From 0 to 1- Data Structures Algorithms in Java

From 0 to 1: Data Structures & Algorithms in Java, available at $64.99, has an average rating of 4.55, with 67 lectures, based on 1354 reviews, and has 12293 subscribers.

You will learn about Visualise – really vividly imagine – the common data structures, and the algorithms applied to them Pick the correct tool for the job – correctly identify which data structure or algorithm makes sense in a particular situation Calculate the time and space complexity of code – really understand the nuances of the performance aspects of code This course is ideal for individuals who are Yep! Computer Science and Engineering grads who are looking to really visualise data structures, and internalise how they work or Yep! Experienced software engineers who are looking to refresh important fundamental concepts It is particularly useful for Yep! Computer Science and Engineering grads who are looking to really visualise data structures, and internalise how they work or Yep! Experienced software engineers who are looking to refresh important fundamental concepts.

Enroll now: From 0 to 1: Data Structures & Algorithms in Java

Summary

Title: From 0 to 1: Data Structures & Algorithms in Java

Price: $64.99

Average Rating: 4.55

Number of Lectures: 67

Number of Published Lectures: 67

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 67

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Visualise – really vividly imagine – the common data structures, and the algorithms applied to them

Pick the correct tool for the job – correctly identify which data structure or algorithm makes sense in a particular situation

Calculate the time and space complexity of code – really understand the nuances of the performance aspects of code

Who Should Attend

Yep! Computer Science and Engineering grads who are looking to really visualise data structures, and internalise how they work

Yep! Experienced software engineers who are looking to refresh important fundamental concepts

Target Audiences

Yep! Computer Science and Engineering grads who are looking to really visualise data structures, and internalise how they work

Yep! Experienced software engineers who are looking to refresh important fundamental concepts

This is an animated, visual and spatial way to learn data structures and algorithms

Our brains process different types of information differently – evolutionarily we are wired to absorb information best when it is visual and spatial i.e. when we can close our eyes and see it

More than most other concepts, Data Structures and Algorithms are best learnt visually.These are incredibly easy to learn visually, very hard to understand most other ways

This course has been put together by a team with tons of everyday experience in thinking about these concepts and using them at work at Google, Microsoft and Flipkart

What’s Covered:

Big-O notation and complexity

Stacks

Queues

Trees

Heaps

Graphs and Graph Algorithms

Linked lists

Sorting

Searching

Course Curriculum

Chapter 1: What this course is about

Lecture 1: You, This course and Us

Chapter 2: Data Structures And Algorithms – A Symbiotic Relationship

Lecture 1: Why are Data Structures And Algorithms important?

Chapter 3: Complexity Analysis and the Big-O Notation

Lecture 1: Performance and Complexity

Lecture 2: The Big-O Notation

Lecture 3: What is the complexity of these pieces of code?

Chapter 4: Linked Lists

Lecture 1: The Linked List – The Most Basic Of All Data Structures

Lecture 2: Linked List Problems

Lecture 3: Linked Lists vs Arrays

Chapter 5: Stacks And Queues

Lecture 1: Meet The Stack – Simple But Powerful

Lecture 2: Building A Stack Using Java

Lecture 3: Match Parenthesis To Check A Well Formed Expression

Lecture 4: Find The Minimum Element In A Stack In Constant Time

Lecture 5: Meet The Queue – A Familiar Sight In Everyday Life

Lecture 6: The Circular Queue – Tricky But Fast

Lecture 7: Build A Queue With Two Stacks

Chapter 6: Sorting and Searching

Lecture 1: Sorting Trade-Offs

Lecture 2: Selection Sort

Lecture 3: Bubble Sort

Lecture 4: Insertion Sort

Lecture 5: Shell Sort

Lecture 6: Merge Sort

Lecture 7: Quick Sort

Lecture 8: Binary Search – search quickly through a sorted list

Chapter 7: Binary Trees

Lecture 1: Meet The Binary Tree – A Hierarchical Data Structure

Lecture 2: Breadth First Traversal

Lecture 3: Depth First – Pre-OrderTraversal

Lecture 4: Depth First – In-Order and Post-Order Traversal

Chapter 8: Binary Search Trees

Lecture 1: The Binary Search Tree – an introduction

Lecture 2: Insertion and Lookup in a Binary Search Tree

Chapter 9: Binary Tree Problems

Lecture 1: Minimum Value, Maximum Depth And Mirror

Lecture 2: Count Trees, Print Range and Is BST

Chapter 10: Heaps

Lecture 1: The Heap Is Just The Best Way to Implement a Priority Queue

Lecture 2: Meet The Binary Heap – Its A Tree At Heart

Lecture 3: The Binary Heap – Logically A Tree Really An Array

Lecture 4: The Binary Heap – Making It Real With Code

Lecture 5: Heapify!

Lecture 6: Insert And Remove From A Heap

Chapter 11: Revisiting Sorting – The Heap Sort

Lecture 1: Heap Sort Phase I – Heapify

Lecture 2: Heap Sort Phase II – The Actual Sort

Chapter 12: Heap Problems

Lecture 1: Maximum Element In A Minimum Heap and K Largest Elements In A Stream

Chapter 13: Graphs

Lecture 1: Introducing The Graph

Lecture 2: Types Of Graphs

Lecture 3: The Directed And Undirected Graph

Lecture 4: Representing A Graph In Code

Lecture 5: Graph Using An Adjacency Matrix

Lecture 6: Graph Using An Adjacency List And Adjacency Set

Lecture 7: Comparison Of Graph Representations

Lecture 8: Graph Traversal – Depth First And Breadth First

Chapter 14: Graph Algorithms

Lecture 1: Topological Sort In A Graph

Lecture 2: Implementation Of Topological Sort

Chapter 15: Shortest Path Algorithms

Lecture 1: Introduction To Shortest Path In An Unweighted Graph – The Distance Table

Lecture 2: The Shortest Path Algorithm Visualized

Lecture 3: Implementation Of The Shortest Path In An Unweighted Graph

Lecture 4: Introduction To The Weighted Graph

Lecture 5: Shortest Path In A Weighted Graph – A Greedy Algorithm

Lecture 6: Dijkstras Algorithm Visualized

Lecture 7: Implementation Of Dijkstras Algorithm

Lecture 8: Introduction To The Bellman Ford Algorithm

Lecture 9: The Bellman Ford Algorithm Visualized

Lecture 10: Dealing With Negative Cycles In The Bellman Ford Algorithm

Lecture 11: Implementation Of The Bellman Ford Algorithm

Chapter 16: Spanning Tree Algorithms

Lecture 1: Prims Algorithm For a Minimal Spanning Tree

Lecture 2: Use Cases And Implementation Of Prims Algorithm

Lecture 3: Kruskals Algorithm For a Minimal Spanning Tree

Lecture 4: Implementation Of Kruskals Algorithm

Chapter 17: Graph Problems

Lecture 1: Design A Course Schedule Considering Pre-reqs For Courses

Lecture 2: Find The Shortest Path In A Weighted Graphs – Fewer Edges Better

Instructors

Loony Corn
An ex-Google, Stanford and Flipkart team

Rating Distribution

1 stars: 30 votes

2 stars: 48 votes

3 stars: 153 votes

4 stars: 452 votes

5 stars: 671 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!