Search results for: data-structures-for-everyone

Computer Science for the Curious

Author : Vaidehi Joshi
File Size : 46.48 MB
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An in-depth, illustrated guide to data structures, a core topic in computer science, by the founder of BaseCS (https: //medium.com/basecs), a top 200 Medium blog that explores computer science basics. First in a series. Computer Science for the Curious aims to make Computer Science topics accessible to programmers who have not taken a traditional computer science curriculum, as well as readers looking for a practical refresher. The first volume in a series, this book focuses on ways that a computer scientist might organize or represent data, covering topics like the binary number system, graphs, hash tables, and trees.

The Bible of Algorithms and Data Structures

Author : Florian Dedov
File Size : 31.69 MB
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The Most Important Skill in Computer Science! The field of algorithms and data structures is one of the most important in computer science. You will rarely be invited to a coding interview at Google, Microsoft or Facebook and not be asked questions about it. This is because these companies know how valuable the skills taught are. It doesn't matter if you are into machine learning, ethical hacking, cyber security or enterprise software engineering. You will always need to be able to work with algorithms and data structures. However, this field is also by many considered to be one of the hardest, since it is so abstract and complex. This is mainly due to the style in which it is taught. Most professors in colleges focus on exact mathematical definitions instead of understanding. And while you can't blame them for doing their job, there are better ways to learn about this subject. This book is for everyone who is interested in an intuitive and simple approach to algorithms and data structures. It is for everyone who is frustrated with memorizing dry formal definitions. This bible covers all the formal definitions that are important and necessary but it mainly focuses on breaking complex things down in a simple way. At the end, you will not only know how to formally analyze algorithms but you will also deeply understand what is happening behind the scenes and why things are the way they are. After Reading This Book You Will Have The Following Skills: - Intuitive understanding of algorithms and data structures - Analyzing the runtime complexity of algorithms - Using the Big O notation - Dissecting and analyzing sorting algorithms (Bubble Sort, Merge Sort, Quick Sort...) - Understanding and applying graph theory and related algorithms (BFS, DFS, Kruskal, Dijkstra) - Understanding basic data structures and their time complexities (Linked Lists, Stacks, Heaps, Trees...) - Using self-balancing trees (AVL, B-Tree...) - Understanding and applying hashing and collision resolution Master Algorithms and Data Structure Simply and Intuitively!

A Common Sense Guide to Data Structures and Algorithms Second Edition

Author : Jay Wengrow
File Size : 72.53 MB
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If you thought that data structures and algorithms were all just theory, you're missing out on what they can do for your code. Learn to use Big O Notation to make your code run faster by orders of magnitude. Choose from data structures such as hash tables, trees, and graphs to increase your code's efficiency exponentially. With simple language and clear diagrams, this book makes this complex topic accessible, no matter your background. This new edition features practice exercises in every chapter, and new chapters on topics such as dynamic programming and heaps and tries. Get the hands-on info you need to master data structures and algorithms for your day-to-day work. Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today's web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code, with examples in JavaScript, Python, and Ruby. This new and revised second edition features new chapters on recursion, dynamic programming, and using Big O in your daily work. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You'll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions. Use these techniques today to make your code faster and more scalable.

Python Data Structures and Algorithms Complete Self Assessment Guide

Author : Gerardus Blokdyk
File Size : 68.66 MB
Format : PDF, ePub, Mobi
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Is Python Data Structures and Algorithms Required? How do we go about Comparing Python Data Structures and Algorithms approaches/solutions? Has the Python Data Structures and Algorithms work been fairly and/or equitably divided and delegated among team members who are qualified and capable to perform the work? Has everyone contributed? Do we cover the five essential competencies-Communication, Collaboration, Innovation, Adaptability, and Leadership that improve an organization's ability to leverage the new Python Data Structures and Algorithms in a volatile global economy? Whats the best design framework for Python Data Structures and Algorithms organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant? This powerful Python Data Structures and Algorithms self-assessment will make you the trusted Python Data Structures and Algorithms domain assessor by revealing just what you need to know to be fluent and ready for any Python Data Structures and Algorithms challenge. How do I reduce the effort in the Python Data Structures and Algorithms work to be done to get problems solved? How can I ensure that plans of action include every Python Data Structures and Algorithms task and that every Python Data Structures and Algorithms outcome is in place? How will I save time investigating strategic and tactical options and ensuring Python Data Structures and Algorithms opportunity costs are low? How can I deliver tailored Python Data Structures and Algorithms advise instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Python Data Structures and Algorithms essentials are covered, from every angle: the Python Data Structures and Algorithms self-assessment shows succinctly and clearly that what needs to be clarified to organize the business/project activities and processes so that Python Data Structures and Algorithms outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Python Data Structures and Algorithms practitioners. Their mastery, combined with the uncommon elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Python Data Structures and Algorithms are maximized with professional results. Your purchase includes access details to the Python Data Structures and Algorithms self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.

