Introduction

• Purpose
  To introduce various abstract data types and their associated algorithms so that you can write correct, efficient, robust, elegant and re-usable programs to solve problems.
  • Correctness: per specifications
  • Efficiency: by choosing and using proper concrete data structures and good algorithms
  • Robust: reasonable and sufficient exception handling
  • Elegancy: easily understandable, maintainable and evovable code
  • Re-usability: modularized implementation, can easily plug and play in other projects
• Data Structures:
  A data structure is a concrete representation of data from the point of view of an implementer.
• Abstract Data Types (from Wiki)
  An abstract data type (ADT) is a mathematical model for data types, where a data type is defined by its behavior (semantics) from the point of view of a user of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations.
• For each ADT:
  • What is it? (Its properties and its typical operations)
  • How to build it? (Its implementation with various concrete data structures and associated algorithms)
  • How to use it? (Its typical applications)
• Concrete data structures we have encountered before
  • Arrays
  • Structs
  • Pointer based lists
• Abstract data types we have encountered before
  • Stack
  • Queue
  • some of Trees
• Topic Overview
  • ADTs
    • Data Objects as Black Boxes
      • Stack and Queue (review)
      • Lists and Sets
    • Data Objects as Black Boxes with additional attributes
      • Priority Queue and Heap
    • Data Objects as Opaque Elements (reveal a bit of their internal information, such as their keys)
      • Dictionary and Maps
      • Hashing and Hash table
      • Tree
      • Skip List
      • Trie
    • Heterogeneous Elements together: Graph (consists Nodes and Edges)
    • Simple but widely used objects: String
  • Algorithms
    • recursion
    • selection and sorting
    • string matching
    • data encoding and compression
    • dynamic programming
    • P and NP
  • Complexity Analysis
    • Analysis in general
    • Big-O Notation (and little-o, Big-Omega, little-Omega, and Theta)
    • Recurrence Equation
    • Proof by Induction
    • The Master Theorem