Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way.
Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. R. Lakshmana Kumar is an Assistant professor in the Computer Applications Department and currently also leading the technical training team in Hindusthan College of Engineering and Technology, Coimbatore. Tamil Nadu. His PhD is from Anna University, Chennai and his Research is on Semantic Web Services. Part of his PhD work was funded by South Korea. He is a global chapter lead for MLCS [Machine Learning for Cyber Security] for the Coimbatore chapter. He is currently allied with company-specific training of Infosys Campus Connect, Oracle WDP and Palo Alto Networks. He has a passion for software development and holds an international certification on SCJP (Sun Certificated Java Programmer) and SCJWCD (Sun Certificate Java Web Component Developer). He is familiar with programming languages like Java, Python, and PHP. He is involved with research and considered an expertise in distributed computing. He also holds the Data Science certification from John Hopkins University and the Amazon Cloud Architect certification from Amazon Web Services. He has published more than 25 papers in various international journals. Dr. R. Indrakumari is an Assistant Professor as the School of Computing Science and Engineering, Galgotias University, NCR Delhi, India. She has completed the M.Tech in Computer and Information Technology from Manonmaniam Sundaranar University, Tirunelveli. Her main areas of interest are Big Data, Internet of Things, Data Mining, Data warehousing and its visualization tools such as Tableau, Qlikview. Dr. B. Balamurugan Completed his PhD. at Vellore Institute of Technology University, Vellore and is currently working as a Professor at Galgotias University, Greater Noida, Uttar Pradesh. He has 15 years of teaching experience in the field of computer science. His area of interest lies in the field of Internet of Things, Big data, Networking. He has published more than 100 international journals papers and contributed book chapters. Dr. Achyut Shankar completed his PhD at Vellore Institute of Technology University, Tamilnadu, India and is currently working as an Assistant Professor at Amity School of Engineering and Technology, India. His areas of interested are Data Communication, Computer Networks, Machine Learning, Statistical Tools, Operating Systems, Pattern Recognition, and Theory of Computation.
Inhaltsangabe
Chapter 1. Visual Analytics: Scopes & Challenges. Chapter 2. Statistical Methods and Applications: A Comprehensive Reference for the Healthcare Industry. Chapter 3. Machine Learning Algorithms for Healthcare Data Analytics. Chapter 4. A Review of Challenges and Opportunities in Machine Learning for Healthcare. Chapter 5. Digitalizing the Health Records Using Machine Learning Algorithms. Chapter 6. Interactive Visualization for Understanding and Analyzing Medical Data. Chapter 7. Heart Disease Prediction Using Tableau. Chapter 8. A Deep Learning Framework Using AlexNet for Early Detection of Pancreatic Cancer. Chapter 9. Applications of the Map-Reduce Programming Framework to Clinical Big Data Analysis: Current Landscape and Future Trends. Chapter 10. An Investigation of Different Machine Learning Approaches for Healthcare Analytics. Chapter 11. The Potential of Machine Learning for Clinical Predictive Analytics. Chapter 12. Predictive Analytics in Healthcare Using Machine Learning Tools and Techniques. Chapter 13. A Collective Study of Machine Learning (ML) Algorithms and Its Impact on Various Facets of Healthcare.
Chapter 1. Visual Analytics: Scopes & Challenges. Chapter 2. Statistical Methods and Applications: A Comprehensive Reference for the Healthcare Industry. Chapter 3. Machine Learning Algorithms for Healthcare Data Analytics. Chapter 4. A Review of Challenges and Opportunities in Machine Learning for Healthcare. Chapter 5. Digitalizing the Health Records Using Machine Learning Algorithms. Chapter 6. Interactive Visualization for Understanding and Analyzing Medical Data. Chapter 7. Heart Disease Prediction Using Tableau. Chapter 8. A Deep Learning Framework Using AlexNet for Early Detection of Pancreatic Cancer. Chapter 9. Applications of the Map-Reduce Programming Framework to Clinical Big Data Analysis: Current Landscape and Future Trends. Chapter 10. An Investigation of Different Machine Learning Approaches for Healthcare Analytics. Chapter 11. The Potential of Machine Learning for Clinical Predictive Analytics. Chapter 12. Predictive Analytics in Healthcare Using Machine Learning Tools and Techniques. Chapter 13. A Collective Study of Machine Learning (ML) Algorithms and Its Impact on Various Facets of Healthcare.
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