The Data Science Design Manual Texts in Computer Science

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Springer #ad - It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course.

Additional learning tools: contains “war stories, ” offering perspectives on how data science applies in the real worldIncludes “Homework Problems, ” providing a wide range of exercises and projects for self-studyProvides a complete set of lecture slides and online video lectures at www. Data-manual.

The Data Science Design Manual Texts in Computer Science #ad - Comprovides “take-home lessons, ” emphasizing the big-picture concepts to learn from each chapterRecommends exciting “Kaggle Challenges” from the online platform KaggleHighlights “False Starts, ” revealing the subtle reasons why certain approaches failOffers examples taken from the data science television show “The Quant Shop” www.

Quant-shop. Com. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles.

Practitioners in these and related fields will find this book perfect for self-study as well. This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science.

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The Algorithm Design Manual

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Springer #ad - Self-motivating Exam Design. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis.

The first part,  techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. Links to Programming Challenge Problems. The second part, is intended for browsing and reference, and comprises the catalog of algorithmic resources,  Resources, implementations and an extensive bibliography.

The Algorithm Design Manual #ad - New to the second edition:• doubles the tutorial material and exercises over the first edition• provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video• Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them• Includes several NEW "war stories" relating experiences from real-world applications• Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java More and Improved Homework Problems.

This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficacy and efficiency. More code, Less Pseudo-code. Take-Home Lessons.

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The Algorithm Design Manual

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Springer #ad - The second part, Resources, implementations and an extensive bibliography. Take-Home Lessons. More code, Less Pseudo-code. The first part, provides accessible instruction on methods for designing and analyzing computer algorithms. More and Improved Homework Problems. Links to Programming Challenge Problems. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, and students.

The reader-friendly algorithm design manual provides straightforward access to combinatorial algorithms technology, is intended for browsing and reference, and comprises the catalog of algorithmic resources, Techniques, stressing design over analysis. This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, researchers, and analyzing their efficacy and efficiency.

The Algorithm Design Manual #ad - Self-motivating Exam Design. Expanding on the highly successful formula of the first edition, this book now serves as the primary textbook of choice for any algorithm design course while maintaining its status as the premier practical reference guide to algorithms.

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Practical Statistics for Data Scientists: 50 Essential Concepts

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O'Reilly Media #ad - More code, Less Pseudo-code. If you’re familiar with the r programming language, this quick reference bridges the gap in an accessible, and have some exposure to statistics, readable format. With this book, you’ll learn:why exploratory data analysis is a key preliminary step in data sciencehow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that “learn” from dataUnsupervised learning methods for extracting meaning from unlabeled data More and Improved Homework Problems.

This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. Self-motivating Exam Design.

Practical Statistics for Data Scientists: 50 Essential Concepts #ad - Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Links to Programming Challenge Problems. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second part, Resources, implementations and an extensive bibliography.

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Algorithms Illuminated Part 3: Greedy Algorithms and Dynamic Programming

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Soundlikeyourself Publishing, LLC #ad - Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, and students. The reader-friendly algorithm design manual provides straightforward access to combinatorial algorithms technology, is intended for browsing and reference, and comprises the catalog of algorithmic resources, Techniques, stressing design over analysis.

Take-Home Lessons. Part 3 covers greedy algorithms scheduling, shortest paths, minimum spanning trees, sequence alignment, Huffman codes and dynamic programming knapsack, clustering, optimal search trees. More code, Less Pseudo-code. This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, researchers, and analyzing their efficacy and efficiency.

Algorithms Illuminated Part 3: Greedy Algorithms and Dynamic Programming #ad - Links to Programming Challenge Problems. Accessible, no-nonsense, and programming language-agnostic introduction to algorithms. Self-motivating Exam Design. The second part, Resources, implementations and an extensive bibliography. The first part, provides accessible instruction on methods for designing and analyzing computer algorithms.

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Daily Coding Problem: Get exceptionally good at coding interviews by solving one problem every day

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Independently published #ad - More and Improved Homework Problems. Each one is based on a real question that was asked recently by top tech companies. It has become increasingly common for candidates to be asked to formulate novel data structures that deal with time and space constraints, or to design a high-level system that meets a particular need.

From deriving a perfect blackjack strategy to deciphering an alien dictionary, these questions are designed to challenge you and widen your understanding of what can be achieved with the right concepts and implementation. Lastly, we address the topic of design. You'll learn about:arraysstringslinked liststreeshash tablesbinary Search TreesTriesHeapsStacks and QueuesGraphsRandomized AlgorithmsDynamic ProgrammingBacktrackingBit ManipulationPathfindingRecursionData Structure DesignSystem DesignThe questions in this book have been chosen with practicality, clarity, and self-improvement in mind.

Daily Coding Problem: Get exceptionally good at coding interviews by solving one problem every day #ad - The first part, provides accessible instruction on methods for designing and analyzing computer algorithms. For each data structure, we offer a refresher on its advantages and disadvantages, its implementation, the time and space complexities of its operations, and what themes and key words to look for in order to recognize it.

Next, including dynamic programming, backtracking, we take a tour through a series of must-know algorithms, sorting, and searching. We examine patterns one can identify to figure out which algorithm to apply in a given problem, and finally we look at a few specialized algorithms that require combining multiple approaches.

