Number theory, which Carl-Friedrich Gauss famously dubbed the queen of mathematics, has always been considered a very beautiful field of mathematics, producing lovely results and elegant proofs. While only very few real-life applications were known in the past, today number theory can be found in everyday life: in supermarket bar code scanners, in our cars’ GPS systems, in online banking, etc.
Starting with a brief introductory course on number theory in Chapter 1, which makes the book more accessible for undergraduates, the authors describe the four main application areas in Chapters 2-5 and offer a glimpse of advanced results that are presented without proofs and require more advanced mathematical skills. In the last chapter they review several further applications of number theory, ranging from check-digit systems to quantum computation and the organization of raster-graphics memory.
Upper-level undergraduates, graduates and researchers in the field of number theory will find this book to be a valuable resource.
Readership: Graduate students and researchers in number theory, discrete mathematics, coding theory, cryptology and IT security.
Keywords:Coding Theory;Cryptology;Number Theory;Algebraic-Geometry Codes;Public-Key Infrastructures;Error-Correcting Codes
Suitable for researchers and graduate students in mathematics and computer science, this self-contained book is one of the first to focus on many topics in cryptography involving algebraic curves. After supplying the necessary background on algebraic curves, the authors discuss error-correcting codes, including algebraic geometry codes, and provide an introduction to elliptic curves. Each chapter in the remainder of the book deals with a selected topic in cryptography (other than elliptic curve cryptography). The topics covered include secret sharing schemes, authentication codes, frameproof codes, key distribution schemes, broadcast encryption, and sequences. Chapters begin with introductory material before featuring the application of algebraic curves.
The book begins with a summary of the nontechnical aspects of interviewing, such as common mistakes, strategies for a great interview, perspectives from the other side of the table, tips on negotiating the best offer, and a guide to the best ways to use EPI.
The technical core of EPI is a sequence of chapters on basic and advanced data structures, searching, sorting, broad algorithmic principles, concurrency, and system design. Each chapter consists of a brief review, followed by a broad and thought-provoking series of problems. We include a summary of data structure, algorithm, and problem solving patterns.
The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts.
The companion web site, algs4.cs.princeton.edu, containsAn online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material
The MOOC related to this book is accessible via the "Online Course" link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants.
Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience.
This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
The Art of Computer Programming, Volumes 1-4A Boxed Set, 3/e
Art of Computer Programming, Volume 4, Fascicle 4,The: Generating All Trees--History of Combinatorial Generation: Generating All Trees--History of Combinatorial Generation
This multivolume work on the analysis of algorithms has long been recognized as the definitive description of classical computer science.The three complete volumes published to date already comprise a unique and invaluable resource in programming theory and practice. Countless readers have spoken about the profound personal influence of Knuth's writings. Scientists have marveled at the beauty and elegance of his analysis, while practicing programmers have successfully applied his “cookbook” solutions to their day-to-day problems. All have admired Knuth for the breadth, clarity, accuracy, and good humor found in his books.
To begin the fourth and later volumes of the set, and to update parts of the existing three, Knuth has created a series of small books called fascicles, which will be published at regular intervals. Each fascicle will encompass a section or more of wholly new or revised material. Ultimately, the content of these fascicles will be rolled up into the comprehensive, final versions of each volume, and the enormous undertaking that began in 1962 will be complete.
Volume 4, Fascicle 4
This latest fascicle covers the generation of all trees, a basic topic that has surprisingly rich ties to the first three volumes of The Art of Computer Programming. In thoroughly discussing this well-known subject, while providing 124 new exercises, Knuth continues to build a firm foundation for programming. To that same end, this fascicle also covers the history of combinatorial generation. Spanning many centuries, across many parts of the world, Knuth tells a fascinating story of interest and relevance to every artful programmer, much of it never before told. The story even includes a touch of suspense: two problems that no one has yet been able to solve.
We are living in the computer age, in a world increasingly designed and engineered by computer programmers and software designers, by people who call themselves hackers. Who are these people, what motivates them, and why should you care?
Consider these facts: Everything around us is turning into computers. Your typewriter is gone, replaced by a computer. Your phone has turned into a computer. So has your camera. Soon your TV will. Your car was not only designed on computers, but has more processing power in it than a room-sized mainframe did in 1970. Letters, encyclopedias, newspapers, and even your local store are being replaced by the Internet.
