JAVA - Data Structures and Algorithms

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Course Overview

Overview

Welcome to the Java Data Structures and Algorithms Masterclass, the most modern, and the most complete Data Structures and Algorithms in Java course on the internet.

Takeaway from this course - 

  • Learn basic algorithmic techniques such as greedy algorithms, binary search, sorting and dynamic programming to solve programming challenges.
  • Learn the strengths and weaknesses of a variety of data structures, so you can choose the best data structure for your data and applications
  • Learn many of the algorithms commonly used to sort data, so your applications will perform efficiently when sorting large datasets
  • Learn how to apply graph and string algorithms to solve real-world challenges: finding shortest paths on huge maps and assembling genomes from millions of pieces.

Curriculum

Contents - 

Java Programming Language Features

  • Basics of Java Programming - Expressions, Variables and Printing Output
  • Java Operators - Java Assignment Operator, Relational and Logical Operators, Short Circuit Operators
  • Java Conditionals and If Statement
  • Methods - Parameters, Arguments and Return Values
  • An Overview Of Java Platform - java, javac, bytecode, JVM and Platform Independence - JDK vs JRE vs JVM
  • Object Oriented Programming - Class, Object, State and Behavior
  • Basics of OOPS - Encapsulation, Abstraction, Inheritance and Polymorphism
  • Basics about Java Data Types - Casting, Operators and More
  • Java Built in Classes - BigDecimal, String, Java Wrapper Classes
  • Conditionals with Java - If Else Statement, Nested If Else, Java Switch Statement, Java Ternary Operator
  • Loops - For Loop, While Loop in Java, Do While Loop, Break and Continue
  • Immutablity of Java Wrapper Classes, String and BigDecimal
  • Java Dates - Introduction to LocalDate, LocalTime and LocalDateTime
  • Java Array and ArrayList - Java String Arrays, Arrays of Objects, Primitive Data Types, toString and Exceptions
  • Introduction to Variable Arguments
  • Basics of Designing a Class - Class, Object, State and Behavior. Deciding State and Constructors.
  • Understanding Object Composition and Inheritance
  • Java Abstract Class and Interfaces. Introduction to Polymorphism.
  • Java Collections - List Interface(ArrayList, LinkedList and Vector), Set Interface (HashSet, LinkedHashSet and TreeSet), Queue Interface (PriorityQueue) and Map Interface (HashMap, HashTable, LinkedHashMap and TreeMap() - Compare, Contrast and Choose
  • Generics - Why do we need Generics? Restrictions with extends and Generic Methods, WildCards - Upper Bound and Lower Bound.
  • Functional Programming - Lambda Expression, Stream and Operations on a Stream (Intermediate Operations - Sort, Distinct, Filter, Map and Terminal Operations - max, min, collect to List), Functional Interfaces - Predicate Interface,Consumer Interface, Function Inteface for Mapping, Method References - static and instance methods
  • Introduction to Threads and MultiThreading - Need for Threads
  • Implementing Threads - Extending Thread Class and Implementing Runnable Interface
  • States of a Thread and Communication between Threads
  • Introduction to Executor Service - Customizing number of Active Threads. Returning a Future, invokeAll and invokeAny
  • Introduction to Exception Handling - Your Thought Process during Exception Handling. try, catch and finally. Exception Hierarchy - Checked Exceptions vs Unchecked Exceptions. Throwing an Exception. Creating and Throwing a Custom Exception - CurrenciesDoNotMatchException. Try with Resources - New Feature in Java 7.
  • List files and folders in Directory with Files list method, File walk method and find methods. Read and write from a File.

Java - Data Structure and Algorithms 

1. Introduction
1.1 Variables
1.2 Data Types
1.3 Data Structure
1.4 Abstract Data Types (ADTs)
1.5 What is an Algorithm?
1.6 Why the Analysis of Algorithms?
1.7 Goal of the Analysis of Algorithms
1.8 What is Running Time Analysis?
1.9 How to Compare Algorithms
1.10 What is Rate of Growth?
1.11 Commonly used Rates of Growth
1.12 Types of Analysis
1.13 Asymptotic Notation
1.14 Big-O Notation
1.15 Omega-Ω Notation
1.16 Theta-Θ Notation
1.17 Important Notes
1.18 Why is it called Asymptotic Analysis?
1.19 Guidelines for Asymptotic Analysis
1.20 Properties of Notations
1.21 Commonly used Logarithms and Summations
1.22 Master Theorem for Divide and Conquer
1.23 Divide and Conquer Master Theorem: Problems & Solutions
1.24 Master Theorem for Subtract and Conquer Recurrences
1.25 Variant of Subtraction and Conquer Master Theorem
1.26 Method of Guessing and Confirming
1.27 Amortized Analysis
1.28 Algorithms Analysis: Problems & Solutions

