Table of Contents
Grokking Data Structures by Marcello La Rocca
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Product Details
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- Publisher and Publication Date: Manning - MEAP began September 2023, Publication in Summer 2024 (estimated)
Book Summary
“Don’t be perplexed by data structures! This fun, friendly, and fully-illustrated guide makes it easy to learn useful data structures you’ll put to work every day.” (978-1633436992 2024)
“Data structures are vital for shaping and handling all of the data an organization processes — that’s why they show up in so many IT interviews! But you don’t need to be a math expert to understand data structures. This book will prove it. You’ll discover the most common and most useful day-to-day data structures through fun examples, engaging industry stories, and hundreds of illustrations.” (978-1633436992 2024)
In Grokking Data Structures you’ll learn how to:
- Understand the most important and widely used data structures
- Identify use cases where data structures make the biggest difference
- Speed up your searches using hash tables and hashing functions (while understanding their risks!)
- Use trees and binary search trees (BSTs) to organize data
- Pick the best data structure solution for a coding challenge
- Understand the tradeoffs of data structures and avoid catastrophes
- Write better code with the power of data structures
“Grokking Data Structures carefully guides you from the most basic data structures like arrays or linked lists all the way to powerful structures like graphs. It’s perfect for beginners — and you won’t need anything more than high school math to get started. Each data structure you encounter comes with its own complete Python data structure implementation so you can start experimenting with what you learn right away.” (978-1633436992 2024)
about the book
“Grokking Data Structures makes it a breeze to learn even complex data structures. You’ll follow a steady learning path from absolute basics to advanced concepts, with fun cartoons and stories to make sure every structure sticks! Real-world examples show you how data structures are used in industry — such as making your searches faster with sorted arrays and binary search, handling your tasks list with a queue, and exploring multiple methods to handle triage in an emergency room. By the time you’ve finished reading, you’ll be able to spot when a data structure would help your code and know the perfect solution for the problem!” (978-1633436992 2024)
about the reader
For readers who know the basics of Python.
about the author
Marcello La Rocca is a research scientist and a full-stack engineer, who has contributed to creating large-scale web applications and machine learning infrastructure at Twitter, Microsoft, and Apple.
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Data Structures: Array, Linked List, Stack, Queue, Binary Tree, Binary Search Tree, Heap, Hash Table, Graph, Trie, Skip List, Red-Black Tree, AVL Tree, B-Tree, B+ Tree, Splay Tree, Fibonacci Heap, Disjoint Set, Adjacency Matrix, Adjacency List, Circular Linked List, Doubly Linked List, Priority Queue, Dynamic Array, Bloom Filter, Segment Tree, Fenwick Tree, Cartesian Tree, Rope, Suffix Array, Suffix Tree, Ternary Search Tree, Radix Tree, Quadtree, Octree, KD Tree, Interval Tree, Sparse Table, Union-Find, Min-Max Heap, Binomial Heap, And-Or Graph, Bit Array, Bitmask, Circular Buffer, Concurrent Data Structures, Content Addressable Memory, Deque, Directed Acyclic Graph (DAG), Edge List, Eulerian Path and Circuit, Expression Tree, Huffman Tree, Immutable Data Structure, Indexable Skip List, Inverted Index, Judy Array, K-ary Tree, Lattice, Linked Hash Map, Linked Hash Set, List, Matrix, Merkle Tree, Multimap, Multiset, Nested Data Structure, Object Pool, Pairing Heap, Persistent Data Structure, Quad-edge, Queue (Double-ended), R-Tree, Radix Sort Tree, Range Tree, Record, Ring Buffer, Scene Graph, Scapegoat Tree, Soft Heap, Sparse Matrix, Spatial Index, Stack (Min/Max), Suffix Automaton, Threaded Binary Tree, Treap, Triple Store, Turing Machine, Unrolled Linked List, Van Emde Boas Tree, Vector, VList, Weak Heap, Weight-balanced Tree, X-fast Trie, Y-fast Trie, Z-order, Zero-suppressed Decision Diagram, Zigzag Tree
Data Structures Fundamentals - Algorithms Fundamentals, Algorithms, Data Types; Primitive Types (Boolean data type, Character (computing), Floating-point arithmetic, Single-precision floating-point format - Double-precision floating-point format, IEEE 754, Category:Floating point types, Fixed-point arithmetic, Integer (computer science), Reference (computer science), Pointer (computer programming), Enumerated type, Date Time);
Composite Types or Non-Primitive Types: Array data structure, String (computer science) (Array of characters), Record (computer science) (also called Struct (C programming language)), Union type (Tagged union, also called Variant type, Variant record, Discriminated union, or Disjoint union);
Abstract Data Types: Container (data structure), List (abstract data type), Tuple, Associative array (also called Map, Multimap, Set (abstract data type), Multiset (abstract data type) (also called Multiset (bag)), Stack (abstract data type), Queue (abstract data type), (e.g. Priority queue), Double-ended queue, Graph (data structure) (e.g. Tree (data structure), Heap (data structure))
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