Probabilistic Data Structures for Blockchain Based Internet of Things Applications

Author : Neeraj Kumar
File Size : 74.20 MB
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This book covers theory and practical knowledge of Probabilistic data structures (PDS) and Blockchain (BC) concepts. It introduces the applicability of PDS in BC to technology practitioners and explains each PDS through code snippets and illustrative examples. Further, it provides references for the applications of PDS to BC along with implementation codes in python language for various PDS so that the readers can gain confidence using hands on experience. Organized into five sections, the book covers IoT technology, fundamental concepts of BC, PDS and algorithms used to estimate membership query, cardinality, similarity and frequency, usage of PDS in BC based IoT and so forth.

97 Things About Ethics Everyone in Data Science Should Know

Author : Bill Franks
File Size : 61.51 MB
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Most of the high-profile cases of real or perceived unethical activity in data science aren’t matters of bad intent. Rather, they occur because the ethics simply aren’t thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices. Articles include: Ethics Is Not a Binary Concept—Tim Wilson How to Approach Ethical Transparency—Rado Kotorov Unbiased ≠ Fair—Doug Hague Rules and Rationality—Christof Wolf Brenner The Truth About AI Bias—Cassie Kozyrkov Cautionary Ethics Tales—Sherrill Hayes Fairness in the Age of Algorithms—Anna Jacobson The Ethical Data Storyteller—Brent Dykes Introducing Ethicize™, the Fully AI-Driven Cloud-Based Ethics Solution!—Brian O’Neill Be Careful with "Decisions of the Heart"—Hugh Watson Understanding Passive Versus Proactive Ethics—Bill Schmarzo

XAFS for Everyone

Author : Scott Calvin
File Size : 71.9 MB
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XAFS for Everyone provides a practical, thorough guide to x-ray absorption fine-structure (XAFS) spectroscopy for both novices and seasoned practitioners from a range of disciplines. The text is enhanced with more than 200 figures as well as cartoon characters who offer informative commentary on the different approaches used in XAFS spectroscopy. The book covers sample preparation, data reduction, tips and tricks for data collection, fingerprinting, linear combination analysis, principal component analysis, and modeling using theoretical standards. It describes both near-edge (XANES) and extended (EXAFS) applications in detail. Examples throughout the text are drawn from diverse areas, including materials science, environmental science, structural biology, catalysis, nanoscience, chemistry, art, and archaeology. In addition, five case studies from the literature demonstrate the use of XAFS principles and analysis in practice. The text includes derivations and sample calculations to foster a deeper comprehension of the results. Whether you are encountering this technique for the first time or looking to hone your craft, this innovative and engaging book gives you insight on implementing XAFS spectroscopy and interpreting XAFS experiments and results. It helps you understand real-world trade-offs and the reasons behind common rules of thumb.

R for Everyone

Author : Jared P. Lander
File Size : 59.13 MB
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Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES • Exploring R, RStudio, and R packages • Using R for math: variable types, vectors, calling functions, and more • Exploiting data structures, including data.frames, matrices, and lists • Creating attractive, intuitive statistical graphics • Writing user-defined functions • Controlling program flow with if, ifelse, and complex checks • Improving program efficiency with group manipulations • Combining and reshaping multiple datasets • Manipulating strings using R’s facilities and regular expressions • Creating normal, binomial, and Poisson probability distributions • Programming basic statistics: mean, standard deviation, and t-tests • Building linear, generalized linear, and nonlinear models • Assessing the quality of models and variable selection • Preventing overfitting, using the Elastic Net and Bayesian methods • Analyzing univariate and multivariate time series data • Grouping data via K-means and hierarchical clustering • Preparing reports, slideshows, and web pages with knitr • Building reusable R packages with devtools and Rcpp • Getting involved with the R global community

A Common Sense Guide to Data Structures and Algorithms Second Edition

Author : Jay Wengrow
File Size : 33.91 MB
Format : PDF, ePub, Mobi
Download : 250
Read : 800
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Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today’s web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code, with examples in JavaScript, Python, and Ruby. This new and revised second edition features new chapters on recursion, dynamic programming, and using Big O in your daily work. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You’ll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions. Use these techniques today to make your code faster and more scalable.

