Course Introduction

Course Objectives

• Discuss key data structures and algorithms essential for solving problems with computers
• (Powerful) Programming skills
  • Translate your idea into languages that computers can understand
  • Live coding session!
• Computational thinking
  • Problem formulation (abstraction)
  • Solution expression (automation)
  • Solution execution and evaluation (analysis)
• “How to think like a computer scientist”

Summary

Definitions and examples of

• Data structures
• Algorithms

Data Structures

Data Structure is a collection of

• Data values
• Relationships among data values
• Operations that can be applied to the data

Abstraction

Process of removing details while preserving information relevant to solving the problem

Fundamental data structures

Primitive	Non Primitive
Integer, Float, Character	Linear, Non Linear, Files

Linear	Non-Linear
Arrays, Lists	Trees, Graphs

Lists
Stacks, Queues

Appropriate data structure should be chosen according to the data characteristics

Algorithm

• An algorithm is a sequence of computational steps that transform the input into the output.
• An instance of a problem consists of the input needed to compute a solution to the problem.
  • A correct algorithm:
  • for every input instances, halts with the correct output

Sorting Problem

• A sorting algorithm
  • Input: A sequence of n numbers <a1, a2,…, an>
  • Output: A permutation (reordering) of the input sequence, <b1, b2,…, bn>, such that b1=<b2=<…=<bn
  • Instance: <7, 10, 4, 5, 2>

source “K-MOOC 허재필 교수님의 <인공지능을 위한 알고리즘과 자료구조: 이론, 코딩, 그리고 컴퓨팅 사고> 강좌의 1-1 자료구조/알고리즘의 정의와 중요성 중(http://www.kmooc.kr/courses/course-v1:SKKUk+SKKU_46+2020_T1)”

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