Introduction to Algorithmic Thinking

Introduction to Algorithmic Thinking

• Algorithmic thinking lays the groundwork for understanding software and data science
• Algorithms provide step-by-step instructions for solving problems, crucial in the field of programming

Algorithms in Everyday Life

• Algorithms are not exclusive to computers; they are used in everyday tasks like organizing laundry or finding the best route on a map
• When computers use algorithms, they follow instructions sequentially for efficient task completion that enables quick data handling and desired results

Learning Algorithms

• Learning algorithms involves understanding the fundamental principles behind computer logic
• Understanding algorithms helps in problem-solving, smart solution creation, and entering the world of programming

Computer Program Basics

• Computer programs are widespread, powering applications on devices like smartphones, tablets, and laptops
• These programs also support businesses like Amazon and Netflix, and assist in transportation systems such as driving cars and flying planes
• Computer programs consist of instructions executed one at a time and the basic instructions types include the following:
  • Programs receive data from various sources such as files, keyboards, touchscreens or networks, using Input
  • Process enables programs to performs computations on data, like adding values (x + y)
  • Programs use Output to puts data somewhere, such as to a file, screen, or network
• Programs utilize variables to refer to data, with values that can change or "vary" as the program executes

Analogies for Programs

• Programs are similar to cooking recipes which consist of instructions that a chef executes, like adding eggs or stirring ingredients
• Computer programs consist of instructions that a computer executes, like multiplying numbers or outputting a number to a screen.

Computational Thinking

• Computational thinking involves creating instruction sequences to solve problems, which is increasingly important in the information age
• A sequence of instructions that solves a problem is called an algorithm

Algorithms for Familiarization

• Turtle graphics involves instructing a robotic turtle to move along a path using commands like "Turn left", "Walk forward 10 steps", or "Pen down" to draw a line while walking

Introduction to Algorithms

• An algorithm describes a sequence of steps to solve a computational problem or perform a calculation, and it can use English, pseudocode, a programming language, or hardware
• A computational problem involves input, a question about the input, the use of a computer to provide an answer, and a desired output

Applications of Algorithms

• Computational problems exist in numerous domains like e-commerce, internet, biology, manufacturing, and transportation
• It may be challenging to find the best algorithm to solve a computational problem that has common subproblems for which efficient algorithms are developed

Example Computational Problems and Algorithms

• The following are examples of where computational problems and common algorithms are applied:
• DNA analysis uses the Longest common substring algorithm to determine the longest shared sequence of nucleotides
• Search engines utilize the Binary search algorithm to check if a product is in stock and determine its price
• Navigation systems use Dijkstra's shortest path algorithm to determine the fastest route between a user's location and a desired location

Efficient Algorithms

• Computer scientists mainly focus on using and designing efficient algorithms to solve problems, algorithm efficiency is commonly measured by runtime
• An efficient algorithm has a runtime that increases no more than polynomially in relation to input size
• Some problems exist for which an efficient algorithm is unknown, and these are termed NP-complete problems

NP-Complete Problems

• No efficient algorithm has been found to solve an NP-complete problem
• It has also not been proven than an efficient algorithm to solve an NP-complete problem is impossible
• If an efficient algorithm exists for one NP-complete problem, then all NP-complete problems can be solved efficiently

Algorithms in Daily Life

• Algorithms in daily life are structured methodologies that guide us through tasks, achieved by systematic execution
• Algorithms in daily life give a framework for navigating challenges and offer a structured approach that leads to desired outcomes
• Daily life algorithms offer both predictability and reliability allowing people to anticipate results and cultivate control, but are also adaptable like software algorithms

Algorithms Provide Flexibility

• Adjustments may be needed to accommodate changes in environment, preferences, or constraints
• Algorithms are ingrained with cultural norms, traditions, and societal expectations

Examples of Algorithms in Daily Life

• Recipes guide cooking tasks
• GPS and maps directs driving directions
• Laundry cleaning and drying uses various steps

How Computers Interpret Algorithms

• Computers take in data from various sources such as keyboards, mice, files, or networks
• Once input is received, the computer processes it according to the algorithm's instructions that include a combination of calculations, comparisons, and data manipulations
• After processing, the algorithm provides an output through calculated values, data structures, or messages on devices

Algorithm Best Practices

• Algorithm development requires a blend of logical thinking, problem-solving skills, and attention to detail
• Important steps to follow that optimize algorithm development:
  • Problem understanding before coding
  • Correct algorithm selection
  • Sketching with pseudocode
  • Code modularization
  • Edge case handling or corner cases
  • Code readability
  • Algorithm testing with various input cases
  • Optimizing for performance
  • Algorithm documentation and explanation
  • Frequent code iteration and refinement

Summary of Algorithmic Thinking

• Important for programming and data science, and are used in everyday tasks that are step-by-step guides to follow
• Algorithms involve step-by-step problem solving that are everywhere and have a basis to assist you with understanding the way computers function