Data Science And Big Data Analytics Process And Practices
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Data Science and Big Data Analytics- Process and practices
Author: Arun Kumar Mittapelly
language: en
Publisher: Academic Guru Publishing House
Release Date: 2025-03-21
“Data Science and Big Data Analytics” is a definitive resource for learning about data science techniques, methodologies, and the technologies that are shaping the future of data analysis. This book covers a broad spectrum of topics, from the fundamentals of data collection and preprocessing to advanced techniques in machine learning and predictive analytics. Designed with both beginners and seasoned professionals in mind, the book takes a structured approach, starting with essential concepts before progressing to more intricate topics like big data technologies (Hadoop, Spark), real-time analytics, and predictive modeling. Detailed explanations and practical examples ensure that readers can easily understand and apply the techniques discussed. Each chapter emphasises hands-on learning and provides practical insights that can be used in everyday business and technical applications. This book is particularly suited for individuals who are preparing to enter the data science field or those already working in industries like healthcare, finance, marketing, and supply chain management. It also addresses key challenges such as data privacy and ethical concerns in big data analytics, ensuring readers are well-prepared to navigate this complex and dynamic domain.
Data Science and Big Data Analytics- Principles, Techniques, and Applications
Author: Dr. Dhananjaya Reddy
language: en
Publisher: Academic Guru Publishing House
Release Date: 2024-10-25
“Data Science and Big Data Analytics: Principles, Techniques, and Applications” is designed to provide a comprehensive introduction to the rapidly growing fields of data science and big data analytics. The book offers a balanced approach to both theoretical concepts and practical knowledge, making it ideal for learners at various levels—ranging from beginners to those seeking advanced understanding. By exploring topics such as data collection, preprocessing, machine learning, and big data technologies, the book lays the groundwork for understanding how large-scale data is managed and utilized in real-world applications. Without including case studies or examples, the text emphasizes key concepts, tools, and methodologies. The book also addresses ethical considerations and future trends in data science, making it a well-rounded resource for students, educators, and professionals aiming to grasp the essentials of the field.
Big Data Analytics Methods
Author: Peter Ghavami
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2019-12-16
Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.