The post walks you through all about Data Structures and Algorithms Tutorial in Java, from basics to advance. If you want to become a great developer, it is important to learn data structures and algorithms then you are able to build scalable systems. Let’s study and grow together.
Data Structures and Algorithms Tutorial in Java
Introduction
What is an algorithm?
In general, an algorithm is a set of well-defined instructions in sequence to solve the problem or it is an essential solution.
A good algorithm should have the qualities at least below:
- Effective: is the algorithm correct? It means that an algorithm should be most effective among many different ways to solve a problem.
- Stable: does the algorithm produce solutions in a manner that is consistent. This may be that the algorithm follows a similar execution path in a program for all instances so that the time taken for a given input (of a given size) does not differ greatly.
- Simple and/or elegant: makes for an easier to understand an algorithm and tends to be easier to implement and use. Input and output should be defined precisely.
For example, an algorithm to solve the problem of factorials look like this:
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Initialize fact = 1 For every value v in range 1 to n: Multiply the fact by v fact contains the factorial of n |
Why you should learn algorithms?
- Time is precious: a good algorithm makes the program return result as soon as possible. Even your system is a large size the algorithm must handle the problem by sequences of tasks in the critical path, which means in the shortest duration.
- Memory is expensive: Memory is not always available. The developers usually face memory issues while dealing with code because it requires them to store or produce a lot of data, it is critical for an algorithm to save the usage of memory wherever possible.
In short, you should use data structures and algorithms to make your code scalable.
Algorithms analysis (Big-O notation)
Asymptotic notations are the mathematical notations used to describe the running time of an algorithm when the input is a limiting value. There are three asymptotic notations: Theta notation, Omega notation, and Big-O notation.
Theta notation represents the upper and the lower bound of the running time of an algorithm, it is used for analyzing the average case complexity of an algorithm.
Omega notation represents the lower bound of the running time of an algorithm. Thus, it shows the best case complexity of an algorithm.
Big-O Notation (O-notation): it represents the upper bound of the running time of an algorithm. Thus, it shows the worst case complexity of an algorithm. It is widely used to analyze an algorithm as we are always interested in worst case scenario.
BASIC DATA STRUCTURES
We will study all of the common data structures which are used in Java and other programming languages.
SORTING ALGORITHM
There are various sorting algorithms, and they’re not all equally efficient. We’ll be analyzing their time complexity in order to compare them and see which ones perform the best.
- Insertion Sort
- Selection Sort
- Bubble Sort
- Heap Sort
- Merge Sort
- Quick Sort
- Shell Sort
- Counting Sort
- Radix Sort
- Bucket Sort
ADVANCED DATA STRUCTURES
We keep going to study deeply about some advanced data structures. These are used widely to resolve the computing problems. These data structures are a important part of our Data Structures and Algorithms Tutorial in Java.
GRAPH ALGORITHMS
- Depth first search
- Breadth first search
- Prim’s Algorithm
- Kruskal’s Algorithm
- Dijkstra’s Algorithm
- Bellman Ford’s Algorithm
That’s all about Data Structures and Algorithms Tutorial in Java.
