Silicon Valley Python Engineer Interview Guide: Data Structure, Algorithm, and System Design - Softcover

Über die Autorin bzw. den Autor

Dr. Jianfeng Ren serves as a Software Manager for Google’s Pixel Camera team and holds a Ph.D. in Electrical Engineering. With deep expertise in computer vision, machine learning, and computational photography, he has built an impressive career, including notable contributions during his tenure at Qualcomm and Huawei USA. Dr. Ren is the author of several acclaimed books, such as Autonomous Driving Algorithms and Chip Design, and is a participant in MIT’s prestigious CTO program. Blending academic rigor with industry leadership, he spearheads ground breaking AI solutions in autonomous driving and smart technologies, driven by a passion for innovation and excellence in technology and management.

Andric Li is a graduate of the University of California, San Diego, with a strong foundation in computer science and a focus on Human-Computer Interaction. His expertise spans software engineering, computer graphics, machine learning, artificial intelligence, and extended reality (XR) technologies. Andric has worked on immersive projects combining virtual and augmented reality with web-based applications, contributing to innovative solutions in digital interaction and intelligent systems. Passionate about merging creativity with advanced technology, Andric aims to leverage his skills to create impactful and efficient systems that bridge the gap between physical and digital environments.

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Silicon Valley Python Interview Guide: Data Structures, Algorithms, and System Design is an essential resource for aspiring software engineers preparing for technical interviews at top-tier companies. This book provides a comprehensive roadmap, covering foundational concepts, practical coding techniques, and advanced problem-solving strategies to help candidates excel in interviews. With a focus on Python, the book equips readers with the skills to tackle challenging coding problems, design scalable systems, and communicate solutions effectively. In the first half, the book delves into core data structures (lists, stacks, queues, graphs, and trees) and algorithms (binary search, dynamic programming, DFS, BFS, and backtracking), offering practical examples and Python implementations. The latter half transitions to system design, including big data architectures, distributed systems, and machine learning workflows. Case studies on real-world applications like Tiny URL, autocomplete systems, and Chat GPT-like models provide hands-on insights. Whether you are an early-career engineer or an experienced professional, this guide is designed to enhance your preparation with real-world examples, tested code, and proven strategies. It is more than a technical handbook--it is your roadmap to building confidence and securing a role in the competitive tech industry.