Hackers & Painters: Big Ideas from the Computer Age, by Paul Graham, explains this world and the motivations of the people who occupy it. In clear, thoughtful prose that draws on illuminating historical examples, Graham takes readers on an unflinching exploration into what he calls "an intellectual Wild West."
The ideas discussed in this book will have a powerful and lasting impact on how we think, how we work, how we develop technology, and how we live. Topics include the importance of beauty in software design, how to make wealth, heresy and free speech, the programming language renaissance, the open-source movement, digital design, internet startups, and more.
Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a naïve Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a “whom to follow” recommendation system from Twitter data
The Art of Computer Programming, Volumes 1-4A Boxed Set, 3/e
Art of Computer Programming, Volume 1, Fascicle 1, The: MMIX -- A RISC Computer for the New Millennium
This multivolume work on the analysis of algorithms has long been recognized as the definitive description of classical computer science. The three complete volumes published to date already comprise a unique and invaluable resource in programming theory and practice. Countless readers have spoken about the profound personal influence of Knuth's writings. Scientists have marveled at the beauty and elegance of his analysis, while practicing programmers have successfully applied his "cookbook" solutions to their day-to-day problems. All have admired Knuth for the breadth, clarity, accuracy, and good humor found in his books.
To begin the fourth and later volumes of the set, and to update parts of the existing three, Knuth has created a series of small books called fascicles, which will be published t regular intervals. Each fascicle will encompass a section or more of wholly new or evised material. Ultimately, the content of these fascicles will be rolled up into the comprehensive, final versions of each volume, and the enormous undertaking that began in 1962 will be complete.
Volume 1, Fascicle 1
This first fascicle updates The Art of Computer Programming, Volume 1, Third Edition: Fundamental Algorithms, and ultimately will become part of the fourth edition of that book. Specifically, it provides a programmer's introduction to the long-awaited MMIX, a RISC-based computer that replaces the original MIX, and describes the MMIX assembly language. The fascicle also presents new material on subroutines, coroutines, and interpretive routines.
Ebook (PDF version) produced by Mathematical Sciences Publishers (MSP),http://msp.org
Algorithms in C++, Third Edition, Part 5: Graph Algorithms is the second book in Sedgewick's thoroughly revised and rewritten series. The first book, Parts 1-4, addresses fundamental algorithms, data structures, sorting, and searching. A forthcoming third book will focus on strings, geometry, and a range of advanced algorithms. Each book's expanded coverage features new algorithms and implementations, enhanced descriptions and diagrams, and a wealth of new exercises for polishing skills. A focus on abstract data types makes the programs more broadly useful and relevant for the modern object-oriented programming environment.
Coverage includes:A complete overview of graph properties and types Diagraphs and DAGs Minimum spanning trees Shortest paths Network flows Diagrams, sample C++ code, and detailed algorithm descriptions
The Web site for this book (http://www.cs.princeton.edu/~rs/) provides additional source code for programmers along with a wide range of academic support materials for educators.
A landmark revision, Algorithms in C++, Third Edition, Part 5 provides a complete tool set for programmers to implement, debug, and use graph algorithms across a wide range of computer applications.
Many new algorithms are presented, and the explanations of each algorithm are much more detailed than in previous editions. A new text design and detailed, innovative figures, with accompanying commentary, greatly enhance the presentation. The third edition retains the successful blend of theory and practice that has made Sedgewick's work an invaluable resource for more than 250,000 programmers!
This particular book, Parts 1n4, represents the essential first half of Sedgewick's complete work. It provides extensive coverage of fundamental data structures and algorithms for sorting, searching, and related applications. Although the substance of the book applies to programming in any language, the implementations by Van Wyk and Sedgewick also exploit the natural match between C++ classes and ADT implementations.Highlights Expanded coverage of arrays, linked lists, strings, trees, and other basic data structures Greater emphasis on abstract data types (ADTs), modular programming, object-oriented programming, and C++ classes than in previous editions Over 100 algorithms for sorting, selection, priority queue ADT implementations, and symbol table ADT (searching) implementations New implementations of binomial queues, multiway radix sorting, randomized BSTs, splay trees, skip lists, multiway tries, B trees, extendible hashing, and much more Increased quantitative information about the algorithms, giving you a basis for comparing them Over 1000 new exercises to help you learn the properties of algorithms
Whether you are learning the algorithms for the first time or wish to have up-to-date reference material that incorporates new programming styles with classic and new algorithms, you will find a wealth of useful information in this book.