2. Recursion and Backtracking
2.1 Introduction
2.2 What is Recursion?
2.3 Why Recursion?
2.4 Format of a Recursive Function
2.5 Recursion and Memory (Visualization)
2.6 Recursion versus Iteration
2.7 Notes on Recursion
2.8 Example Algorithms of Recursion
2.9 Recursion: Problems & Solutions
2.10 What is Backtracking?
2.11 Example Algorithms of Backtracking
2.12 Backtracking: Problems & Solutions

3. Linked Lists
3.1 What is a Linked List?
3.2 Linked Lists ADT
3.3 Why Linked Lists?
3.4 Arrays Overview
3.5 Comparison of Linked Lists with Arrays & Dynamic Arrays
3.6 Singly Linked Lists
3.7 Doubly Linked Lists
3.8 Circular Linked Lists
3.9 A Memory-efficient Doubly Linked List
3.10 Unrolled Linked Lists
3.11 Skip Lists
3.12 Linked Lists: Problems & Solutions

4. Stacks
4.1 What is a Stack?
4.2 How Stacks are used
4.3 Stack ADT
4.4 Exceptions
4.5 Applications
4.6 Implementation
4.7 Comparison of Implementations
4.8 Stacks: Problems & Solutions

5. Queues
5.1 What is a Queue?
5.2 How are Queues Used
5.3 Queue ADT
5.4 Exceptions
5.5 Applications
5.6 Implementation
5.7 Queues: Problems & Solutions

6. Trees
6.1 What is a Tree?
6.2 Glossary
6.3 Binary Trees
6.4 Binary Tree Traversals
6.5 Generic Trees (N-ary Trees)
6.6 Threaded Binary Tree Traversals (Stack or Queue-less Traversals)
6.7 Expression Trees
6.8 XOR Trees
6.9 Binary Search Trees (BSTs)
6.10 Balanced Binary Search Trees
6.11 AVL (Adelson-Velskii and Landis) Trees
6.12 Other Variations on Trees

7. Priority Queues and Heaps
7.1 What is a Priority Queue?
7.2 Priority Queue ADT
7.3 Priority Queue Applications
7.4 Priority Queue Implementations
7.5 Heaps and Binary Heaps
7.6 Binary Heaps
7.7 Priority Queues [Heaps]: Problems & Solutions

8. Disjoint Sets ADT
8.1 Introduction
8.2 Equivalence Relations and Equivalence Classes
8.3 Disjoint Sets ADT
8.4 Applications
8.5 Tradeoffs in Implementing Disjoint Sets ADT
8.6 Fast UNION Implementation (Slow FIND)
8.7 Fast UNION Implementations (Quick FIND)
8.8 Path Compression
8.9 Summary
8.10 Disjoint Sets: Problems & Solutions

9. Graph Algorithms
9.1 Introduction
9.2 Glossary
9.3 Applications of Graphs
9.4 Graph Representation
9.5 Graph Traversals
9.6 Topological Sort
9.7 Shortest Path Algorithms
9.8 Minimal Spanning Tree
9.9 Graph Algorithms: Problems & Solutions

10. Sorting
10.1 What is Sorting?
10.2 Why is Sorting Necessary?
10.3 Classification of Sorting Algorithms
10.4 Other Classifications
10.5 Bubble Sort
10.6 Selection Sort
10.7 Insertion Sort
10.8 Shell Sort
10.9 Merge Sort
10.10 Heap Sort
10.11 Quick Sort
10.12 Tree Sort
10.13 Comparison of Sorting Algorithms
10.14 Linear Sorting Algorithms
10.15 Counting Sort
10.16 Bucket Sort (or Bin Sort)
10.17 Radix Sort
10.18 Topological Sort
10.19 External Sorting
10.20 Sorting: Problems & Solutions

