Coding Interview Jumpstart: Algorithms and Problem Solving from Udemy

In this course we will together make the first steps in the exciting world of algorithms. If you have never learned anything about algorithms, don’t be afraid or intimidated. Algorithms aren’t as hard as people often consider them to be. I’m convinced that any programmer can master the art of problem solving and algorithms if he or she has the motivation to succeed. In fact, I believe that most of the algorithms can be very easy to understand if they are explained in a clear and simple way.

Considering that I know how hard can the beginning be, because I have been in the same situation once, I have invested my knowledge and experience into this course, to help you guys making the first steps towards mastering algorithms and bringing you one step closer to your dream job.

Section 1: Introduction

In this section, I will introduce you with the material and the structure of the course.

Section 2: Algorithm Analysis

In section 2, we are going to talk about algorithm analysis. For some of you, this will be your first step into the world of algorithms and you will understand why is this such an important topic.

We are going to learn several things:

First, we will learn about the importance of analysis, and why do we have to analyze our solutions.
Then we are going to focus on Asymptotic notation, which is basically a language that allows us to analyze program running time by identifying its behavior as the input size for the algorithm increases.
After that we will focus on a specific type of notation, called Big-O notation, and we will use the Big-O notation to compare algorithms.
Finally we are going to practice these concepts by calculating time and space complexity of some given algorithms.

Section 3: Sorting

Sorting is one of the most common algorithms in the world, if you look all around you can find a lot of applications that are using a sorting algorithm to put elements of some list in a certain order. The elements don’t have to be numbers, they can be strings or maybe some events which are sorted by date.

In this section we will learn several sorting algorithms:

Bubble Sort
Selection Sort
Insertion Sort
Merge Sort
Quick Sort
Counting Sort

For each algorithm we will determine the time and space complexity and compare trade-offs.

At the end of this section you will learn the most important sorting algorithms and you will be able to determine what is the best algorithm to use depending on the type and range of the data which needs to be sorted.

Section 4: Binary Search

This section is dedicated to a fundamental searching algorithm in computer science: binary search.

We start with an explanation about the basic idea of the algorithm and its implementation.

Then we are going to explore some variations of binary search and how we can implement them:

Finding the first occurrence of a target value in the input array
Finding the last occurrence of a target value in the input array
Finding the number of occurrences of a target value in the input array

Section 5: BONUS: Interview Problems

This section is a special bonus section, here I will post detailed interview problems solutions. I encourage you to post some interesting problems in the discussions, and each one-two weeks I will choose new problems and post a new video in this section where we solve those new problems.

I also encourage you to post questions about problems that you don't know how to solve or if you have some bugs in your code and you're not sure why your program isn't working

What's inside

Syllabus

Introduction

Course Introduction

Course Overview

Resources and help

Test your knowledge of algorithm analysis.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Includes a bonus section dedicated to solving coding interview problems, which provides practical experience and insights directly applicable to the interview process

Starts with the fundamentals of algorithm analysis, including asymptotic notation and Big-O notation, which are essential for understanding algorithm efficiency

Covers a range of sorting algorithms, such as Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort, and Counting Sort, providing a comprehensive understanding of sorting techniques

Explores binary search and its variations, including finding the first occurrence, last occurrence, and number of occurrences of a target value, which are common interview questions

Focuses on algorithm analysis, which helps learners understand the importance of analyzing solutions and comparing algorithms based on time and space complexity

Explains algorithms in a clear and simple way, making them easy to understand even for those with no prior experience, which helps build a strong foundation

Reviews summary

Algorithms and interview prep jumpstart

According to learners, this course serves as a solid and accessible starting point for anyone looking to grasp fundamental algorithms and prepare for technical interviews. Students highlight the clear explanations provided in the lectures, finding complex topics like Big-O notation and various sorting algorithms easier to understand. It's often described as being particularly good for beginners in algorithms who have some prior programming experience. Reviewers frequently mention its direct relevance to coding interview preparation, appreciating the inclusion of interview problems. While strong as a jumpstart, some note it provides a foundation and may require further study for more advanced topics.

Best with some coding experience.

"Be aware, it assumes you already know basic programming syntax and data structures."

"This course might not be suitable if you're an absolute beginner to coding entirely."

"If you're completely new to programming, you might find yourself struggling initially."

"Prior coding knowledge is definitely helpful to keep up with the examples."

Concepts explained simply and effectively.

"The lecture explanations were crystal clear, making complex topics like Big-O easy to grasp."

"Instructor does a great job breaking down difficult algorithm concepts simply."

"I finally felt like I understood Big-O notation effectively after taking this course."

"The explanations are very clear and easy to follow, even for complex ideas."

Excellent introduction for beginners in algorithms.

"This course is an amazing way to start my journey into algorithms and data structures."

"Perfect for getting a solid foothold in algorithm basics for someone new."

"As someone who was intimidated by algorithms, I found this a really helpful jumpstart."

"A really great course for beginners to learn algorithms, it explains the concepts clearly."

Prepares well for coding interviews.

"This course genuinely helped me prepare for and feel more confident in my coding interviews."

"The concepts covered are absolutely essential for anyone facing technical interviews."

