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Shibaji Paul

This course will help to understand seven most important comparison based sorting algorithms along with the details of how to estimate the complexities for any algorithm. Students will clearly understand how to estimate the best case, average case and worst case complexities for any algorithm along with details analysis of each of the sorting algorithm.

The seven sorting algorithms that you will learn in this course are as follows:

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This course will help to understand seven most important comparison based sorting algorithms along with the details of how to estimate the complexities for any algorithm. Students will clearly understand how to estimate the best case, average case and worst case complexities for any algorithm along with details analysis of each of the sorting algorithm.

The seven sorting algorithms that you will learn in this course are as follows:

  1. Bubble sort

  2. Selection Sort

  3. Insertion Sort

  4. Shell Sort

  5. Quick Sort

  6. Merge Sort

  7. Heap Sort

Students will learn details of heap data structures along with the heap operations like, insertion into heap, heap adjust, heap delete and heapify while learning the heap sort.

Although, sorting utilities can be found in the library of any modern day programming language, however, it is must for a programming student to understand them from scratch as this will help to form strong foundation on algorithm. Also, it is often found that, many questions are asked on sorting algorithms on Job interviews, hence, it will be really fruitful to have a strong hold on this topic.

In the course, I described the logic of each of the seven comparison based sorting algorithms using visual description that is really easy to understand, then I explained the algorithm, analysed them for their performance and finally implemented them using C and Java.

If you are interested of implementing them using in other language you can also do that following the lectures. It will be really easy to do.

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What's inside

Learning objectives

  • How to analyse an algorithm, understanding worst case, best case and average case complexities, use big o, big omega and big theta notations.
  • Seven (7) important comparison based sorting algorithms, #bubble sort, #selection sort, #insertion sort, #shell sort, #quick sort, #merge sort and #heap sort.
  • Students will get to know details of #heap data structures along with heap operations while leaning heap sort.
  • They will experience and understand how to execute program on various input sizes and compare the execution time between different input sizes using graph.

Syllabus

Introduction

Introduction to the course.

To understand how to estimate the efficiency of algorithm for best case, worst case and average case. Clearly understand Big Oh, Big theta and big omega notations and how to calculate them.
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How we can decide about the efficiency or performance of algorithm, what are the yard sticks? How to compare two algorithms on their performance. Concept of RAM model.

This quiz is based on the first lecture of the section - 2.

Understand how we can approach mathematically to estimate for the efficiency of algorithm.

This quiz depends on the second lecture of section 2.

How to find the execution time for any algorithm, the definition of worst case complexity, Big-Oh notation.

You must view the 3rd lecture in section 2 to be able to answer these questions.

Learn how we can calculate the Big-Oh for an algorithm.

Please view the 5th lecture of section - 2 before attempting this quiz.

Calculate Big-Oh of some more algorithms.

Some more examples on calculating Big-Oh.

You must have completed lectures 5, 6 and 7 to attempt this quiz.

Understand best case complexity of an algorithm, the Big Omega notation.

Understand average case complexity and Big Theta notation.

Understand the logic of bubble sort clearly, step-by-step with visual description.

Understand how to analyse bubble sort for worst case behaviour.

Understand how we can improve the bubble sort algorithm for best case behaviour when input is a sorted list.

Starting the implementation of bubble sort using C. Understand all the function prototypes that we will need. You can download the entire code as attached.

Writing the function for bubble sort.

Testing the bubble sort with different randomly generated input list. I will note down the execution time on each of the execution and finally will use these data to plot the n vs f(n) curve.

Check how bubble sort program behaves if you supply a sorted array as input.

Implementing the bubble sort method in a class SortUtility along with some other helper methods which may be required for testing bubble sort. You can download the code attached with this lecture.

Executing the program with various input sizes and plotting the n vs f(n) graph. The code is attached with the previous lecture.

How bubble sort works with sorted input. Download the code attached.

