Algorithms in Java :Live problem solving & Design Techniques from Udemy

What's inside

Learning objectives

Algorithm design in computer systems
Algorithm design techniques
How to code a algo in java

Calculate time and space complexity
Dynamic programming, greedy, d&c and much more

Algorithm design in computer systems
Algorithm design techniques
How to code a algo in java
Calculate time and space complexity
Dynamic programming, greedy, d&c and much more

Syllabus

Course Introduction

Course Resources

Introduction to Algorithms

recurrence relation

Complexity Analysis

Section Introduction

Complexity Analysis 1

Complexity Analysis 2

Section Summary

Recurrence Relation

Solving Recurrence Relation

Master's Theorem

Thinking Recursively

Recursion

Identification

Approaching

Problem 01 : FindingSubsequences - Logic

Problem 01 : FindingSubsequences - Live Code Java

Problem 01 : FindingSubsequences - Complexity Analysis

Problem 02: Tower of Hanoi - Logic

Problem 02 : Tower of Hanoi - Live Code Java

Problem 02 : Tower of Hanoi - Complexity Analysis

Problem 03 : 7.Array Product Sum - Logic

Problem 03 : 7.Array Product Sum - Live Code Java

Problem 03 : Array Product Sum - Complexity Analysis

Problem 04 : Binary Subtree - Logic

Problem 04 : Binary Subtree - Live Code Java

Problem 04 : Binary Subtree - Complexity Analysis

Why and Why not Recursion

Types Of Recursion

Tail Recursion

Backtracking

Introduction to Backtracking

Identify Backtracking

Approching the Solution

Problem 01 : Rat In Maze - Logic

Problem 01 : Rat In Maze - Code

Problem 01 : Rat In Maze - Complexity Analysis

Problem 02 : NQueen - Logic

Problem 02 : NQueen - Live Code in Java

Problem 02 : NQueen - Complexity Analysis

Problem 03 : Knights Tour Problem - Logic

Problem 03 : Knights Tour Problem - Live Code in Java

Problem 03 : Knight Tour Problem - Complexity Analysis

Problem 04 : Boggle | Word Search - Logic

Problem 04 : Boggle | Word Search - Live Code in Java

Problem 04 : Boggle | Word Search - Complexity Analysis

Divide and Conquer

Introduction To Divide And Conquer

Identification and Approaching

Problem 01 : MergeSort - Logic

Problem 01 : MergeSort - Live Java Code

Problem 01 : MergeSort - Complexity Analysis

Problem 02 : QuickSort - Logic

Problem 02 : QuickSort - Live Java Code

Problem 02 : QuickSort - Complexity Analysis

Problem 03 : Median Of Medians - Logic

Problem 03 : Median Of Medians - Live Java Code

Greedy Technique

Introduction to Greedy

Identification & Approaching the Solution

Problem 01 : Fractional Knapsack - Logic

Problem 01 : Fractional Knapsack - Live Code Java

Problem 01 : Fractional Knapsack - Complexity Analysis

Problem 02 : IntervalScheduling - Logic

Problem 02 : IntervalScheduling - Live Code Java

Problem 02 : IntervalScheduling - Complexity Analysis

Problem 03 : Huffman Code - Logic

Problem 03 : Huffman Code - Live Code Java

Problem 03 : Huffman Code - Complexity Analysis

Problem 04 : Dijkstra - Logic

Problem 04 : Dijkstra Logic - Live Code

Problem 04 : Dijkstra - Complexity Analysis

Summary

Dynamic Programming

Introduction to Dynamic Programming

Compare DP D&C and Greedy

Approaching the Solution

Example 01 : Staircase Problem Theory & Live Code

Example 01 : Staircase Problem Complexity Analysis

Example 02 - 0/1 Knapsack Theory & Live code

Example 02 - 0/1 Knapsack Complexity Analysis

Example 03 - Coin Change Problem Theory and Code

Example 03 - Coin Change Problem Complexity Analysis

Example 04 : Longest Decreasing Subsequence Explanation And Code

Example 04 : Longest Decreasing Subsequence | Complexity Analysis

Example 05 : Levenshtein problem

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides live coding examples in Java, which is widely used in enterprise environments and backend development

Covers dynamic programming, a core topic that is frequently tested during coding interviews at tech companies

Explores algorithm design techniques like recursion, backtracking, and divide and conquer, which are fundamental concepts in computer science curricula

Includes complexity analysis for each algorithm, which is essential for optimizing code performance in real-world applications

Features live problem-solving sessions, which simulate the experience of coding interviews and competitive programming challenges

Teaches greedy algorithms, which are useful for solving optimization problems in areas like scheduling and resource allocation

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 Algorithms in Java :Live problem solving & Design Techniques with these activities:

Review Data Structures

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Strengthen your understanding of fundamental data structures to better grasp algorithm implementations in Java.