"The bonus interview problems section was a particularly great touch for practical application."

"Exactly what I needed to kickstart my interview prep focusing on algorithms."

Covers basics, requires further study.

"It's a good introduction but only scratches the surface of more advanced algorithms."

"I felt the need to supplement this course with more in-depth resources afterwards."

"While great for basics, I wish there were more challenging practice problems included."

"Provides a necessary foundation, but not a complete guide to all algorithms."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Coding Interview Jumpstart: Algorithms and Problem Solving with these activities:

Review Data Structures

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Solidify your understanding of fundamental data structures before diving into algorithms. A strong grasp of data structures is essential for efficient algorithm design and implementation.

Browse courses on Arrays

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Review the definitions and properties of common data structures.
Implement basic operations (insertion, deletion, search) for each data structure.
Solve simple problems using each data structure.

Review Cracking the Coding Interview

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Supplement your learning with a widely-used resource for coding interview preparation. This book provides additional practice problems and insights into the interview process.

View Cracking the Coding Interview: 189 Programming... on Amazon

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Read the chapters relevant to the topics covered in the course.
Solve the practice problems at the end of each chapter.
Review the solutions and explanations provided in the book.

LeetCode Easy Problems

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Practice solving easy algorithm problems on platforms like LeetCode. This will help you build confidence and familiarity with common problem-solving patterns.

Show steps

Select a set of easy algorithm problems on LeetCode or a similar platform.
Attempt to solve each problem independently within a reasonable time limit.
Analyze the provided solutions and identify areas for improvement.

Three other activities

Expand to see all activities and additional details

Show all six activities

Create a Blog Post on Big-O Notation

Show steps

Explain Big-O notation in your own words to solidify your understanding. Writing about a topic forces you to think critically and identify any gaps in your knowledge.

Show steps

Research and gather information about Big-O notation.
Outline the key concepts and examples to include in your blog post.
Write a clear and concise explanation of Big-O notation.
Include examples to illustrate different time complexities.
Publish your blog post on a platform like Medium or your personal website.

Implement Sorting Algorithm Visualizer

Show steps

Create a visual representation of different sorting algorithms in action. This project will deepen your understanding of how these algorithms work and their relative performance.

Show steps

Choose a programming language and a suitable GUI library.
Implement the sorting algorithms covered in the course.
Design a user interface to visualize the sorting process step-by-step.
Add features to compare the performance of different algorithms.

Review Introduction to Algorithms

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Deepen your understanding of algorithms with a classic textbook. This book provides a more rigorous and theoretical treatment of the topics covered in the course.

View Introduction to Algorithms, fourth edition on Amazon

Show steps

Read the chapters relevant to the topics covered in the course.
Work through the exercises and problems at the end of each chapter.
Consult the book as a reference when you encounter difficult concepts.

Career center

Learners who complete Coding Interview Jumpstart: Algorithms and Problem Solving will develop knowledge and skills that may be useful to these careers:

Software Engineer

A software engineer designs, develops, tests, and maintains software applications. This course, with its focus on algorithms and problem solving, helps lay groundwork for the analytical and computational thinking required in software engineering. The course's sections on algorithm analysis, sorting algorithms like bubble sort and merge sort, and search algorithms such as binary search, provide a crucial toolkit for optimizing code performance and efficiency. The inclusion of interview problems in the course helps aspiring software engineers prepare for technical interviews, where problem-solving skills are heavily assessed. A software engineer who wishes to strengthen their core algorithm knowledge may find this course especially helpful.

See salaries and explore the career path for Software Engineer

Data Scientist

A data scientist uses statistical methods, machine learning, and data visualization techniques to extract insights and knowledge from data. This course helps build a foundation for data science by introducing the core concepts of algorithm analysis and efficiency. Knowledge of algorithms such as sorting and searching is essential for handling large datasets and developing efficient data processing pipelines. The course's treatment of Big-O notation and time complexity analysis provides a framework for evaluating the scalability of data science algorithms. If you want to improve your ability to analyze data effectively, this course may be a great fit.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A machine learning engineer focuses on developing and deploying machine learning models. This course helps build a foundation for understanding the computational complexity and performance trade-offs inherent in machine learning algorithms. The course's focus on algorithm analysis, sorting, and searching provides a groundwork for optimizing machine learning models and data processing pipelines. A machine learning engineer benefits from understanding how to analyze the performance of algorithms and select optimized solutions for implementation. If you are a machine learning engineer, this course may be helpful.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A data analyst interprets data, analyzes results using statistical techniques, and provides ongoing reports. While a data analyst may not need to implement complex algorithms from scratch, understanding algorithm efficiency is crucial for working with large datasets and optimizing data queries. This course helps develop a foundational understanding of algorithm analysis, including Big-O notation and sorting algorithms. This knowledge enables a data analyst to write more efficient queries and better understand the performance implications of different data analysis techniques. A bonus is that analysts can expect to perform better on technical interviews. Aspiring data analysts may find the course useful in this respect.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

A quantitative analyst, often working in the finance industry, develops and implements mathematical models for pricing derivatives, managing risk, and identifying trading opportunities. This course may help build a foundation for quantitative analysis by teaching the principles of algorithm design and analysis, essential for developing efficient and accurate models. The sorting and searching algorithms covered in the course may be used to optimize data processing and model calibration. The course's coverage of time complexity analysis is directly applicable to evaluating the performance of quantitative models. A quantitative analyst may find this course helpful.