Introduction to selection sort, understand clearly how the selection sort logic works, then understand the algorithm.

Analysis of selection sort for best case and worst case time complexity. Also, deciding about space complexity.

Implementation of selection sort using C programming. Testing the method using various input sizes and finally plotting the size of input vs execution time graph to inspect if the curve is indeed O(n^2).

Implementation of selection sort logic using Java, then testing the method using different input sizes and finally plotting the input size vs execution time graph to check if the curve is indeed O(n^2).

Introduction to insertion sort, then understand the logic of insertion sort, understand the algorithm.

The best case analysis of insertion sort, the best case behaviour happens when the input list is sorted. Understand how the best case time complexity of insertion sort is linear.

Analysis of insertion sort for the worst case, understand how the insertion sort is O(n^2)

Test your knowledge on Insertion Sort with this Quiz.

The C implementation and testing, code attached, I will run the program for various input sizes and will note down the execution time on each occasion and finally I will plot the input size vs execution time graph to check if indeed insertion sort is O(n^2).

Implementation of insertion sort using Java, code attached, executing the program for various input sizes and finally plotting the input size vs execution time graph to check if indeed insertion sort is O(n^2).

Introduction to Shell Sort, then understand the logic of Shell Sort with visual description.

See how we can implement Shell sort using C program, here I will implement shell sort using both default Shell gap and Sedgewick gap. You can download the code from the attached file.

Watch how we can implement Shell sort using Java (code is attached), I will implement Shell sort using both default Shell gap and also by using Sedgewick gap.

Brief introduction to quick sort.

Clearly understand the quick sort procedure with comprehensive visual description.

I will explain the partition logic using example array with visual description.

Understand the partition logic algorithm more clearly in this lecture as I will dry run the algorithm step-by-step with example input.

Understand how we can analyse quick sort for it's average behaviour.

Understand how to estimate the space complexity of quick sort.

See how we can implement and execute quick sort using C, also experience the how the graph - input size vs execution time looks like for different input sizes. You can download the code attached with this lecture.

See how we can implement and execute quick sort using Java. Also watch and experience the input size vs. execution time curve for different input sizes. You can download the code attached with this lecture.

Brief introduction to merge sort.

Understand the merge sort helper - the merge logic. How to merge two sorted halves of an array into one completely sorted array in-place using this merge logic, this will be used in merge sort.

Understand clearly step-by-step with the visual description that how merge sort works.

Details analysis of merge sort to find the worst case complexity and space complexity.

Watch and experience merge sort implementation and execution using C. You can download attached code.

Watch and implement merge sort implementation and execution in Java. You can download the code as attached.

Brief introduction to heap data structure and heap sort.

You must have basic understanding of binary tree to understand heap, so here in this lecture I have briefly explained binary tree data structure.

Understand almost complete binary tree, a special type of binary tree.

Understand heap data structure clearly.

Understand clearly how we can insert an element in heap with visual description.

Understand how we delete an element from heap.

Understand clearly how to adjust a particular index i of heap, when the left child of i is a heap and right child of i is also heap. This procedure is required to adjust the heap after delete operation and also from heapify operation to build a heap from any arbitrary array.

This method is really awesome, it builds a heap from any arbitrary array using the heap adjust operation.

Understand the heap sort.

Watch how we can implement heap sort using C, you can download the code attached with this lecture.

Understand how we can implement heap sort using Java. You can download the code attached with this lecture.

