The Most Popular Algorithms & Data Structures Books

The Most Popular Algorithms & Data Structures Books

Being a great programmer is not only about programming in the right language, but coming up with the right algorithm or data structure is just as important when creating a scalable solution. These following algorithm and data structures books are a great way to get you started and possibly to keep you up late at night!

  • Introduction to Algorithms, Third Edition (International Edition)
    Introduction to Algorithms, Third Edition (International Edition)Introduction to Algorithms uniquely combines rigor and comprehensiveness. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks.
  • The Algorithm Design ManualThe Algorithm Design ManualMost professional programmers that I’ve encountered are not well prepared to tackle algorithm design problems. Good algorithm designers understand several fundamental algorithm design techniques, including data structures, dynamic programming, depth first search, backtracking, and heuristics.
  • Programming Collective Intelligence: Building Smart Web 2.0 Applications [Read online]
    Programming Collective Intelligence: Building Smart Web 2.0 ApplicationsThis fascinating book demonstrates how you can build Web 2. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you’ve found it.
  • Hacker’s Delight (2nd Edition) [Read online]
    Hacker's Delight (2nd Edition)In Hacker’s Delight, Second Edition, Hank Warren once again compiles an irresistible collection of programming hacks: timesaving techniques, algorithms, and tricks that help programmers build more elegant and efficient software, while also gaining deeper insights into their craft. Warren’s hacks are eminently practical, but they’re also intrinsically interesting, and sometimes unexpected, much like the solution to a great puzzle.
  • Algorithms (4th Edition) [Read online]
    Algorithms (4th Edition)This fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms is the leading textbook on algorithms today and is widely used in colleges and universities worldwide. This book surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing–including fifty algorithms every programmer should know. edu, contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material.
  • Security Engineering: A Guide to Building Dependable Distributed Systems
    Security Engineering: A Guide to Building Dependable Distributed SystemsSpammers, virus writers, phishermen, money launderers, and spies now trade busily with each other in a lively online criminal economy and as they specialize, they get better. In this indispensable, fully updated guide, Ross Anderson reveals how to build systems that stay dependable whether faced with error or malice. s straight talk on critical topics such as technical engineering basics, types of attack, specialized protection mechanisms, security psychology, policy, and more.
  • Doing Data Science: Straight Talk from the Frontline [Read online]
    Doing Data Science: Straight Talk from the FrontlineNow that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data 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.
  • Weapons of Math Destruction [Read online]
    Weapons of Math DestructionThe models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy. Tracing the arc of a person’s life, O’Neil exposes the black box models that shape our future, both as individuals and as a society.

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