Byte-Sized-Chunks: Sorting and Searching Algorithms from Udemy

Note: This course is a subset of our 20+ hour course 'From 0 to 1: Data Structures & Algorithms' so please don't sign up for both:-)

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
Sorts: Quick, heap, merge, bubble, selection
Search: Binary searching a sorted list

What's inside

Learning objectives

Design and implement software that use sorting and searching algorithms
Understand the trade-offs, complexity and use-cases for different types of sort and search algorithms

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

Design and implement software that use sorting and searching algorithms
Understand the trade-offs, complexity and use-cases for different types of sort and search algorithms
Visualise - really vividly imagine - the common data structures, and the algorithms applied to them

Syllabus

Needle In A Haystack: Sorting and Searching!

You, This Course, and Us!

What is the performance of your code? How do you measure this? What is complexity and what is its relationship with performance?

The Big O notation becomes much clearer when you practice find the complexity of some sample pieces of code. Let's see how many of these you get right!

A sorting algorithm is not just defined by its complexity, there are a whole bunch of other characteristics which can be used to determine which sorting algorithm is the right one for a system. Let's understand what these characteristics are and what are the trade offs we might make.

The simplest and most naive sorting algorithm.

Quick sort is the sort of choice for developers of programming libraries. Let's see what makes it so attractive.

Binary search is a pretty nifty way to search through a sorted list in O(Log N) time. It's also an interview favorite so make sure you understand it well!

in-place.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Uses animations and visualizations, which can help beginners grasp abstract concepts in data structures and algorithms more easily

Covers Big-O notation, which is essential for analyzing the performance and efficiency of algorithms and code

Explores trade-offs between different sorting algorithms, which helps learners choose the right algorithm for a specific use case

Includes quicksort, which is widely used in programming libraries due to its efficiency in many practical scenarios

Teaches binary search, which is a fundamental algorithm and a common topic in technical interviews

Is a subset of a larger course, which may mean that learners will need to take the larger course to gain a more complete understanding

Reviews summary

Visual & concise sorting and searching

According to learners, this course offers a highly visual and easy-to-understand introduction to sorting and searching algorithms. Many found the byte-sized chunks and animations exceptionally helpful for grasping complex concepts. Students particularly praised the clear explanations and engaging delivery. While it provides a solid foundational understanding, some noted it might be less suitable for advanced learners or those seeking deep dives into implementation details or optimization techniques.

Short lessons make learning easy.

"The fact that it's in byte-sized chunks made it very easy to fit into my busy schedule."

"I appreciated the modular structure. I could focus on one algorithm at a time."

"Learning felt less intimidating because each video was relatively short and focused."

"The short lessons helped me stay focused and retain the information better."

Provides a strong starting point.

"This course gave me a really solid foundation for understanding sorting and searching algorithms."

"Perfect for beginners looking to get a handle on the basics of DSA."

"It's a great entry point before tackling more advanced topics or implementations."

"I feel much more confident about these fundamental concepts after taking this course. It's a good starting line."

Concepts are explained simply.

"The explanations were crystal clear and the pace was just right. Not too fast, not too slow."

"Loved the concise format. Each section covered a specific algorithm without unnecessary jargon."

"They broke down Big O and complex sorts into manageable, easy-to-digest pieces."

"Finally, a course that doesn't overcomplicate things. Everything was explained simply and effectively."

Visualizations make algorithms clear.

"The animations and visual explanations really clicked for me; I finally understand how these algorithms work step-by-step."

"Excellent visualizations... the most helpful resource I've found for seeing sorting algorithms in action."

"I'm a visual learner, and this course delivered! The way they showed the data moving was super effective."

"The visual approach made complex ideas surprisingly intuitive."

Not detailed enough for experts.

"As someone with some prior knowledge, I found it a bit too basic. It doesn't go deep into edge cases or complex optimizations."

"It's great for the concept, but doesn't really cover implementation details or coding challenges."

"I wish it had more focus on the practical application and coding these algorithms myself."

"Good overview, but lacks the rigor needed for interview prep or professional level understanding."

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 Byte-Sized-Chunks: Sorting and Searching Algorithms with these activities:

Review Big O Notation

Show steps

Reinforce your understanding of Big O notation, a fundamental concept for analyzing algorithm performance, before diving into the course.

Browse courses on Big O Notation

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Read articles and watch videos explaining Big O notation.
Practice determining the Big O complexity of simple code snippets.
Review common Big O complexities like O(1), O(log n), O(n), O(n log n), and O(n^2).