Third, we present a set of more advanced problems that require you to use the preceding data structures and algorithms in novel ways in order to solve real-world applications.

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Linear Algebra and Learning from Data

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Wellesley-Cambridge Press #ad - Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, and students. The reader-friendly algorithm design manual provides straightforward access to combinatorial algorithms technology, is intended for browsing and reference, and comprises the catalog of algorithmic resources, Techniques, stressing design over analysis.

This uses the full array of applied linear algebra, including randomization for very large matrices. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Self-motivating Exam Design. The second part, Resources, implementations and an extensive bibliography. More and Improved Homework Problems.

Linear Algebra and Learning from Data #ad - . Based on the second linear algebra course taught by Professor Strang, whose lectures on the training data are widely known, it starts from scratch the four fundamental subspaces and is fully accessible without the first text. Audience: this book is for anyone who wants to learn how data is reduced and interpreted by and understand matrix methods.

Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. More code, Less Pseudo-code.

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Algorithms Illuminated Part 2: Graph Algorithms and Data Structures Volume 2

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Soundlikeyourself Publishing, LLC #ad - This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, researchers, and analyzing their efficacy and efficiency. Part 1 is not a prerequisite. More and Improved Homework Problems. Accessible, no-nonsense, and programming language-agnostic introduction to algorithms.

Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, and students. The reader-friendly algorithm design manual provides straightforward access to combinatorial algorithms technology, Techniques, and comprises the catalog of algorithmic resources, is intended for browsing and reference, stressing design over analysis.

Algorithms Illuminated Part 2: Graph Algorithms and Data Structures Volume 2 #ad - More code, Less Pseudo-code. Includes solutions to all quizzes and selected problems, and a series of YouTube videos by the author accompanies the book. Links to Programming Challenge Problems. The second part, Resources, implementations and an extensive bibliography. Part 2 covers graph search and its applications, hash tables, search trees, shortest-path algorithms, and the applications and implementation of several data structures: heaps, and bloom filters.

Self-motivating Exam Design. Take-Home Lessons.

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Machine Learning: An Applied Mathematics Introduction

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Panda Ohana Publishing #ad - Take-Home Lessons. Self-motivating Exam Design. This book is an accessible introduction for anyone who wants to understand the foundations but also wants to “get to the meat without having to eat too many vegetables. Paul wilmott has been called “cult derivatives lecturer” by the Financial Times and “financial mathematics guru” by the BBC.

More code, Less Pseudo-code. This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, researchers, and analyzing their efficacy and efficiency. More and Improved Homework Problems. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, and students.

Machine Learning: An Applied Mathematics Introduction #ad - The reader-friendly algorithm design manual provides straightforward access to combinatorial algorithms technology, Techniques, and comprises the catalog of algorithmic resources, is intended for browsing and reference, stressing design over analysis. The second part, Resources, implementations and an extensive bibliography.

Links to Programming Challenge Problems. The first part, provides accessible instruction on methods for designing and analyzing computer algorithms. Machine learning: an applied mathematics introduction covers the essential mathematics behind all of the following topics K Nearest Neighbours K Means Clustering Naïve Bayes Classifier Regression Methods Support Vector Machines Self-Organizing Maps Decision Trees Neural Networks Reinforcement LearningPaul Wilmott brings three decades of experience in education, and his inimitable style, to this, the hottest of subjects.

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The Hundred-Page Machine Learning Book

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Andriy Burkov #ad - Self-motivating Exam Design. Warning: to avoid counterfeit, make sure that the book ships from and sold by Amazon. Avoid third-party sellers. Peter norvig, co-author of aima, research director at google, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages.

I would highly recommend “the hundred-page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base. Everything you really need to know in Machine Learning in a hundred pages. This is the first of its kind "read first, buy later" book.

More and Improved Homework Problems. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, not the last, and for the reader who understands that this is the first 100 or actually 150 pages you will read, provides a solid introduction to the field.

The Hundred-Page Machine Learning Book #ad - Aurélien géron, senior ai engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages plus few bonus pages! Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, and students.

The reader-friendly algorithm design manual provides straightforward access to combinatorial algorithms technology, Techniques, and comprises the catalog of algorithmic resources, is intended for browsing and reference, stressing design over analysis.

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Heard In Data Science Interviews: Over 650 Most Commonly Asked Interview Questions & Answers

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CreateSpace Independent Publishing Platform #ad - More code, Less Pseudo-code. A collection of over 650 actual data scientist/Machine Learning Engineer job interview questions along with their full answers, references, and useful tips More and Improved Homework Problems. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, and students.

The reader-friendly algorithm design manual provides straightforward access to combinatorial algorithms technology, and comprises the catalog of algorithmic resources, Techniques, is intended for browsing and reference, stressing design over analysis. Self-motivating Exam Design. Links to Programming Challenge Problems.

Heard In Data Science Interviews: Over 650 Most Commonly Asked Interview Questions & Answers #ad - Take-Home Lessons. The first part, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, implementations and an extensive bibliography. This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, researchers, and analyzing their efficacy and efficiency.

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