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LEARN TO OPTIMISE YOUR CODE

Overview

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Want to learn how to solve coding problems easily and efficiently?

​

Time to learn about the pillars of good code and programming: Data structures and algorithms. In the Data Structures Essentials: Pseudocode with Python Examples zyBook, you will learn and master these basics in pseudocode and Python.

Research Proven Excellence

Preferred Choice

80% of surveyed students preferred zyBooks over regular textbooks.

Better Learning

Studies show that students learn 118% more with the minimal text model in only one lesson.

Proven Results

On average, zyBook users improved grades up to ⅔ and read 74% more than users of regular textbooks.

*All findings backed by research.

What is a zyBook?

In short, zyBooks are interactive, digital textbooks.

 

By incorporating available technology, zyBooks make learning fun, interactive and engaging – all while drastically reducing the time spent on learning theoretical concepts.

Course Details

The Data Structures Essentials: Pseudocode with Python Examples zyBook teaches essential data structures and algorithms with minimal text, maximum interactivity.

  • INTRODUCTION
    Introduction What is AI good for?
  • GRAPH-SEARCH ALGORITHMS
    Breadth-first search introduction Breadt-first search implementation Depth-first search introduction Depth-first search implementation I - with stack Depth-first search implementation II - with recursion Enhanced search algorithms introduction Iterative deepening depth-first search (IDDFS) A* search introduction
  • BASIC SEARCH & OPTIMIZATION ALGORITHMS
    Brute-force search introduction Brute-force search example Stochastic search introduction Stochastic search example Hill climbing introduction Hill climbing example
  • META-HEURISTIC OPTIMIZATION METHODS
    Heuristics VS meta-heuristics Tabu search introduction Simulated annealing introduction Simulated annealing - function extremum I Simulated annealing - function extremum II Simulated annealing - function extremum III Travelling salesman problem I - city Travelling salesman problem II - tour Travelling salesman problem III - annealing algorithm Travelling salesman problem IV - testing Genetic algorithms introduction - basics Genetic algorithms introduction - chromosomes Genetic algorithms introduction - crossover Genetic algorithms introduction - mutation Genetic algorithms introduction - the algorithm Genetic algorithm implementation I - individual Genetic algorithm implementation II - population Genetic algorithm implementation III - the algorithm Genetic algorithm implementation IV - testing Genetic algorithm implementation V - function optimum Swarm intelligence intoduction Partical swarm optimization introduction I - basics Partical swarm optimization introduction II - the algorithm Particle swarm optimization implementation I - particle Particle swarm optimization implementation II - initialize Particle swarm optimization implementation III - the algorithm Particle swarm optimization implementation IV - testing
  • MINIMAX ALGORITHM - GAME ENGINES
    Game trees introduction Minimax algorithm introduction - basics Minimax algorithm introduction - the algorithm Minimax algorithm introduction - relation with tic-tac-toe Alpha-beta pruning introduction Alpha-beta pruning example Chess problem
  • BUILDING TIC-TAC-TOE
    About the game Cell Constants and Player Game implementation I Game implementation II Board implementation I Board implementationj II - isWinning() Board implementation III Minimax algorithm Running tic-tac-toe
  • INTERVIEW: SINGAPOREAN EXPERT
    Background of Expert Information and Communication Technology in Singapore

Pricing

Each purchase comes with...

Practice questions

Highly effective reading materials

1 year access

Interactive figures & tables

Online lab environment (zyLabs)

What Our Students Say

“I really enjoyed zyBooks for use in my Python class. It has surely aided my success in class and helped me build some confidence in my first year at university.”
 

Isaac C.

Cal State University, Long Beach

  • INTRODUCTION
    Introduction What is AI good for?
  • GRAPH-SEARCH ALGORITHMS
    Breadth-first search introduction Breadt-first search implementation Depth-first search introduction Depth-first search implementation I - with stack Depth-first search implementation II - with recursion Enhanced search algorithms introduction Iterative deepening depth-first search (IDDFS) A* search introduction
  • BASIC SEARCH & OPTIMIZATION ALGORITHMS
    Brute-force search introduction Brute-force search example Stochastic search introduction Stochastic search example Hill climbing introduction Hill climbing example
  • META-HEURISTIC OPTIMIZATION METHODS
    Heuristics VS meta-heuristics Tabu search introduction Simulated annealing introduction Simulated annealing - function extremum I Simulated annealing - function extremum II Simulated annealing - function extremum III Travelling salesman problem I - city Travelling salesman problem II - tour Travelling salesman problem III - annealing algorithm Travelling salesman problem IV - testing Genetic algorithms introduction - basics Genetic algorithms introduction - chromosomes Genetic algorithms introduction - crossover Genetic algorithms introduction - mutation Genetic algorithms introduction - the algorithm Genetic algorithm implementation I - individual Genetic algorithm implementation II - population Genetic algorithm implementation III - the algorithm Genetic algorithm implementation IV - testing Genetic algorithm implementation V - function optimum Swarm intelligence intoduction Partical swarm optimization introduction I - basics Partical swarm optimization introduction II - the algorithm Particle swarm optimization implementation I - particle Particle swarm optimization implementation II - initialize Particle swarm optimization implementation III - the algorithm Particle swarm optimization implementation IV - testing
  • MINIMAX ALGORITHM - GAME ENGINES
    Game trees introduction Minimax algorithm introduction - basics Minimax algorithm introduction - the algorithm Minimax algorithm introduction - relation with tic-tac-toe Alpha-beta pruning introduction Alpha-beta pruning example Chess problem
  • BUILDING TIC-TAC-TOE
    About the game Cell Constants and Player Game implementation I Game implementation II Board implementation I Board implementationj II - isWinning() Board implementation III Minimax algorithm Running tic-tac-toe
  • INTERVIEW: SINGAPOREAN EXPERT
    Background of Expert Information and Communication Technology in Singapore
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