11. Searching
11.1 What is Searching?
11.2 Why do we need Searching?
11.3 Types of Searching
11.4 Unordered Linear Search
11.5 Sorted/Ordered Linear Search
11.6 Binary Search
11.7 Interpolation Search
11.8 Comparing Basic Searching Algorithms
11.9 Symbol Tables and Hashing
11.10 String Searching Algorithms
11.11 Searching: Problems & Solutions
12. Selection Algorithms [Medians]
12.1 What are Selection Algorithms?
12.2 Selection by Sorting
12.3 Partition-based Selection Algorithm
12.4 Linear Selection Algorithm - Median of Medians Algorithm
12.5 Finding the K Smallest Elements in Sorted Order
12.6 Selection Algorithms: Problems & Solutions

13. Symbol Tables
13.1 Introduction
13.2 What are Symbol Tables?
13.3 Symbol Table Implementations
13.4 Comparison Table of Symbols for Implementations

14. Hashing
14.1 What is Hashing?
14.2 Why Hashing?
14.3 HashTable ADT
14.4 Understanding Hashing
14.5 Components of Hashing
14.6 Hash Table
14.7 Hash Function
14.8 Load Factor
14.9 Collisions
14.10 Collision Resolution Techniques
14.11 Separate Chaining
14.12 Open Addressing
14.13 Comparison of Collision Resolution Techniques
14.14 How Hashing Gets O(1) Complexity?
14.15 Hashing Techniques
14.16 Problems for which Hash Tables are not suitable
14.17 Bloom Filters
14.18 Hashing: Problems & Solutions

15. String Algorithms
15.1 Introduction
15.2 String Matching Algorithms
15.3 Brute Force Method
15.4 Rabin-Karp String Matching Algorithm
15.5 String Matching with Finite Automata
15.6 KMP Algorithm
15.7 Boyer-Moore Algorithm
15.8 Data Structures for Storing Strings
15.9 Hash Tables for Strings
15.10 Binary Search Trees for Strings
15.11 Tries
15.12 Ternary Search Trees
15.13 Comparing BSTs, Tries and TSTs
15.14 Suffix Trees
15.15 String Algorithms: Problems & Solutions

16. Algorithms Design Techniques
16.1 Introduction
16.2 Classification
16.3 Classification by Implementation Method
16.4 Classification by Design Method
16.5 Other Classifications

17. Greedy Algorithms
17.1 Introduction
17.2 Greedy Strategy
17.3 Elements of Greedy Algorithms
17.4 Does Greedy Always Work?
17.5 Advantages and Disadvantages of Greedy Method
17.6 Greedy Applications
17.7 Understanding Greedy Technique
17.8 Greedy Algorithms: Problems & Solutions

18. Divide and Conquer Algorithms
18.1 Introduction
18.2 What is Divide and Conquer Strategy?
18.3 Does Divide and Conquer Always Work?
18.4 Divide and Conquer Visualization
18.5 Understanding Divide and Conquer
18.6 Advantages of Divide and Conquer
18.7 Disadvantages of Divide and Conquer
18.8 Master Theorem
18.9 Divide and Conquer Applications
18.10 Divide and Conquer: Problems & Solutions

19. Dynamic Programming
19.1 Introduction
19.2 What is Dynamic Programming Strategy?
19.3 Properties of Dynamic Programming Strategy
19.4 Can Dynamic Programming Solve All Problems?
19.5 Dynamic Programming Approaches
19.6 Examples of Dynamic Programming Algorithms
19.7 Understanding Dynamic Programming
19.8 Dynamic Programming: Problems & Solutions

20. Complexity Classes
20.1 Introduction
20.2 Polynomial/Exponential Time
20.3 What is a Decision Problem?
20.4 Decision Procedure
20.5 What is a Complexity Class?
20.6 Types of Complexity Classes
20.7 Reductions
20.8 Complexity Classes: Problems & Solutions
21. Miscellaneous Concepts
21.1 Introduction
21.2 Hacks on Bitwise Programming
21.3 Other Programming Questions
 

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JAVA - Data Structures and Algorithms

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