Data Structures and Algorithms: Deep Dive Using Java
So you've worked with the basics of data structures and algorithms in Java (or another OO programming language) but feel like you need a deeper knowledge of how things work. Maybe you have taken other courses on this topic that focus more on teaching how to pass job interview tests (theory) instead of how to make good choices for the programs you develop (implementation).
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Or maybe you are ready to move from a junior programming position to a more senior one and need to get skilled in advanced concepts like data structures, and how to apply them to your own projects.
Whatever the reason, if you are looking for a course that focus on the implementations to give you a complete understanding of how things work, then this is the course for you.
This course goes over the theory of how things work, but only to give you what you need to know to understand the implementation covered.
Complete source code is included and available for you to download.
This is a hands-on course! If you want to try understand things at a deep level, and work on implementations, rather than theory, then again, this is the course for you.
Topics covered:-
Arrays
Linked Lists
Trees
Hashtables
Stacks
Queues
Heaps
Sort algorithms
Search algorithms
The course also spends more time than most other courses of its kind looking at what’s available in the JDK. Students wanting to understand how things work "under the hood" will benefit enormously from this course.
Why learn about data structures and algorithms?
The reality is, the more you learn about data structures and algorithms, the better a programmer you become.
Why?
Because, data structures and algorithms are effectively patterns for solving problems. You want to add as many of them as you can to your skill-set. By doing so, you will find you solve more problems, and use the right tools for the job, in a more elegant way. And you will learn a heap of them in this course.
Why enrolling in this course is the best decision you can make.
Your instructor, Sarah Ettritch has over 25 years’ software development experience and has been working with Java since JDK 1.1. She has spent many years creating tools used by developers, which required a deep knowledge of data structures and algorithms, and is perfectly qualified to teach this course.
Most courses focus on giving you the theory of how things work, so that you can take an interview. Whilst the theory is important, the knowledge of how to implement these data structures and algorithms are of vital importance.
This course goes over the theory of how things work, but only to give you what you need to know to understand the implementation covered. The main focus of the course is to give you a real understanding of how things work under the hood, so that you can apply this to future programming projects.
If you want to actually understand how things work, and be able to take that understanding and apply it to your own programs, then this course is for you.
After completing this course, you will have a solid understanding of data structures and algorithms (both the theory, and the implementation).
The sooner you sign up for this course, the sooner you will have the skills and knowledge you need to increase your job or consulting opportunities. Java developers with key skills and understanding of data structures and algorithms are in high demand and get paid extremely well.
If you are ready for that new job promotion or consulting opportunity, it's time to get started.
- Previous experience with an object-oriented programming language, preferably Java (but any OO language is fine).
Who this course is for:
- Developers who have some knowledge of Java (or another OO language) looking to understand data structures and algorithms at a deep level
What you'll learn:
- Learn the strengths and weaknesses of a variety of data structures, so you can choose the best data structure for your data and applications
- Code an implementation of each data structure, so you understand how they work under the covers
- Learn many of the algorithms commonly used to sort data, so your applications will perform efficiently when sorting large datasets
- Learn what’s available in the JDK for storing and sorting data, so you won’t waste time reinventing the wheel
Watch Online Data Structures and Algorithms: Deep Dive Using Java
| # | Title | Duration |
|---|---|---|
| 1 | Introduction to the Course | 03:43 |
| 2 | JDK8 for Windows | 05:16 |
| 3 | JDK8 for MAC | 02:45 |
| 4 | JDK8 for Linux | 04:21 |
| 5 | IntelliJ for Windows | 09:55 |
| 6 | IntelliJ for MAC | 09:44 |
