoretical concepts, practical applications, and tools for solving everyday problems.
Data wrangling is the process of cleaning and unifying messy and complex data sets for easy access and analysis. This process typically includes manually converting and mapping data from one raw form into another format to allow for more convenient consumption and organization of the data. Data wrangling is increasingly ubiquitous at today’s top firms.
Data cleaning focuses on removing inaccurate data from your data set whereas data wrangling focuses on transforming the data’s format, typically by converting “raw” data into another format more suitable for use. Data wrangling is a necessary component of any business. Data wrangling solutions are specifically designed and architected to handle diverse, complex data at any scale, including many applications, such as Datameer, Infogix, Paxata, Talend, Tamr, TMMData, and Trifacta.
This book synthesizes the processes of data wrangling into a comprehensive overview, with a strong focus on recent and rapidly evolving agile analytic processes in data-driven enterprises, for businesses and other enterprises to use to find solutions for their everyday problems and practical applications. Whether for the veteran engineer, scientist, or other industry professional, this book is a must have for any library.
About the Author
M. Niranjanamurthy, PhD, is an assistant professor in the Department of Computer Applications, M S Ramaiah Institute of Technology, Bangalore, Karnataka. He earned his PhD in computer science at JJTU, Rajasthan, India. He has over 11 years of teaching experience and two years of industry experience as a software engineer. He has published several books, and he is working on numerous books for Scrivener Publishing. He has published over 60 papers for scholarly journals and conferences, and he is working as a reviewer in 22 scientific journals. He also has numerous awards to his credit.
Kavita Sheoran, PhD, is an associate professor in the Computer Science Department, MSIT, Delhi, and she earned her PhD in computer science from Gautam Buddha University, Greater Noida. With over 17 years of teaching experience, she has published various papers in reputed journals and has published two books.
Geetika Dhand, PhD, is an associate professor in the Department of Computer Science and Engineering at Maharaja Surajmal Institute of Technology. After earning her PhD in computer science from Manav Rachna International Institute of Research and Studies, Faridabad, she has taught for over 17 years. She has published one book and a number of papers in technical journals.
Prabhjot Kaur has over 19 years of teaching experience and has earned two PhDs for her work in two different research areas. She has authored two books and more than 40 research papers in reputed journals and conferences. She also has one patent to her credit.
In this Book
Basic Principles of Data Wrangling
Skills and Responsibilities of Data Wrangler
Data Wrangling Dynamics
Essentials of Data Wrangling
Data Leakage and Data Wrangling in Machine Learning for Medical Treatment
Importance of Data Wrangling in Industry 4.0
Managing Data Structure in R
Dimension Reduction Techniques in Distributional Semantics: An Application Specific Review
Big Data Analytics in Real Time for Enterprise Applications to Produce Useful Intelligence
Generative Adversarial Networks: A Comprehensive Review
Analysis of Machine Learning Frameworks Used in Image Processing: A Review
Use and Application of Artificial Intelligence in Accounting and Finance: Benefits and Challenges
Obstacle Avoidance Simulation and Real-Time Lane Detection for AI-Based Self-Driving Car
Impact of Suppliers Network on SCM of Indian Auto Industry: A Case of Maruti Suzuki India Limited