Algorithms for Coding Interviews

Algorithms for Coding Interviews, available at $59.99, has an average rating of 4.55, with 86 lectures, based on 11 reviews, and has 118 subscribers.

You will learn about Understand the inner working of the most common algorithms used in coding interviews Identify which algorithm is a better fit depending on the problem Improve your problem solving skills Improve your interviewing skills This course is ideal for individuals who are New graduates and experienced engineers preparing for a coding interview It is particularly useful for New graduates and experienced engineers preparing for a coding interview.

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Summary

Title: Algorithms for Coding Interviews

Price: $59.99

Average Rating: 4.55

Number of Lectures: 86

Number of Published Lectures: 86

Number of Curriculum Items: 86

Number of Published Curriculum Objects: 86

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the inner working of the most common algorithms used in coding interviews

Identify which algorithm is a better fit depending on the problem

Improve your problem solving skills

Improve your interviewing skills

Who Should Attend

New graduates and experienced engineers preparing for a coding interview

Target Audiences

New graduates and experienced engineers preparing for a coding interview

What will you learn from the course?

By the end of the course, you will have a better idea of the type of problems asked in coding interviews and how to approach them to implement a viable solution.

You will also learn the most common algorithms used in coding interviews, and more importantly, when to use them.

You will improve your problem-solving skills and interviewing skills.

About the Instructor

David has more than 10 years of experience teaching the Algorithms Design and Analysis course at Universidad Panamericana. He has been involved in the ACM-ICPC programming team of the university as a contestant, coach, and advisor.

David is a Principal Software Engineer with more than 10 years of experience in the industry, having worked at Amazon and Oracle. He also has worked at Karat as a contractor to interview engineers for companies such as Roblox, Indeed, Walmart, Palantir, and others. He has interviewed hundreds of candidates during his trajectory and has participated as a problem setter for questions used in recruitment processes.

He is the founder of dnd-learning, where he creates educational content related to algorithms.  He provides guidance and mentorship for coding interviews and constantly publishes material about algorithms and interviews. He is co-author of the book “Algorithms for Competitive Programming”.

Material

The slides of the course are available for download.

For each coding exercise in the course, it is provided the code with the implementation, and a document explaining the solution.

The coding questions are public to practice, and all of them have automated test cases.

Content of the Course

Introduction

Objectives

Motivation

Tools that will be used during the course

Complexity Analysis

Importance of identifying time and space complexity in an interview

Common types of complexities

Interview tips

Coding exercises

Data Structures I

Linear data structures: Vector, list, queue, and stack

Tree data structures

Interview tips

Coding exercises

Data Structures II

Hashing data structures

Interview tips

Coding exercises

Graphs

Definition

Types of graphs

Paths and cycles

Representation of a graph

Graph traversal

Interview tips

Coding exercises

Dynamic Programming

Definition

How to approach a problem with dynamic programming

Examples of DP problems

Interview tips

Coding exercises

Backtracking

Definition

How to implement a backtracking solution

When is a good idea to use backtracking

Example: Sudoku

Interview tips

Coding exercises

Course Curriculum

Chapter 1: Introduction

Lecture 1: Objectives

Lecture 2: Roadmap

Lecture 3: Omega UP

Chapter 2: Complexity Analysis

Lecture 1: Introduction – Complexity Analysis

Lecture 2: Constant Time Complexity

Lecture 3: Linear Time Complexity

Lecture 4: Polynomial Time Complexity

Lecture 5: Logarithmic Time Complexity

Lecture 6: Exponential Time Complexity

Lecture 7: Interview Tips

Lecture 8: Coding Question – Blame

Lecture 9: Coding Question – Blame (Naive Approach)

Lecture 10: Coding Question – Blame (Naive Approach Code)

Lecture 11: Coding Question – Blame (Optimal Approach)

Lecture 12: Coding Question – Blame (Optimal Approach Code)

Chapter 3: Data Structures I

Lecture 1: Introduction – Data Structures I

Lecture 2: Vector

Lecture 3: List

Lecture 4: Stack

Lecture 5: Queue

Lecture 6: Heap

Lecture 7: Summary

Lecture 8: Coding Question – Top K

Lecture 9: Coding Question – Top K (Approach)

Lecture 10: Coding Question – Top K (Code)

Lecture 11: Coding Question – Latest Averages

Lecture 12: Coding Question – Latest Averages (Approach)

Lecture 13: Coding Question – Latest Averages (Code)

Chapter 4: Data Structures II

Lecture 1: Introduction – Data Structures II

Lecture 2: Unordered Set / Hash Set

Lecture 3: Set / Tree Set

Lecture 4: Unordered Map / Hash Map

Lecture 5: Map / Tree Map

Lecture 6: Interview Tips

Lecture 7: Coding Question – You Complete Me

Lecture 8: Coding Question – You Complete Me (Approach)

Lecture 9: Coding Question – You Complete Me (Code)

Lecture 10: Coding Question – Lufillo and Anagrams

Lecture 11: Coding Question – Lufillo and Anagrams (Approach)

Lecture 12: Coding Question – Lufillo and Anagrams (Code)

Lecture 13: Coding Question – Lufillo and Anagrams (Conclusion)

Chapter 5: Graphs

Lecture 1: Introduction – Graphs

Lecture 2: Definition

Lecture 3: Directed Graphs

Lecture 4: Connected / Disconnected Graphs

Lecture 5: Paths and Cycles

Lecture 6: Adjacency Matrix

Lecture 7: Adjacency List

Lecture 8: Adjacency Matrix VS. Adjacency List

Lecture 9: DFS

Lecture 10: DFS (Code)

Lecture 11: BFS

Lecture 12: BFS (Code)

Lecture 13: Interview Tips

Lecture 14: Coding Question – Up Land

Lecture 15: Coding Question – Up Land (Approach)

Lecture 16: Coding Question – Up Land (Code)

Lecture 17: Coding Question – Dora the Explorer A

Lecture 18: Coding Question – Dora the Explorer A (Approach)

Lecture 19: Coding Question – Dora the Explorer A (Code)

Lecture 20: Coding Question – Dora the Explorer B

Lecture 21: Coding Question – Dora the Explorer B (Approach)

Lecture 22: Coding Question – Dora the Explorer B (Code)

Chapter 6: Dynamic Programming

Lecture 1: Introduction – Dynamic Programming

Lecture 2: Factorial

Lecture 3: Fibonacci

Lecture 4: Domino Tiles

Lecture 5: Lufe Numbers

Lecture 6: Interview Tips

Lecture 7: Coding Question – Flags

Lecture 8: Coding Question – Flags (Approach)

Lecture 9: Coding Question – Flags (Code)

Lecture 10: Coding Question – Stars

Lecture 11: Coding Question – Stars (Approach)

Lecture 12: Coding Question – Stars (Code)

Chapter 7: Backtracking

Lecture 1: Introduction – Backtracking

Lecture 2: Sudoku – Statement

Lecture 3: Sudoku – Approach

Lecture 4: Sudoku – Algorithm

Lecture 5: Sudoku – Code

Lecture 6: Interview Tips

Lecture 7: Coding Question – Super Market

Lecture 8: Coding Question – Super Market (Approach)

Lecture 9: Coding Question – Super Market (Code)

Lecture 10: Coding Question – Super Market (Bitmask)

Chapter 8: Conclusion

Lecture 1: Conclusion

Instructors

David Esparza Alba
Principal Software Engineer

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3 stars: 0 votes

4 stars: 6 votes

5 stars: 5 votes

Frequently Asked Questions

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