Data Structures and Algorithms Using Java

Author : William McAllister
File Size : 46.52 MB
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Data Structures & Theory of Computation

The Design of Dynamic Data Structures

Author : Mark H. Overmars
File Size : 30.87 MB
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In numerous computer applications there is a need of storing large sets of objects in such a way that some questions about those objects can be answered efficiently. Data structures that store such sets of objects can be either static (built for a fixed set of objects) or dynamic (insertions of new objects and deletions of existing objects can be performed). Especially for more complex searching problems as they arise in such fields as computational geometry, database design and computer graphics, only static data structures are available. This book aims at remedying this lack of flexibility by providing a number of general techniques for turning static data structures for searching problems into dynamic structures. Although the approach is basically theoretical, the techniques offered are often practically applicable. The book is written in such a way that it is readable for those who have some elementary knowledge of data structures and algorithms. Although this monograph was first published in 1983, it is still unique as a general treatment of methods for constructing dynamic data structures.

Pascal Plus Data Structures Algorithms and Advanced Programming

Author : Nell B. Dale
File Size : 45.18 MB
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Data Structures Using Java

Author : Duncan A. Buell
File Size : 48.41 MB
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Data Structures & Theory of Computation

Data Structures

Author : Elliot B. Koffman
File Size : 30.10 MB
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Data Structures: Abstraction and Design Using Java offers a coherent and well-balanced presentation of data structure implementation and data structure applications with a strong emphasis on problem solving and software design. Step-by-step, the authors introduce each new data structure as an abstract data type (ADT), explain its underlying theory and computational complexity, provide its specification in the form of a Java interface, and demonstrate its implementation as one or more Java classes. Case studies using the data structures covered in the chapter show complete and detailed solutions to real-world problems, while a variety of software design tools are discussed to help students “Think, then code.” The book supplements its rigorous coverage of basic data structures and algorithms with chapters on sets and maps, balanced binary search trees, graphs, event-oriented programming, testing and debugging, and other key topics. Now available as an enhanced e-book, the fourth edition of Data Structures: Abstraction and Design Using Java enables students to measure their progress after completing each section through interactive questions, quick-check questions, and review questions.

Programming for Everyone

Author : Keith Lee
File Size : 32.83 MB
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Java For Everyone

Author : Cay S. Horstmann
File Size : 24.33 MB
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This book gives an introduction to Java and computer programming that focuses on the essentials and on effective learning.

Objects Abstraction Data Structures and Design

Author : Elliot B. Koffman
File Size : 43.22 MB
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"It is a practical book with emphasis on real problems the programmers encounter daily." --Dr.Tim H. Lin, California State Polytechnic University, Pomona "My overall impressions of this book are excellent. This book emphasizes the three areas I want: advanced C++, data structures and the STL and is much stronger in these areas than other competing books." --Al Verbanec, Pennsylvania State University Think, Then Code When it comes to writing code, preparation is crucial to success. Before you can begin writing successful code, you need to first work through your options and analyze the expected performance of your design. That's why Elliot Koffman and Paul Wolfgang's Objects, Abstraction, Data Structures, and Design: Using C++ encourages you to Think, Then Code, to help you make good decisions in those critical first steps in the software design process. The text helps you thoroughly understand basic data structures and algorithms, as well as essential design skills and principles. Approximately 20 case studies show you how to apply those skills and principles to real-world problems. Along the way, you'll gain an understanding of why different data structures are needed, the applications they are suited for, and the advantages and disadvantages of their possible implementations. Key Features * Object-oriented approach. * Data structures are presented in the context of software design principles. * 20 case studies reinforce good programming practice. * Problem-solving methodology used throughout... "Think, then code!" * Emphasis on the C++ Standard Library. * Effective pedagogy.

The Algorithm Design Manual

Author : Steven S Skiena
File Size : 73.72 MB
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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. 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. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. 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

97 Things Every Data Engineer Should Know

Author : Tobias Macey
File Size : 43.74 MB
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Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail

Why Everyone Else Is a Hypocrite

Author : Robert Kurzban
File Size : 43.65 MB
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We're all hypocrites. Why? Hypocrisy is the natural state of the human mind. Robert Kurzban shows us that the key to understanding our behavioral inconsistencies lies in understanding the mind's design. The human mind consists of many specialized units designed by the process of evolution by natural selection. While these modules sometimes work together seamlessly, they don't always, resulting in impossibly contradictory beliefs, vacillations between patience and impulsiveness, violations of our supposed moral principles, and overinflated views of ourselves. This modular, evolutionary psychological view of the mind undermines deeply held intuitions about ourselves, as well as a range of scientific theories that require a "self" with consistent beliefs and preferences. Modularity suggests that there is no "I." Instead, each of us is a contentious "we"--a collection of discrete but interacting systems whose constant conflicts shape our interactions with one another and our experience of the world. In clear language, full of wit and rich in examples, Kurzban explains the roots and implications of our inconsistent minds, and why it is perfectly natural to believe that everyone else is a hypocrite.