It used to be that to diagnose an illness, interpret legal documents, analyze foreign policy, or write a newspaper article you needed a human being with specific skills—and maybe an advanced degree or two. These days, high-level tasks are increasingly being handled by algorithms that can do precise work not only with speed but also with nuance. These “bots” started with human programming and logic, but now their reach extends beyond what their creators ever expected.
In this fascinating, frightening book, Christopher Steiner tells the story of how algorithms took over—and shows why the “bot revolution” is about to spill into every aspect of our lives, often silently, without our knowledge.
The May 2010 “Flash Crash” exposed Wall Street’s reliance on trading bots to the tune of a 998-point market drop and $1 trillion in vanished market value. But that was just the beginning. In Automate This, we meet bots that are driving cars, penning haiku, and writing music mistaken for Bach’s. They listen in on our customer service calls and figure out what Iran would do in the event of a nuclear standoff. There are algorithms that can pick out the most cohesive crew of astronauts for a space mission or identify the next Jeremy Lin. Some can even ingest statistics from baseball games and spit out pitch-perfect sports journalism indistinguishable from that produced by humans.
The interaction of man and machine can make our lives easier. But what will the world look like when algorithms control our hospitals, our roads, our culture, and our national security? What happens to businesses when we automate judgment and eliminate human instinct? And what role will be left for doctors, lawyers, writers, truck drivers, and many others?Who knows—maybe there’s a bot learning to do your job this minute.
The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research.
The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text.
Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided.Many illustrative examples and entertaining asides MATLAB code Accessible and informal style Complete and self-contained section for mathematics review
Using a practical, hands-on approach, this book will take you through all the facets of developing Access-based solutions, such as data modeling, complex form development, and user interface customizations. You'll then deploy your solution to the web and integrate it with other external data sources. This book is full of handy tricks to help you get the most out of what Access has to offer, including its comprehensive set of features and tools for collecting, using, and acting on business data, whether your data is in Access or stored on another platform. You'll also see how to smoothly integrate your applications with SQL Server databases and other Office programs, such as Outlook.
Beginning ASP.NET 4.5 Databases is a comprehensive introduction on how you can connect a Web site to many different data sources — not just databases — and use the data to create dynamic page content. It also shows you how to build a relational database, use SQL to communicate with it, and understand how they differ from each other.
With in-depth, on-target coverage of the new data access features of .NET Framework 4.5, this book is your guide to using ASP.NET to build responsive, easy-to-update data-driven Web sites.
Windows Server 2008 Active Directory, Configuring
Covers the critical information you’ll need to know to score higher on Exam 70-640!
Utilize new features of Windows Server 2008 related to Active Directory
Install and configure Active Directory forests and domains
Plan and implement an Organizational Unit structure
Plan and manage Active Directory infrastructure components, such as operations masters, global catalogs, read-only domain controllers, and sites
Plan, implement, and manage Active Directory using Group Policy
Plan and manage Active Directory roles, including Active Directory Lightweight Directory Services, Active Directory Rights Management Services, and Active Directory Federation Services
Deploy software in Active Directory using Group Policy
Utilize Active Directory features to manage users and groups
Back up and restore Active Directory, and rebuild an Active Directory domain controller from backup
Deploy and manage a public key infrastructure using Active Directory Certificate Services
WRITTEN BY A LEADING EXPERT:
Don Poulton, MCSA, MCSE, A+, Network+, Security+, has been involved in consulting with small training providers as a technical writer, during which time he wrote training and exam prep materials for Windows NT 4.0, Windows 2000, and Windows XP. More recently, he has written or co-authored several certification volumes on Security+, Windows XP, Windows Server 2003, and Windows Vista, published by Que Publishing.
Extensive additions in this edition includeA new chapter on cyclic redundancy checking (CRC), including routines for the commonly used CRC-32 code A new chapter on error correcting codes (ECC), including routines for the Hamming code More coverage of integer division by constants, including methods using only shifts and adds Computing remainders without computing a quotient More coverage of population count and counting leading zeros Array population count New algorithms for compress and expand An LRU algorithm Floating-point to/from integer conversions Approximate floating-point reciprocal square root routine A gallery of graphs of discrete functions Now with exercises and answers
Good math skills are indispensable for all engineers regardless of their specialty, yet only a relatively small portion of the math that engineering students study in college mathematics courses is used on a frequent basis in the study or practice of engineering. That's why Essential Math Skills for Engineers focuses on only these few critically essential math skills that students need in order to advance in their engineering studies and excel in engineering practice.