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Provides a strong foundation in algorithm analysis, which is essential for understanding the performance characteristics of different sorting methods
Covers seven comparison-based sorting algorithms, offering a comprehensive overview of fundamental sorting techniques
Includes implementations in both C and Java, allowing learners to compare and contrast the implementations in different languages
Explores heap data structures and operations, which are crucial for understanding and implementing heap sort effectively
Emphasizes the importance of understanding sorting algorithms from scratch, even with readily available library functions
Requires learners to implement sorting algorithms, which may require additional software and tools beyond a standard computer

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Reviews summary

Sorting algorithms and complexity fundamentals

According to learners, this course provides a strong foundation in sorting algorithms. Students particularly appreciate the clear explanations and visual descriptions which make complex topics easy to grasp. The course offers a solid deep dive into complexity analysis, including Big O, Big Omega, and Big Theta notations, which many found highly beneficial for truly understanding algorithm performance. The inclusion of practical code implementations in both Java and C is another frequently praised feature, allowing learners to see the algorithms in action. Many feel the course is well-suited for beginners in algorithms or those needing to refresh their basics, and some note its helpfulness for interview preparation.
Good preparation for technical interviews.
"The course covers common interview topics related to sorting algorithms and complexity."
"I felt much more confident discussing sorting during technical interviews after taking this course."
Implementations provided in Java and C.
"Having the code implemented in both Java and C was extremely useful for understanding the concepts across different languages."
"I liked that I could follow along and practice coding in either Java or C."
"The provided code files were a great resource to experiment with the algorithms."
Excellent for building algorithm basics.
"This course is perfect for beginners who need a solid introduction to sorting algorithms."
"It really helped make my basics strong as the title promises."
"Highly recommended for anyone starting out in algorithms or needing a quick refresh."
Thorough coverage of Big O and performance.
"The section on complexity analysis, Big O, and other notations was very detailed and cleared up a lot of confusion."
"Learning how to properly analyze algorithm performance was a key takeaway for me."
"I appreciate the time spent on explaining worst case, best case, and average case complexities."
Complex logic made easy with visuals.
"The step-by-step visual descriptions were incredibly helpful in understanding the logic of each sorting algorithm."
"Instructor's visual explanations make even the more complex algorithms like Heap Sort clear."
"I finally understood bubble sort thanks to the visual walkthroughs provided in the lectures."

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 Sorting Algorithms using Java & C: Make Your Basics Strong with these activities:
Review Data Structures
Reinforce your understanding of fundamental data structures like heaps and binary trees, which are essential for grasping the concepts behind heap sort and other sorting algorithms.
Browse courses on Heaps
Show steps
  • Review notes on binary trees and heaps.
  • Work through examples of heap insertion and deletion.
  • Implement basic heap operations in Java or C.
Read 'Introduction to Algorithms' by Cormen et al.
Supplement your learning with a comprehensive algorithms textbook that provides in-depth explanations and analysis of sorting algorithms.
Show steps
  • Read the chapters on sorting algorithms.
  • Work through the examples and exercises.
  • Compare the book's explanations with the course material.
Implement Sorting Algorithms from Scratch
Solidify your understanding of sorting algorithms by implementing them from scratch without relying on built-in library functions.
Show steps
  • Choose a sorting algorithm (e.g., bubble sort, insertion sort).
  • Write the code for the algorithm in Java or C.
  • Test the implementation with various input arrays.
  • Debug and optimize the code for performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Read 'Data Structures and Algorithms in Java' by Robert Lafore
Enhance your understanding with a Java-focused book that provides practical examples and code implementations of sorting algorithms.
Show steps
  • Read the chapters on sorting algorithms.
  • Study the Java code examples.
  • Experiment with the code and modify it to test different scenarios.
Visualize Sorting Algorithms
Deepen your understanding by creating visual representations of how different sorting algorithms work, highlighting their steps and comparisons.
Show steps
  • Select a sorting algorithm to visualize.
  • Create a step-by-step diagram or animation of the algorithm's execution.
  • Use different colors or shapes to represent comparisons and swaps.
  • Write a brief explanation of the algorithm's logic.
Benchmark Sorting Algorithms
Gain practical experience by benchmarking the performance of different sorting algorithms with varying input sizes and data distributions.
Show steps
  • Implement several sorting algorithms in Java or C.
  • Generate random input arrays of different sizes.
  • Measure the execution time of each algorithm for each input size.
  • Plot the results on a graph to compare performance.
  • Analyze the results and draw conclusions about the algorithms' efficiency.
Contribute to an Open Source Sorting Library
Apply your knowledge by contributing to an open-source project that implements or optimizes sorting algorithms.
Show steps
  • Find an open-source sorting library on GitHub or GitLab.
  • Identify a bug or a missing feature related to sorting algorithms.
  • Implement a fix or a new feature.
  • Submit a pull request with your changes.
  • Respond to feedback from the project maintainers.

