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The PMBOK® Guide – Seventh Edition provides globally recognized standards and principles for project management, focusing on outcomes, value delivery, and adaptive project practices.
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Ace the Data Science Interview presents a curated set of real-world interview questions commonly used in data science hiring processes, covering statistics, probability, SQL, machine learning, product analytics, and business case scenarios.
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ACSM’s Guidelines for Exercise Testing and Prescription, Eleventh Edition provides evidence-based recommendations for exercise testing, risk stratification, and the development of safe and effective exercise programs across diverse populations.
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The AWS Certified Cloud Practitioner Study Guide is designed to help learners understand core cloud computing concepts and foundational Amazon Web Services (AWS) knowledge. This guide supports preparation for the Foundational (CLF-C01) exam by explaining AWS services, cloud architecture principles, security basics, pricing models, and shared responsibility concepts in a clear and structured manner.
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The Clinician’s Thesaurus® (8th Edition) is a practical reference designed to support mental health professionals in conducting clinical interviews and writing clear, accurate psychological reports using precise professional language.
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The Concise Guide to APA Style® (7th Edition) is an official APA publication designed to help students learn and apply APA Style quickly and accurately for academic writing, formatting, citations, and references.
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Cracking the PM Interview explains common product management interview formats and provides structured guidance on preparing for product design, strategy, analytics, and behavioral questions used in technology hiring processes.
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Deep Learning provides a comprehensive introduction to the theory, algorithms, and practical applications of deep learning, covering both foundational concepts and modern neural network architectures.
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Deep Learning with Python, Second Edition introduces deep learning concepts and practical techniques using the Python programming language and modern neural network libraries.
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Designing Data-Intensive Applications explains the fundamental principles behind building reliable, scalable, and maintainable software systems that manage large volumes of data. The book focuses on core data system concepts rather than specific tools or frameworks.
