Data Structures in Java - Part I (+INTERVIEW QUESTIONS)
What you'll learn
- grasp the fundamentals of algorithms and data structures
- detect non-optimal code snippets
- learn about arrays and linked lists
- learn about stacks and queues
- learn about binary search trees
- learn about balanced binary search trees such as AVL trees or red-black trees
- learn about priority queues and heaps
- learn about B-trees and external memory
- learn about hashing and hash tables
Requirements
- Basic Java (loops and some OOP)
Description
This course is about data structures and algorithms. We are going to implement the problems in Java. The course takes approximately 14 hours to complete. It is highly recommended to type out these data structures several times on your own in order to get a good grasp of it.
Section 1:
data structures and abstract data types
Section 2 - Arrays
what are arrays
what is random access and how to indexes
Section 3 - Linked Lists
linked lists and doubly linked lists
linked list related interview questions
Section 2 - Stacks and Queues:
what are stacks and queues
heap memory and stack memory
visualizing stack memory
Section 3 - Binary Search Trees (BSTs):
what are tree data structures?
how to achieve O(logN) logarithmic running time?
binary search trees
Section 4 - AVL Trees
what is the problem with binary search trees?
balanced search trees: AVL trees
rotations
Section 5 - Red-Black Trees
what are red-black trees?
what is recovering operation?
comparing AVL trees and red-black trees
Section 6 - Splay Trees
splay trees and caches
achieve O(1) running time for getting the recently visited item
Section 7 - Heaps and Priority Queues
what are priority queues?
what is heap data structure?
how to do sorting in O(NlogN) with heaps?
Section 8 - B-Trees
external memory and the main memory (RAM)
B-trees and their applications in memory
B* trees and B+ trees
Section 9 - Hashing and HashMaps:
what are hashing and hashtables (hashmaps)
what are hash-functions
how to achieve O(1) running time complexity
Section 10 (BONUS):
what is LRU cache
LRU cache implementation
Section 11 (BONUS):
Fenwick trees (binary indexed trees)
binary indexed tree implementation
In each chapter you will learn about the theoretical background of each algorithm or data structure, then we are going to write the code on a step by step basis in Eclipse, Java.
Most of the advanced algorithms relies heavily on these topics so it is definitely worth understanding the basics. These principles can be used in several fields: in investment banking, artificial intelligence or electronic trading algorithms on the stock market.
Thanks for joining the course, let's get started!
Who this course is for:
- This course is meant for everyone from scientists to software developers who want to get closer to algorithmic thinking in the main
Course content
- Preview02:15
- 00:11Complexity theory basics
Instructor
![Holczer Balazs](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.5 Instructor Rating
- 25,386 Reviews
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Hi!
My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model.
Take a look at my website if you are interested in these topics!