| 7 | IntelliJ for Linux | 10:23 |
| 8 | Introduction to Data Structures | 02:54 |
| 9 | Introduction to Algorithms | 04:16 |
| 10 | Introduction to Arrays | 00:54 |
| 11 | Big-O Notation | 14:10 |
| 12 | A Quick Review of Arrays in Java | 07:43 |
| 13 | Arrays in Memory | 09:03 |
| 14 | Big-O Values for Array Operations | 11:57 |
| 15 | Introduction to Sort Algorithms | 01:04 |
| 16 | Bubble Sort (Theory) | 08:51 |
| 17 | Bubble Sort (Implementation) | 11:35 |
| 18 | Stable vs. Unstable Sort Algorithms | 04:43 |
| 19 | Selection Sort (Theory) | 06:34 |
| 20 | Selection Sort (Implementation) | 05:03 |
| 21 | Insertion Sort (Theory) | 07:35 |
| 22 | Insertion Sort (Implementation) | 07:13 |
| 23 | Shell Sort (Theory) | 12:55 |
| 24 | Shell Sort (Implementation) | 10:50 |
| 25 | Recursion | 18:11 |
| 26 | Merge Sort (Theory) | 20:41 |
| 27 | Merge Sort (Implementation) | 28:10 |
| 28 | Quick Sort (Theory) | 08:59 |
| 29 | Quick Sort (Implementation) | 12:35 |
| 30 | Counting Sort (Theory) | 07:57 |
| 31 | Counting Sort (Implementation) | 08:36 |
| 32 | Radix Sort (Theory) | 11:13 |
| 33 | Stable Counting Sort (Theory) | 13:19 |
| 34 | Radix Sort (Implementation) | 14:06 |
| 35 | Sorting Arrays Using the JDK | 07:01 |
| 36 | Sort Algorithms Challenge #1 | 01:12 |
| 37 | Sort Algorithms Challenge #1 Solution | 04:04 |
| 38 | Sort Algorithms Challenge #2 | 00:50 |
| 39 | Sort Algorithms Challenge #2 Solution | 11:47 |
| 40 | Sort Algorithms Challenge #3 | 01:13 |
| 41 | Sort Algorithms Challenge #3 Solution | 09:50 |
| 42 | Introduction to Lists | 02:50 |
| 43 | Abstract Data Types | 02:57 |
| 44 | Array Lists | 23:57 |
| 45 | Vectors | 06:49 |
| 46 | Singly Linked Lists (Theory) | 06:43 |
| 47 | Singly Linked Lists (Implementation) | 17:40 |
| 48 | Doubly Linked Lists (Theory) | 09:35 |
| 49 | Doubly Linked Lists (Implementation) | 21:48 |
| 50 | The JDK LinkedList Class | 12:35 |
| 51 | Linked Lists Challenge #1 | 01:37 |
| 52 | Linked Lists Challenge #1 Solution | 09:30 |
| 53 | Linked Lists Challenge #2 | 01:47 |
| 54 | Linked Lists Challenge #2 Solution | 07:32 |
| 55 | Introduction to Stacks | 00:39 |
| 56 | Stacks (Theory) | 07:23 |
| 57 | Stacks Implementation (Array) | 18:32 |
| 58 | Stacks Implementation (Linked List) | 12:00 |
| 59 | Stacks Challenge | 02:04 |
| 60 | Stacks Challenge Solution | 08:26 |
| 61 | Introduction to Queues | 00:27 |
| 62 | Queues (Theory) | 03:39 |
| 63 | Queues (Array Implementation) | 15:50 |
| 64 | Circular Queue Implementation (Part One) | 14:22 |
| 65 | Circular Queue Implementation (Part Two) | 20:32 |
| 66 | Queues and the JDK | 08:21 |
| 67 | Queues Challenge | 01:59 |
| 68 | Queues Challenge Solution | 08:53 |
| 69 | Introduction to Hashtables | 00:24 |
| 70 | Hashtables (Theory) | 08:26 |
| 71 | Hashtables (Array Implementation) | 13:54 |
| 72 | Linear Probing | 21:15 |
| 73 | Linear Probing - Removing Items | 09:40 |
| 74 | Linear Probing - Rehashing | 11:04 |
| 75 | Chaining | 21:25 |
| 76 | Hashtables and the JDK | 16:53 |
| 77 | Bucket Sort (Theory) | 06:36 |
| 78 | Bucket Sort (Implementation) | 09:05 |
| 79 | Hashtables Challenge #1 | 01:55 |
| 80 | Hashtables Challenge #1 Solution | 02:40 |
| 81 | Hashtables Challenge #2 | 01:38 |
| 82 | Hashtables Challenge #2 Solution | 07:56 |
| 83 | Introduction to Search Algorithms | 00:51 |
| 84 | Linear Search Algorithm | 03:52 |
| 85 | Binary Search Algorithm | 07:58 |
| 86 | Binary Search (Implementation) | 13:13 |
| 87 | Introduction to Trees | 00:56 |
| 88 | Trees (Theory) | 11:17 |
| 89 | Binary Search Trees (Theory) | 14:18 |
| 90 | Binary Search Trees (Insertion) | 10:51 |
| 91 | Binary Search Trees (Traversal) | 11:25 |
| 92 | Binary Search Trees (Get, Min, Max) | 10:29 |
| 93 | Binary Search Trees (Delete Cases 1 and 2) | 03:16 |
| 94 | Binary Search Trees (Implement Cases 1 and 2) | 09:12 |
| 95 | Binary Search Trees (Delete Case 3) | 08:52 |
| 96 | Binary Seach Trees (Implement Case 3) | 07:17 |
| 97 | Trees and the JDK | 03:45 |
| 98 | Binary Search Trees Challenge #1 | 01:54 |
| 99 | Binary Search Trees Challenge #1 Solution | 02:50 |
| 100 | Binary Search Trees Challenge #2 | 09:18 |
| 101 | Introduction to Heaps | 00:30 |
| 102 | Heaps (Theory) | 07:51 |
| 103 | Storing Heaps as Arrays | 07:55 |
| 104 | Heaps (Insert) | 09:03 |
| 105 | Heaps (Delete Theory) | 06:56 |
| 106 | Heaps (Delete) | 20:07 |
| 107 | Heaps (Peek) | 05:20 |
| 108 | Priority Queues | 12:01 |
| 109 | Heapsort (Theory) | 04:55 |
| 110 | Heapsort (Implementation) | 07:17 |
| 111 | Sets | 02:52 |
| 112 | Course Wrap-Up | 01:25 |
| 113 | Bonus - Please Watch! | 00:49 |
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