Data Structures & Algorithms In Go

Grow your proficiency in Golang by learning about algorithm design, analysis, and use of common Golang data structures by solving coding exercises.

Beginner

210 Lessons

55h

Certificate of Completion

Grow your proficiency in Golang by learning about algorithm design, analysis, and use of common Golang data structures by solving coding exercises.

AI-POWERED

Mock Interview

Explanations

AI-POWERED

Mock Interview

Explanations

This course includes

128 Playgrounds

81 Challenges

This course includes

128 Playgrounds

81 Challenges

Course Overview

Data structures and algorithms are central concepts of computer science in general and software development in particular. Logging, transforming, and analyzing data are core skills in both real-world applications and coding interviews. This course is a detailed guide for data structures and algorithms with specific applications in the Go programming language. You’ll learn basic data structures starting with arrays, stacks, queues, hash tables, and trees. You’ll then process the data with common techniques,...Show More

TAKEAWAY SKILLS

Programming Language

Data Structures

Algorithms

What You'll Learn

A strong understanding of fundamental data structures and algorithms in Go

The ability to implement common data structures in Go

The ability to process data using basic algorithms in Go

Familiarity with computer science fundamentals and Go

What You'll Learn

A strong understanding of fundamental data structures and algorithms in Go

Course Content

Getting Started

This chapter outlines the course scope, objectives, and prerequisites for the Go programmers.

Who Should Take this Course and Why?

Time Complexity

In this chapter, you will learn about time complexity, analyzing algorithms with notations, runtime functions, and the Master Theorem.

Introduction and Asymptotic Analysis

Big-O, Omega and Theta Notations

Complexity Analysis of Algorithms

Time Complexity Order

Deriving an Algorithm's Runtime Function

Examples Related to Time Complexities

Master Theorem

Master Theorem Examples

Quiz

Arrays

This chapter explores array challenges, covering tasks like searching, rotation, and pattern formation, with solutions for each problem.

Recursive Functions

This chapter covers recursive functions, including factorials, GCD, permutations, and their solutions with complexity analysis.

Recursive Functions

Challenge: Factorial

Solution Review: Factorial

Challenge: Print Base 16 Integers

Solution Review: Print Base 16 Integers

Challenge: Greatest Common Divisor

Solution Review: Greatest Common Divisor

Challenge: Fibonacci Numbers

Solution Review: Fibonacci Numbers

Challenge: All Permutations of an Integer List

Solution Review: All Permutations of an Integer List

Challenge: Tower of Hanoi

Solution Review: Tower of Hanoi

Quiz: Recursion

Approaches to Solve Algorithm Design Problems

In this chapter, you will learn to solve algorithm design problems by understanding constraints, idea generation, complexities, coding, and testing.

Introduction and Constraints

Idea Generation

Complexities, Coding, and Testing

Example and Summary

Data Structures in Go

11 Lessons

This chapter explores data structures in Go, such as stacks, queues, trees, heaps, hash tables, and graphs, along with their applications.

Sorting

17 Lessons

This chapter explains sorting algorithms, their efficiencies, and applications, helping you choose the best method for data.

Stack

36 Lessons

In chapter discovers stacks and their last-in-first-out operations through challenges like sorting, parentheses management, and expression conversion.

Queue

11 Lessons

This chapter covers queues, exploring implementations with various data structures and challenges involving basic operations.

10.

Tree

59 Lessons

This chapter explores tree structures, traversals, and challenges like searching, paths, and node calculations, focusing on hierarchical relationships.

11.

Binary Search Tree

25 Lessons

In this chapter, you will learn about Binary Search Trees, covering creation, insertion, searching, validation, and operations to maintain tree integrity.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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Frequently Asked Questions

What is the goal of this course?

The course aims to teach data structures and algorithms, specifically within the Go programming language.

Who is this course for?

It’s ideal for developers who want to build a strong foundation in Go and understand how to apply data structures and algorithms.

What types of data structures will I learn?

You’ll learn about arrays, stacks, queues, hash tables, and trees.

Does the course cover algorithmic techniques?

It includes greedy algorithms, dynamic programming, and other core techniques.

What is the teaching format of the course?

The course comprises lectures, coding exercises, detailed explanations, and hands-on examples in Go.

Are advanced algorithms covered?

Yes, the course goes beyond basics and covers dynamic programming, greedy algorithms, and more.

Is there any real-world application of the Go algorithms discussed?

Yes, the course applies algorithms to practical, real-world problems, focusing on efficient data processing in Go.