Browse courses on Data Structures

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

Introduction to Algorithms

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Reference a comprehensive algorithms textbook to solidify your understanding of the theoretical concepts.

View Introduction to Algorithms, fourth edition on Amazon

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Read the chapters relevant to the course syllabus.
Work through the examples and exercises in the book.
Compare the book's explanations with the course lectures.

LeetCode Easy Problems in Java

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Practice solving algorithm problems on LeetCode to improve coding skills and problem-solving speed in Java.

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Select a set of LeetCode easy problems focusing on recursion, backtracking, and divide and conquer.
Solve each problem in Java, focusing on clean and efficient code.
Analyze the time and space complexity of your solutions.

Three other activities

Expand to see all activities and additional details

Show all six activities

Create a Blog Post on Dynamic Programming

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Write a blog post explaining dynamic programming concepts and providing Java code examples to reinforce your understanding.

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Choose a specific dynamic programming topic (e.g., memoization, tabulation).
Research the topic and gather relevant information.
Write a clear and concise explanation of the concept.
Provide Java code examples to illustrate the concept.
Publish the blog post on a platform like Medium or personal blog.

Implement a Sorting Algorithm Visualizer

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Build a visualizer for sorting algorithms to deepen understanding of their mechanics and performance characteristics.

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Choose a GUI framework (e.g., Swing, JavaFX) for the visualizer.
Implement several sorting algorithms (e.g., Merge Sort, Quick Sort, Bubble Sort) in Java.
Visualize the sorting process step-by-step, highlighting comparisons and swaps.
Add features to compare the performance of different algorithms.

Cracking the Coding Interview

Show steps

Study a coding interview preparation book to improve your problem-solving skills and prepare for technical interviews.

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

Show steps

Read the chapters on algorithm design techniques.
Solve the problems related to recursion, backtracking, and dynamic programming.
Practice explaining your solutions clearly and concisely.

Career center

Learners who complete Algorithms in Java :Live problem solving & Design Techniques 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 is directly relevant. The core focus of this course is on algorithm design techniques like recursion, backtracking, divide and conquer, greedy algorithms, and dynamic programming, which are essential tools for any algorithm developer. The live problem-solving sessions in Java provide practical experience in implementing these algorithms. Furthermore, the course covers algorithm complexity analysis, which is critical for optimizing algorithm performance. Algorithm developer candidates may find this course directly applicable.

See salaries and explore the career path for Algorithm Developer

Software Engineer

A software engineer designs, develops, and tests software applications. This course can help aspiring software engineers build a foundation in algorithm design techniques, a crucial aspect of efficient and effective software development. Understanding concepts like recursion, backtracking, divide and conquer, greedy algorithms, and dynamic programming, as covered in this course, helps a software engineer choose the best approach for solving complex coding problems. The live problem-solving sessions in Java within this course provide practical experience that mirrors real-world software development challenges. This course will be particularly helpful.

See salaries and explore the career path for Software Engineer

Machine Learning Engineer

A machine learning engineer develops and deploys machine learning models. This course can help machine learning engineers when optimizing algorithms for training and inference. The algorithm design techniques taught in this course, such as divide and conquer and dynamic programming, can be applied to improve the performance of machine learning algorithms. The course's focus on complexity analysis is also helpful, as machine learning engineers need to understand the efficiency of their models. Machine learning engineers may find this course extremely useful.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A data scientist analyzes large datasets to extract meaningful insights and build predictive models. This course helps data scientists when optimizing algorithms for data processing and analysis. The algorithm design techniques in this course, such as dynamic programming and greedy algorithms, can be directly applied to improve the performance of machine learning models and data mining processes. The course's focus on complexity analysis is also helpful, because data scientists need to understand the efficiency of their algorithms when dealing with large datasets. Data scientists may find this course specifically useful.

See salaries and explore the career path for Data Scientist

Research Scientist

A research scientist conducts research to advance knowledge in a particular field, often requiring a doctoral degree. This course helps research scientists who are working on algorithm design or optimization. Understanding and applying algorithm design techniques, such as recursion, backtracking, and dynamic programming, is essential for developing novel algorithms and improving existing ones. The focus on complexity analysis is particularly relevant for evaluating the efficiency of new algorithms. Research scientists may find this course very relevant.

