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A Revision Guide To Data Structures & Algorithms

A Revision Guide To Data Structures & Algorithms

Everything is data. Your name, address, and other credentials, your search history or bank account details, the information in a book or a webpage, and back account details— data is ubiquitous & all over. Information systems store and process all such data using different data structures and software algorithms.

Courses and study programs in IT, computer science & applications, such as the ones offered by TAFE Australia, impart the skills & knowledge to operate, maintain, and improve such information systems, be it the World Wide Web or any private domain. Assignments and coursework on data structures & algorithms are fundamental and essential for any domain student.

This article looks precisely at the different types of data structures and two central data manipulation algorithms. Go through it for some top-notch data structures & algorithm computer science assignment help.

Data Structures

Before we begin, let’s make one thing clear, data structures & databases are NOT the same. While they are both used to store data, databases are much larger in scale than data structures. Moreover, data structures are generated in the temporary memory while databases are stored in the permanent memory.

The most rudimentary data structures namely:

  • Stacks
  • Queues
  • Linked Lists
  • Rooted Trees
  • Graphs

Data structures can also be primarily categorized as linear & non-linear data structures.

  • Linear data structures can be static in nature (array) and can be dynamic (stack, queue, linked list).
  • Trees and graphs are the most commonly employed data structures.

The above data structures can be enhanced and/or used in tandem to develop intricate & advanced data structures.

Let’s take a quick look at their properties.

  • Stacks follow the LIFO (last in, first out) policy, wherein the last element that is pushed into the data structure is also the first element to be popped out.
  • Queues follow the FIFO (first in, first out) policy where the absolute first element in the structure is also the first one to exit.
  • Linked lists are unique data structures that comprise of two distinct parts, one containing a value and the other, the address of the successive elements of the linked list. Linked lists can be singly linked, doubly linked, or circularly linked.

Doubly Circular List

  • Rooted trees are another type of non-linear data structure that begin at a single object (the root) and point to two other objects, which are its children. Binary trees are rooted trees that can have at most two children.

Every node or object in a binary tree has three parts, the data, the pointer to the left child, and the pointer to the right child.

  • Graphs are another non-linear data structure comprising nodes/vertices(V) & edges (E). Graphs are denoted as G(E, V) and find many real-life applications in communications & networking, social networking, circuits, etc.

Two other particular types of data structures are dictionaries and heaps. They can also be referred to as abstract data types, whose behavior is dictated by specific attributes and functions.

Dictionaries map keys to values.

Hash tables are a special dictionary data structure implementation that uses the hashing technique to map hashes of certain keys to values. Hashing tables use a hash function that improves data access speeds, and the more efficient the hashing function, the better the mapping efficiency.

  • Heaps are a special type of tree-based data structure using complete binary trees. They are mainly of two types: min-heaps (all root nodes of every sub-tree in a heap must have the minimum value or key) and max-heaps (roots must have maximum value).

Those were the fundamental and most commonly used data structures in computer programming.

Let us now look at the most commonly used algorithms in programming & applications.

Searching & Sorting Algorithms

Two of the most rudimentary operations in any information system are sorting & searching. Therefore, sorting & searching data collections & databases is an essential learning aspect of any computer science and/or application course.

  • Searching involves finding something in a data structure. It is one of the most common tasks in computer applications and something we humans frequently do. For example, we search for information using search engines, PDFs, documents,IT assignment help, etc.
  • Similarly, we can search different data structures like stacks, lists, dictionaries, etc.

Data searching operations can vary per the arrangement of data in the structures. If they are sorted or organised in a specific fashion, then interval or bisect search is more efficient. In the case of unorganised data, then a linear search is much more effective.

  • Sorting involves organising elements in a data structure in a particular fashion. Sorting algorithms are generally well-researched, but new & improved algorithms are designed by researchers & engineers.

There are many different sorting algorithms in place. Unfortunately, dwelling into every one of them in depth will require an entire book. So instead, we will talk about the top 5 sorting algorithms in brief.

The Searching & Sorting Algorithms

  • Linear Searchà Iterate the entire list from 0 to N-1 and compare the values at every index with the value searched for. Return the location index if found. Start from the leftmost data structure element and compare it with every element.
  • Binary Search àThe binary search approach works best in a sorted data structure. This is because it bisects the entire structure in half, reducing time complexity.

The basic steps are as follows:

  • Begin with the element at the centre and make it the search key.
  • If the mid value is equal to the searched element, then return.
  • If it is less than the element, narrow the search interval to the upper half.
  • If it is more than the element, narrow the search interval to the lower half.
  • Bubble Sorting is the simplest of all sorting algorithms. The chief idea is to place the largest element at the end or top of the sorted data structure and do so during every iteration.
  • Begin by comparing the first two elements in the data structure.  
  • Selection Sortingselects a particular element and compares it with every other element in the data structure. 
  • Take a variable named ‘minimum index’ and set it to the first index location of the structure. That is the selected value.
  • Traverse the rest of the array and exchange the value at the minimum index if you find something smaller. The value at the minimum index takes the position of that smaller value.
  • After every traversal, point the minimum index to the next element, the value at index 1.
  • Insertion Sortis another simple algorithm with easy implementations. 
  • Start iterating from the 1stindex, from the second element to the last element of the array. That’s the key.
  • Compare the key to its predecessor.
  • If the key element is smaller than its predecessor, compare it to the elements before the predecessor. Then, move the greater elements one position up to make way for the swapped element.

We wrap up this write-up with a look at the different time & space complexities of different sorting algorithms.

Time & Space Complexity

The operational efficiency of any algorithm primarily depends upon two parameters: time & space complexity.

  • the time taken for an algorithm to run depends upon its time complexity and factors such as compiler & processor time.

Time complexity can have worst, best, or average time complexities.

  • Space Complexity: Space complexity is the total amount of space a program requires for execution.

And that’s all the space we have for today. Here’s hoping this article comes in handy and offers some quality data structures & algorithm assignment help.

If you are enrolled in a TAFE computer science course and struggling with DS & algorithms, then look for a reputed TAFE assignment help service today.

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