Essential Math Skills for Engineers features concise, easy-to-follow explanations that quickly bring readers up to speed on all the essential core math skills used in the daily study and practice of engineering. These fundamental and essential skills are logically grouped into categories that make them easy to learn while also promoting their long-term retention. Among the key areas covered are:
Algebra, geometry, trigonometry, complex arithmetic, and differential and integral calculus
Simultaneous, linear, algebraic equations
Linear, constant-coefficient, ordinary differential equations
Linear, constant-coefficient, difference equations
Linear, constant-coefficient, partial differential equations
Fourier series and Fourier transform
Mathematics of vectors
With the thorough understanding of essential math skills gained from this text, readers will have mastered a key component of the knowledge needed to become successful students of engineering. In addition, this text is highly recommended for practicing engineers who want to refresh their math skills in order to tackle problems in engineering with confidence.
Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference.
At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to:Learn the languages of machine learning including Hadoop, Mahout, and Weka Understand decision trees, Bayesian networks, and artificial neural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficient machine learning
By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
Robert Sedgewick and the late Philippe Flajolet have drawn from both classical mathematics and computer science, integrating discrete mathematics, elementary real analysis, combinatorics, algorithms, and data structures. They emphasize the mathematics needed to support scientific studies that can serve as the basis for predicting algorithm performance and for comparing different algorithms on the basis of performance.
Techniques covered in the first half of the book include recurrences, generating functions, asymptotics, and analytic combinatorics. Structures studied in the second half of the book include permutations, trees, strings, tries, and mappings. Numerous examples are included throughout to illustrate applications to the analysis of algorithms that are playing a critical role in the evolution of our modern computational infrastructure.
Improvements and additions in this new edition includeUpgraded figures and code An all-new chapter introducing analytic combinatorics Simplified derivations via analytic combinatorics throughout
The book’s thorough, self-contained coverage will help readers appreciate the field’s challenges, prepare them for advanced results—covered in their monograph Analytic Combinatorics and in Donald Knuth’s The Art of Computer Programming books—and provide the background they need to keep abreast of new research.
"[Sedgewick and Flajolet] are not only worldwide leaders of the field, they also are masters of exposition. I am sure that every serious computer scientist will find this book rewarding in many ways."
—From the Foreword by Donald E. Knuth
This book is for Java developers with basic Java programming knowledge. No previous knowledge of neural networks is required as this book covers the concepts from scratch.What You Will LearnGet to grips with the basics of neural networks and what they are used forDevelop neural networks using hands-on examplesExplore and code the most widely-used learning algorithms to make your neural network learn from most types of dataDiscover the power of neural network's unsupervised learning process to extract the intrinsic knowledge hidden behind the dataApply the code generated in practical examples, including weather forecasting and pattern recognitionUnderstand how to make the best choice of learning parameters to ensure you have a more effective applicationSelect and split data sets into training, test, and validation, and explore validation strategiesDiscover how to improve and optimize your neural networkIn Detail
Vast quantities of data are produced every second. In this context, neural networks become a powerful technique to extract useful knowledge from large amounts of raw, seemingly unrelated data. One of the most preferred languages for neural network programming is Java as it is easier to write code using it, and most of the most popular neural network packages around already exist for Java. This makes it a versatile programming language for neural networks.
This book gives you a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java.
You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using the concepts you've learned. Furthermore, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time.
All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience.Style and approach
This book adopts a step-by-step approach to neural network development and provides many hands-on examples using Java programming. Each neural network concept is explored through real-world problems and is delivered in an easy-to-comprehend manner.
If you’re a reasonably proficient programmer who can think logically, you have all the background you’ll need. Stepanov and Rose introduce the relevant abstract algebra and number theory with exceptional clarity. They carefully explain the problems mathematicians first needed to solve, and then show how these mathematical solutions translate to generic programming and the creation of more effective and elegant code. To demonstrate the crucial role these mathematical principles play in many modern applications, the authors show how to use these results and generalized algorithms to implement a real-world public-key cryptosystem.
As you read this book, you’ll master the thought processes necessary for effective programming and learn how to generalize narrowly conceived algorithms to widen their usefulness without losing efficiency. You’ll also gain deep insight into the value of mathematics to programming—insight that will prove invaluable no matter what programming languages and paradigms you use.