Review Introduction to Algorithms

Show steps

Deepen your understanding of sorting and searching algorithms by consulting a comprehensive algorithms textbook.

View Introduction to Algorithms, fourth edition on Amazon

Show steps

Read the chapters related to sorting and searching algorithms.
Work through the examples and exercises provided in the book.
Compare the book's explanations with the course material.

Peer Code Review and Algorithm Discussion

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Improve your understanding and coding skills through collaborative code review and discussions with peers.

Show steps

Find a study partner or group.
Share your sorting algorithm implementations with each other.
Review each other's code and provide constructive feedback.
Discuss the trade-offs and complexities of different algorithms.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Implement Sorting Algorithms from Scratch

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Solidify your understanding of sorting algorithms by implementing them yourself without relying on built-in functions.

Show steps

Choose a programming language (e.g., Python, Java, C++).
Implement bubble sort, selection sort, insertion sort, merge sort, and quicksort.
Test your implementations with various input data sets.
Compare the performance of your implementations.

Visualize Sorting Algorithms

Show steps

Enhance your understanding by creating visual representations of how different sorting algorithms work.

Show steps

Choose a visualization tool or library (e.g., matplotlib, Processing).
Create animations or interactive visualizations of bubble sort, selection sort, insertion sort, merge sort, and quicksort.
Share your visualizations with others and get feedback.

Cracking the Coding Interview

Show steps

Prepare for technical interviews by practicing sorting and searching problems from a popular coding interview book.

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

Show steps

Read the sections on sorting and searching algorithms.
Solve the practice problems provided in the book.
Analyze the solutions and explanations provided.

Build a Search Engine

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Apply your knowledge of sorting and searching algorithms to build a practical search engine application.

Show steps

Design the architecture of your search engine.
Implement indexing and searching functionalities using appropriate algorithms.
Optimize the performance of your search engine.
Test and evaluate your search engine with various queries.

Career center

Learners who complete Byte-Sized-Chunks: Sorting and Searching Algorithms will develop knowledge and skills that may be useful to these careers:

Algorithm Developer

An algorithm developer designs and implements algorithms for various applications. This course helps build a strong understanding of different sorting and searching algorithms and their complexities, which are directly relevant to algorithm development. The course's visual approach makes it easier to internalize these concepts and apply them creatively. The focus on Big O notation enables you to analyze and compare the performance of different algorithms. This course's coverage of specific sorting algorithms may be especially useful.

See salaries and explore the career path for Algorithm Developer

Software Engineer

A software engineer designs, develops, and tests software applications. This course helps build a foundation in fundamental algorithms, which are crucial for efficient software development. Understanding Big O notation, as covered in the course, enables you to analyze the performance of code and choose appropriate algorithms for different tasks. This course is particularly useful because of its visual approach to learning, making complex algorithms easier to grasp and apply in real-world projects. This course's coverage of quick sort, heap sort, and merge sort may be especially useful to a software engineer.

See salaries and explore the career path for Software Engineer

Robotics Engineer

A robotics engineer designs, builds, and programs robots. This course helps build a foundation in algorithms essential for robot control, path planning, and sensor data processing. Understanding Big O notation, as taught in the course, is crucial for optimizing robot performance and responsiveness. The visual learning approach offered by the course can be beneficial for quickly implementing and debugging robot control algorithms. This course's coverage of searching algorithms is particularly useful.

See salaries and explore the career path for Robotics Engineer

Web Developer

A web developer builds and maintains websites and web applications. This course is useful by providing developers with a strong understanding of sorting and searching algorithms for handling data efficiently on the front-end and back-end. This includes sorting data for display and searching through data for specific information. The course's visual approach to learning algorithms can be beneficial for understanding how different algorithms affect web application performance and user experience. The lessons on Big O notation are especially valuable.

See salaries and explore the career path for Web Developer

Game Developer

A game developer creates video games. This course helps build a foundation in algorithms, which are critical for game development tasks such as pathfinding, collision detection, and artificial intelligence. Understanding Big O notation, as covered in the course, enables game developers to optimize game performance. The course's visual approach to learning algorithms can be beneficial for quickly implementing and debugging game logic. The course's coverage of searching algorithms is particularly valuable for many game development tasks.

See salaries and explore the career path for Game Developer

Firmware Engineer

A firmware engineer develops software embedded in hardware devices. This course enables firmware engineers to understand and implement efficient sorting and searching algorithms, which are critical for resource-constrained environments. The visual approach to learning algorithms helps in optimizing code for embedded systems. Knowledge of Big O notation, as covered in the course, allows for performance analysis and optimization. The coverage of specific algorithms may be useful for a firmware engineer.