See salaries and explore the career path for Quantitative Analyst

Game Developer

A game developer designs and develops video games. This course helps one build a foundation for performance optimization in game development. The course's focus on algorithms and data structures provides a toolkit for developing efficient game logic, AI, and rendering techniques. Game developers often need to implement custom algorithms for collision detection, pathfinding, and other game-related tasks. One may make use of the sorting and searching algorithms. A game developer wishing to improve their game's performance may find this course useful.

See salaries and explore the career path for Game Developer

Database Administrator

A database administrator is responsible for maintaining, securing, and optimizing databases. While database systems provide built-in optimization tools, a DBA can benefit from understanding algorithm analysis. This course may help enhance a DBA's ability to troubleshoot performance bottlenecks and design efficient database queries. The focus on sorting and searching algorithms can be applied to optimizing database indexing and query execution plans. This course can assist database administrators in understanding the underlying algorithmic principles that govern database performance. Those seeking to work on the back end may find that this course helps them prepare.

See salaries and explore the career path for Database Administrator

Web Developer

A web developer designs, builds, and maintains websites and web applications. This course may help web developers write more efficient code and optimize website performance. The sorting and searching algorithms covered in the course can be applied to tasks such as sorting search results or implementing efficient data retrieval. Understanding time complexity analysis can help web developers identify and address performance bottlenecks in their code. A web developer who wants to improve the performance of their web applications may find this course helpful.

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Mobile Application Developer

A mobile application developer designs, develops, and tests mobile applications for smartphones and tablets. This course helps developers write efficient code that minimizes battery usage and ensures smooth performance on mobile devices. The sorting and searching algorithms covered in the course are applicable to tasks such as managing data in local databases or implementing search functionality within the app. The concepts of time and space complexity can help mobile application developers optimize the performance of their applications on resource-constrained devices. If you are a mobile application developer, this course may be useful.

See salaries and explore the career path for Mobile Application Developer

Firmware Engineer

A firmware engineer develops low-level software that controls hardware devices. Embedded systems often have limited resources, making algorithm efficiency a critical concern. This course may help a firmware engineer optimize code for resource-constrained environments. The course's focus on algorithm analysis, sorting, and searching algorithms can allow for engineers to write code that balances performance with memory usage. The principles of time and space complexity analysis are directly applicable to optimizing firmware code for embedded systems. Those looking to apply to the niche field of firmware engineering may find this course useful.

See salaries and explore the career path for Firmware Engineer

Quality Assurance Engineer

A quality assurance engineer tests software to ensure it meets quality standards. While QA engineers may not write production code, understanding algorithm analysis can help them design effective test cases and identify potential performance issues. This course may help quality assurance engineers develop a deeper understanding of software performance and scalability. By learning about common algorithms and their time complexities, QA engineers can better anticipate potential bottlenecks and design test cases that stress the system in meaningful ways. A QA engineer seeking to improve their testing skills may find this course useful.

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Technical Writer

A technical writer creates documentation for software and hardware products. While technical writers do not directly implement algorithms, understanding the basics can help them communicate complex concepts more clearly. This course may help technical writers develop a foundational understanding of algorithm analysis, which can be valuable when documenting software performance characteristics or explaining the behavior of algorithms. Gaining familiarity with common algorithms and data structures can improve a technical writer's ability to create accurate and informative documentation for technical audiences. If you are a technical writer, this course may prove useful.

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Project Manager

A project manager plans, executes, and closes projects, often in the technology sector. While a project manager does not need to be an expert programmer, a basic understanding of software development principles can improve communication and collaboration with the development team. This course may help a project manager gain insight into the challenges and complexities of software development, particularly around algorithm design and performance optimization. This course provides a foundation for understanding the trade-offs involved in choosing different algorithms and data structures, which can facilitate more informed decision-making during the project lifecycle. Project managers may find this course useful.

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Product Manager

A product manager defines the vision, strategy, and roadmap for a software product. While product managers don't typically write code, understanding the technical aspects of software development can enhance their ability to make informed decisions about product features and priorities. This course may help product managers gain a foundational understanding of algorithm analysis and performance optimization. By learning about common algorithms and their trade-offs, product managers can better assess the technical feasibility and scalability of different product features. If you are a product manager, this course may be useful.

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IT Support Specialist

An information technology support specialist provides technical assistance to computer users. While this role doesn't require in-depth knowledge of algorithms, understanding basic programming concepts can be helpful for troubleshooting software issues. This course may improve an IT support specialist's ability to understand error messages. Familiarity with basic algorithms, may assist IT support specialists in diagnosing and resolving technical problems more effectively. A specialist who wants to broaden their general knowledge, may find this course helpful.

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Coding Interview Jumpstart

Algorithms and Problem Solving

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Algorithms and interview prep jumpstart

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