Career center

Learners who complete Sorting Algorithms using Java & C: Make Your Basics Strong will develop knowledge and skills that may be useful to these careers:
Algorithm Developer
Algorithm developers design and implement algorithms for a variety of applications, including search engines, recommendation systems, and data compression tools. This course directly aligns with the requirements of this role by providing in-depth knowledge of various sorting algorithms and their performance characteristics. The course's coverage of Big O, Big Omega, and Big Theta notations equips algorithm developers with the tools needed to analyze and optimize algorithm performance. This course may be particularly beneficial to algorithm developers by detailing the strengths and weaknesses of comparison-based sorting algorithms.
Software Engineer
A software engineer designs, develops, and tests software applications. This course helps build a foundation in essential sorting algorithms, which are fundamental to efficient data management and manipulation within software. Understanding the complexities of algorithms, as taught in the course, is crucial for optimizing software performance. A software engineer can leverage knowledge of algorithms like Bubble Sort, Selection Sort, and Merge Sort to write efficient code. Someone looking to become a software engineer may find this course particularly useful by applying these data structures in real programming scenarios.
Data Scientist
Data scientists analyze large datasets to extract meaningful insights and inform decision-making. Sorting algorithms play a critical role in data preprocessing and analysis. This course helps build a strong understanding of these algorithms, enhancing their ability to efficiently handle and process data. For example, a data scientist may employ sorting techniques to organize data before applying machine learning models. Those interested in becoming data scientists can use this course to learn how to assess the complexities of algorithms like Quick Sort or Heap Sort. This insight may offer efficiency gains in models and pipelines.
Full-Stack Developer
A full stack developer works on both the front-end and back-end of web applications. Sorting algorithms are frequently used in back-end development for tasks such as data retrieval, filtering, and ordering. Students that are hoping to become full stack developers may find this course useful. For instance, if you are working with sorting utilities found in the library of modern day programming language, as this course encourages, the concepts that you've learned in this course will come in handy when you need to debug.
Performance Engineer
Performance engineers are responsible for optimizing the performance of software systems. This course will give you a robust background in sorting algorithms, so you can then apply that knowledge to identify and address performance bottlenecks related to data processing. Understanding the complexities of sorting algorithms allows them to make informed decisions about algorithm selection and implementation. This course may be particularly helpful to performance engineers by providing them with the mathematical tools to analyze the best, worst, and average case complexities for any comparison based sorting algorithm.
Data Engineer
Data engineers build and maintain the infrastructure required for data storage, processing, and analysis. Sorting algorithms are frequently used in data processing pipelines for tasks such as data cleaning, transformation, and aggregation. Someone looking to become a data engineer should consider this course because it provides an understanding of the performance characteristics of different sorting algorithms. For example, understanding heap data structures along with heap operations, as taught in this course, is indispensable to data engineers.
Systems Programmer
Systems programmers develop and maintain the core software components of operating systems and hardware platforms. A deep understanding of algorithms, including sorting algorithms, is crucial for writing efficient and reliable system software. The course's emphasis on algorithm analysis and complexity estimation is particularly relevant for systems programmers. This course provides a strong foundation in algorithm design and implementation, which translates into success as a systems programmer. For example, they might use sorting algorithms to optimize memory management or to improve the performance of file systems.
Machine Learning Engineer
Machine learning engineers develop and deploy machine learning models. Sorting algorithms are often used in data preprocessing steps and in some machine learning algorithms. This course provides a foundation in sorting algorithms, allowing machine learning engineers to optimize data handling and improve model performance. The course's coverage of algorithm analysis and complexity estimation is particularly relevant for machine learning engineers. A candidate may find the implementation of sorting algorithms in Java and C especially useful in their machine learning career.