See salaries and explore the career path for Firmware Engineer

Data Engineer

A data engineer builds and maintains data pipelines. This course may be useful by helping data engineers understand the performance and trade-offs of different algorithms when processing data. Big O notation, which the course covers, is essential for evaluating the efficiency of data pipelines. The visual and spatial learning approach facilitates understanding complex data structures and algorithms. This course's coverage of merge sort may be especially relevant.

See salaries and explore the career path for Data Engineer

Technical Lead

A technical lead manages a team of developers and guides technical decisions. This course may be useful by bolstering their understanding of algorithm complexity and trade-offs. The course's lessons on Big O notation can assist when evaluating different approaches for efficiency. The visual approach to learning benefits the technical lead's ability to communicate these concepts effectively to their team. The course's coverage of searching algorithms may be useful.

See salaries and explore the career path for Technical Lead

Systems Architect

A systems architect designs and oversees the implementation of complex computer systems. A systems architect needs to understand algorithmic complexity to ensure that systems are scalable and efficient. This course may be useful by providing a foundation in Big O notation and algorithm analysis, helping to make informed decisions about system design. The visual learning approach can assist in understanding how different algorithms interact within a larger system. This course's coverage of searching and sorting algorithms may be useful.

See salaries and explore the career path for Systems Architect

Data Scientist

A data scientist analyzes large datasets to extract meaningful insights. This course may be useful for data scientists by providing them with a solid understanding of sorting and searching algorithms, which are essential for data manipulation and analysis. Knowledge of Big O notation, as taught in the course, helps data scientists optimize their code for performance. The course's focus on visualizing algorithms can assist in understanding how different approaches impact data processing efficiency. This course's discussions of specific algorithms may be useful to a data scientist.

See salaries and explore the career path for Data Scientist

Database Administrator

A database administrator manages and maintains databases. This course may be useful by providing a fundamental understanding of sorting and searching algorithms, which are essential for optimizing database queries and data retrieval. Knowledge of Big O notation, as taught in the course, helps optimize database performance. The course's visual approach can assist in understanding how different algorithms interact with large datasets. This course's discussions of binary search may be especially useful.

See salaries and explore the career path for Database Administrator

Machine Learning Engineer

A machine learning engineer develops and deploys machine learning models. While this role often requires an advanced degree, this course may be useful by introducing fundamental sorting and searching algorithms. Though machine learning focuses on statistical methods, understanding algorithm complexity, as taught in the course, helps optimize data processing pipelines. The visual learning approach can assist in understanding the underlying mechanics of data manipulation within machine learning workflows. This course's coverage of Big O notation may be especially useful.

See salaries and explore the career path for Machine Learning Engineer

Research Scientist

A research scientist, often requiring an advanced degree, conducts research in various fields. This course may be useful by providing a foundation in algorithmic thinking and complexity analysis. Depending on the area of research, understanding algorithm performance, as covered in the course, may help optimize data processing and simulations. The visual approach to learning facilitates grasping underlying computational principles. This is especially true if the research involves processing large datasets. This course's coverage of Big O notation may be useful.

See salaries and explore the career path for Research Scientist

Quantitative Analyst

A quantitative analyst, often requiring an advanced degree, develops and implements mathematical models for financial analysis. This course may be useful by building a foundation in algorithmic thinking and complexity analysis. While quantitative finance relies on more advanced mathematical techniques, understanding algorithm performance, as covered in the course, helps optimize trading strategies and risk management systems. The visual approach to learning complex concepts facilitates grasping underlying computational principles. This course's coverage of Big O notation is helpful in this line of work.

See salaries and explore the career path for Quantitative Analyst

Bioinformatician

A bioinformatician, often requiring an advanced degree, analyzes biological data using computational tools. This course may be useful by building a foundation in sorting and searching algorithms, which are essential for processing genomic data and identifying patterns. While bioinformatics involves specialized algorithms, understanding Big O notation, as taught in the course, helps optimize data processing pipelines. The visual learning approach can assist in understanding the underlying mechanics of data manipulation. This course's coverage of searching algorithms may be useful.

See salaries and explore the career path for Bioinformatician

Byte-Sized-Chunks

Sorting and Searching Algorithms

What's inside

Learning objectives

Syllabus

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Reviews summary

Visual & concise sorting and searching

Activities

Career center

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