See salaries and explore the career path for Research Scientist

Robotics Engineer

Robotics engineers design, build, and program robots for various applications. This course can help robotics engineers when optimizing robot control algorithms and path planning. The course's coverage of algorithm design techniques, such as greedy algorithms and dynamic programming, can be applied to solve complex robotics problems. The live coding sessions in Java will provide practical experience in implementing these algorithms. This course will be extremely useful for robotics engineers.

See salaries and explore the career path for Robotics Engineer

Data Engineer

Data engineers build and maintain the infrastructure for collecting, storing, and processing data. This course helps data engineers when designing efficient data processing pipelines. The algorithm design techniques covered in this course, such as divide and conquer and dynamic programming, can be applied to optimize data processing algorithms. The course's focus on complexity analysis is also helpful, as data engineers need to ensure the scalability of their data pipelines. Data engineers may find this course quite useful.

See salaries and explore the career path for Data Engineer

Game Developer

Game developers create the logic, artificial intelligence, and systems that drive video games. This course may be useful for game developers, as the algorithm design techniques taught can be applied to optimize game performance and create engaging gameplay mechanics. For example, pathfinding algorithms (like Dijkstra's algorithm taught in the course) are essential for character movement, and dynamic programming can be used for optimizing game resources. Game developers will find the live coding sessions in Java beneficial for implementing these algorithms. Game developers may find this course extremely helpful.

See salaries and explore the career path for Game Developer

Quantitative Analyst

A quantitative analyst, often working in finance, uses mathematical and statistical methods to develop and implement trading strategies and manage risk. This course may be helpful for quantitative analysts, as a strong understanding of algorithm design is crucial for developing efficient and accurate trading models. The course's coverage of dynamic programming and greedy algorithms can be applied to portfolio optimization and risk management. Furthermore, the focus on complexity analysis is important for ensuring the scalability of trading algorithms. A quantitative analyst should find this course to be very useful.

See salaries and explore the career path for Quantitative Analyst

Technical Consultant

A technical consultant provides expert advice and guidance to clients on technology-related issues. This course may be helpful for technical consultants, as it provides a good foundation in algorithm design and problem-solving techniques. The course's coverage of various algorithm design paradigms, such as recursion, backtracking, and dynamic programming, can be applied to a wide range of technical challenges. The live coding sessions in Java will provide practical experience in implementing these algorithms. Technical consultants may find this course useful.

See salaries and explore the career path for Technical Consultant

Systems Architect

A systems architect designs and oversees the implementation of complex computer systems. This course can help systems architects when making informed decisions about system design and optimization. The algorithm design techniques covered in this course, such as divide and conquer and dynamic programming, can be applied to optimize system performance. Systems Architects may find this course really helpful.

See salaries and explore the career path for Systems Architect

Bioinformatician

Bioinformaticians analyze biological data using computational tools and algorithms. This course may be helpful for bioinformaticians as they optimize algorithms for sequence analysis, genomics, and proteomics. The course's coverage of dynamic programming is particularly relevant, as it is a key technique for sequence alignment and phylogenetic analysis. Bioinformaticians might find this course to be quite useful.

See salaries and explore the career path for Bioinformatician

Cryptographer

A cryptographer designs and analyzes encryption algorithms to secure data. This course can help cryptographers understand the principles of algorithm design and complexity analysis. While the course may not directly cover cryptographic algorithms, the fundamental concepts of algorithm design, such as recursion, backtracking, and divide and conquer, are applicable to cryptography. Cryptographers may benefit from the live coding sessions in Java to implement and test cryptographic algorithms. Cryptographers may find this course to be helpful.

See salaries and explore the career path for Cryptographer

Network Engineer

Network engineers design, implement, and manage computer networks. This course may be helpful for network engineers, as a strong understanding of algorithm design is crucial for optimizing network routing and traffic flow. The course's coverage of greedy algorithms, such as Dijkstra's algorithm, is directly applicable to network routing protocols. Network engineers will find the focus on complexity analysis valuable for ensuring network scalability. Network engineers may find this course useful.

See salaries and explore the career path for Network Engineer

Database Administrator

Database administrators are responsible for the performance, integrity, and security of databases. This course may give database administrators insight into query optimization via algorithm design. The course's coverage of algorithm design techniques, such as greedy algorithms and dynamic programming, can be applied to improve database query performance. Database administrators may find this course useful.

See salaries and explore the career path for Database Administrator

Algorithms in Java

Live problem solving & Design Techniques

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Syllabus

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