You will learn aboutHow to generalize a four thousand-year-old algorithm, demonstrating indispensable lessons about clarity and efficiency Ancient paradoxes, beautiful theorems, and the productive tension between continuous and discrete A simple algorithm for finding greatest common divisor (GCD) and modern abstractions that build on it Powerful mathematical approaches to abstraction How abstract algebra provides the idea at the heart of generic programming Axioms, proofs, theories, and models: using mathematical techniques to organize knowledge about your algorithms and data structures Surprising subtleties of simple programming tasks and what you can learn from them How practical implementations can exploit theoretical knowledge
Previously the domain of philosophers and linguists, information theory has now moved beyond the province of code breakers to become the crucial science of our time. In Decoding the Universe, Charles Seife draws on his gift for making cutting-edge science accessible to explain how this new tool is deciphering everything from the purpose of our DNA to the parallel universes of our Byzantine cosmos. The result is an exhilarating adventure that deftly combines cryptology, physics, biology, and mathematics to cast light on the new understanding of the laws that govern life and the universe.
Write powerful C programs…without becoming a technical expert! This book is the fastest way to get comfortable with C, one incredibly clear and easy step at a time. You’ll learn all the basics: how to organize programs, store and display data, work with variables, operators, I/O, pointers, arrays, functions, and much more. C programming has neverbeen this simple!
This C Programming book gives a good start and complete introduction for C Programming for Beginner’s. Learn the all basics and advanced features of C programming in no time from Bestselling Programming Author Harry. H. Chaudhary. This Book, starts with the basics; I promise this book will make you 100% expert level champion of C Programming.
This book contains 1000+ Live C Program’s code examples, and 500+ Lab Exercise & 200+ Brain Wash Topic-wise Code book and 20+ Live software Development Project’s. All what you need ! Isn’t it ?
Write powerful C programs…without becoming a technical expert! This book is the fastest way to get comfortable with C, one incredibly clear and easy step at a time. You’ll learn all the basics: how to organize programs, store and display data, work with variables, operators, I/O, pointers, arrays, functions, and much more. (See Below List)C programming has never been this simple!
Who knew how simple C programming could be?
This is today’s best beginner’s guide to writing C programs–and to learning skills you can use with practically any language. Its simple, practical instructions will help you start creating useful, reliable C code. This book covers common core syllabus for BCA, MCA, B.TECH, BS (CS), MS (CS), BSC-IT (CS), MSC-IT (CS), and Computer Science Professionals as well as for Hackers.
This Book is very serious C Programming stuff: A complete introduction to C Language. You'll learn everything from the fundamentals to advanced topics. If you've read this book, you know what to expect a visually rich format designed for the way your brain works. If you haven't, you're in for a treat. You'll see why people say it's unlike any other C book you've ever read.
Learning a new language is no easy. You might think the problem is your brain. It seems to have a mind of its own, a mind that doesn't always want to take in the dry, technical stuff you're forced to study. The fact is your brain craves novelty. It's constantly searching, scanning, waiting for something unusual to happen. After all, that's the way it was built to help you stay alive. It takes all the routine, ordinary, dull stuff and filters it to the background so it won't interfere with your brain's real work--recording things that matter. How does your brain know what matters?
(A) 1000+ Live C Program’s code examples,
(B) 500+ Lab Exercises,
(C) 200+ Brain Wash Topic-wise Code
(D) 20+ Live software Development Project’s.
(E) Learn Complete C- without fear,
|| Inside Chapters. ||
1. Preface – Page-6, || Introduction to C.
2. Elements of C Programming Language.
3. Control statements (conditions).
4. Control statements (Looping).
5. One dimensional Array.
6. Multi-Dimensional Array.
7. String (Character Array).
8. Your Brain on Functions.
9. Your Brain on Pointers.
10. Structure, Union, Enum, Bit Fields, Typedef.
11. Console Input and Output.
12. File Handling In C.
13. Miscellaneous Topics.
14. Storage Class.
16. Unsolved Practical Problems.
17. PART-II-120+ Practical Code Chapter-Wise.
18. Creating & Inserting own functions in Liberary.
19. Graphics Programming In C.
20. Operating System Development –Intro.
21. C Programming Guidelines.
22. Common C Programming Errors.
23. Live Software Development Using C.
In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.
Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and Hadoop
Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
Using the Association of College and Research Libraries' Information Literacy Competency Standards for Higher Education as a framework, this much-needed sourcebook covers all the major facets of the information literacy process. For students, it is a ready-to-use guide that explains what information literacy is, why it is so important, and how to put it to use in both print and online research. For teachers, it is a helpful classroom resource that can serve as the basis for an information literacy course, a supplemental text, or a handy reference for research in any subject.