Database Administrator
A database administrator manages and maintains databases, ensuring data integrity, security, and accessibility. Sorting algorithms are fundamental to database operations, such as indexing and query optimization. This course helps build an understanding of these algorithms providing essential knowledge for improving database performance. Database administrators can apply the concepts learned in the course to optimize query execution plans and improve data retrieval times. This course may be particularly helpful to them. Analyzing complexity, as taught in this course, offers insight into database design, and maintenance.
Embedded Systems Engineer
Embedded systems engineers design and develop software for embedded systems, which are specialized computer systems embedded within other devices, such as appliances, vehicles, and medical equipment. Sorting algorithms are often used in embedded systems for data processing and control applications. This course helps build knowledge of the best, worst, and average case complexities for comparison based sorting algorithms. Analyzing algorithm performance is critical to success as an embedded systems engineer. Concepts like insertion into heap will be particularly applicable.
Software Architect
Software architects design the high-level structure of software systems. A deep understanding of algorithms, including sorting algorithms, is essential for making informed design decisions. This course provides insights into the trade-offs between different sorting algorithms and helps software architects choose the most appropriate algorithms for specific use cases. Being able to analyze those trade-offs in terms of Big O notation, makes this course particularly useful. Consideration of how different sorting algorithms, such as Merge Sort or Quick Sort, affect the larger software architecture is critical.
Robotics Engineer
Robotics engineers design, develop, and test robots and robotic systems. Sorting algorithms can be used in robotics for tasks such as path planning, object recognition, and sensor data processing. This course helps robotics engineers who are looking to improve the efficiency of data handling. A robotics engineer can use the skills learned in this course to implement efficient data structures and algorithms for robot control and perception. Concepts such as execution time between different input sizes is very relevant to the robotics engineering field.
Quantitative Analyst
Quantitative analysts, often working in the finance industry, develop and implement mathematical models for financial analysis and risk management. Sorting algorithms can be useful for organizing and analyzing large datasets of financial data. Students interested in becoming quantitative analysts often pursue advanced degrees. This course may be useful for students who need to preprocess data for analysis. For example, a quantitative analyst might use sorting algorithms to prepare data for statistical modeling or to identify trends in financial markets.
DevOps Engineer
DevOps engineers automate and streamline the software development and deployment process. While not directly related to sorting algorithms, this course may be helpful for devops engineers. This course provides foundational knowledge for analyzing the efficiency of algorithms and is useful when considering how to optimize infrastructure performance. A devops engineer can leverage the skills learned in this course to improve data processing. The consideration of run time and size will be important to a devops engineer.
Game Developer
Game developers create video games for computers, consoles, and mobile devices. Efficient algorithms, including sorting algorithms, are essential for optimizing game performance and creating smooth gameplay experiences. The knowledge gained from that course will help you build a strong grasp of data structures. This course helps game developers to make trade-offs of different sorting algorithms and their impacts on game performance. This course may be particularly useful to game developers. Consideration of algorithms like Merge Sort or Heap Sort is critical.

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Sorting Algorithms using Java & C: Make Your Basics Strong.
Comprehensive textbook on algorithms, covering a wide range of topics including sorting algorithms. It provides detailed explanations, pseudocode, and analysis of various sorting algorithms like bubble sort, insertion sort, merge sort, quicksort, and heapsort. It's a valuable resource for understanding the theoretical foundations and practical implementations of these algorithms, making it a great supplement to the course.
Provides a clear and accessible introduction to data structures and algorithms using Java. It covers sorting algorithms with practical examples and code implementations. This book is helpful in providing background knowledge and is valuable as additional reading. It is commonly used as a textbook at academic institutions.

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