cracking the coding interview 6th edition 189 programming questions and solutions

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Cracking The Coding Interview

Author : Gayle Laakmann McDowell
ISBN : 0984782850
Genre : Business & Economics
File Size : 28. 96 MB
Format : PDF, ePub
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Now in the 6th edition, the book gives you the interview preparation you need to get the top software developer jobs. This is a deeply technical book and focuses on the software engineering skills to ace your interview. The book includes 189 programming interview questions and answers, as well as other advice.

Cracking The Coding Interview 6th Edition

Author : Gayle Laakmann McDowell
ISBN : 0984782869
Genre :
File Size : 76. 2 MB
Format : PDF, Kindle
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Managing The Drug Discovery Process

Author : Walter Moos
ISBN : 9780081006320
Genre : Medical
File Size : 80. 40 MB
Format : PDF, Docs
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Managing the Drug Discovery Process: How to Make It More Efficient and Cost-Effective thoroughly examines the current state of pharmaceutical research and development by providing chemistry-based perspectives on biomedical research, drug hunting and innovation. The book also considers the interplay of stakeholders, consumers, and the drug firm with attendant factors, including those that are technical, legal, economic, demographic, political, social, ecological, and infrastructural. Since drug research can be a high-risk, high-payoff industry, it is important to researchers to effectively and strategically manage the drug discovery process. This book takes a closer look at increasing pre-approval costs for new drugs and examines not only why these increases occur, but also how they can be overcome to ensure a robust pharmacoeconomic future. Written in an engaging manner and including memorable insights, this book is aimed at redirecting the drug discovery process to make it more efficient and cost-effective in order to achieve the goal of saving countless more lives through science. A valuable and compelling resource, this is a must-read for all students and researchers in academia and the pharmaceutical industry. Considers drug discovery in multiple R&D venues, including big pharma, large biotech, start-up ventures, academia, and nonprofit research institutes Analyzes the organization of pharmaceutical R&D, taking into account human resources considerations like recruitment and configuration, management of discovery and development processes, and the coordination of internal research within, and beyond, the organization, including outsourced work Presents a consistent, well-connected, and logical dialogue that readers will find both comprehensive and approachable

Cool Careers Without College For People Who Love Coding

Author : Asher Powell
ISBN : 9781508175360
Genre : Juvenile Nonfiction
File Size : 78. 98 MB
Format : PDF, ePub, Mobi
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This comprehensive career guide helps readers take a close look at coding as a career path and gives them a long, actionable list for turning their interests into a career across a variety of fields. Whether a student is interested in games, engineering, design, or systems administration, each career path comes with a detailed list of resources and first-person accounts from professionals in the field. This guide is all a coding enthusiast needs to get started planning and building a career, all without having to worry about student loans.

The Data Science Handbook

Author : Field Cady
ISBN : 9781119092940
Genre : Mathematics
File Size : 46. 4 MB
Format : PDF
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A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.

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