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Role of data structure and algorithms in computer science

Data Science

Data structures and algorithms are truly regarded as the backbone of computer science, playing a fundamental role in programming and problem-solving. They lay the key foundation for advanced concepts in computer science, simplifying complex subjects such as AI, ML, and cryptography. They also help achieve efficiency in coding, helping programmers optimize code so that it uses less memory and runs faster. Additionally, programmers learning data structures and algorithms tend to develop strong analytical thinking and problem-solving abilities, approaching issues in a more methodical manner and finding creative solutions to them. 

This blog takes a deeper look into the data structure and algorithms’ role while establishing their key importance in providing the required tools and techniques for efficient programming and career advancement in the computer science industry. 

Learn more about the key differences between data science & computer science 

Importance of Data Structures

Data structure refers to a format of data organization and storage in computer science, which enables easy access to data. In other words, data structures are a much more efficient way of organizing and storing data, thereby improving the performance of algorithms. Let us delve deeper into the importance of data structure and algorithms below.

Different types of data structures enable different means of data organization, some of which include:

  • Linked lists: These refer to linear data structures comprising nodes. Each code in turn comprises a data element as well as a reference or pointer to the next node in the sequence.  
  • Graphs: They are the non-linear data structures comprising nodes and edges, and are used to demonstrate complex relationships between objects. 
  • Arrays: They are one of the most basic data structures that comprise a collection of elements stored in adjacent memory locations. 
  • Trees: True to their name, trees look like hierarchical data structures comprising nodes. The nodes are connected by edges. Trees are structured such that they have a root node, branches, and leaves. A few examples include binary search trees, B-trees, binary trees, AVL trees, and red-black trees.    
  • Stacks: They are abstract types of data wherein elements are added and removed from the top of the stack. Stacks therefore follow the Last In, First Out (LIFO) principle.
  • Queues: These are also abstract data types; however, they follow the First In, First Out (FIFO) principle, wherein elements are added at the rear and removed from the front of the queue.

Along with organization, data structures also facilitate easy access to the large sets of data stored therein. This further enables quick and easy search and retrieval of data, as and when required by the programmer. 

You may want to read about the four pillars of data science 

Significance of Algorithms

Data structure and algorithms in CS both have key individual roles to play. The above section details the importance of data structures. Algorithms too are considered the backbone of computing, providing solutions to complex computational problems and enabling optimization of those solutions. Algorithms help provide step-by-step processes for efficient solving of computational problems. They help break down complex tasks into smaller, more manageable sub-tasks.

Certain algorithms are also used for determining optimal solutions within a specified frame of time. Additionally, they enable automation of certain repetitive tasks, thereby reducing manual effort and improving efficiency. 

Some of the popular algorithms include sorting, searching, and graph traversal algorithms. Let us look at their applications in real-world scenarios:

  1. Sorting algorithms: They play an important role in the efficient management of databases by sorting out large datasets based on certain criteria – numerical value, alphabetical order, and time/date stamp. 
  1. Searching algorithms: They work at the core of search engines on the internet, such as Bing, Google, and Yahoo, helping users find relevant information from a huge repository of web pages to match user search queries.
  1. Graph traversal algorithms: They have multiple applications, namely in social network analysis to determine interactions between individuals and entities, in traffic management and network routing, and in logistics and urban planning.

Relationship between Data Structures and Algorithms

The choice of data structure can impact the efficiency of algorithms and the overall program performance. The right data structure directly impacts how well and how fast the program will work its memory usage, and its efficiency in making changes or adding updates later on. 

Data structures help organize and store data, while algorithms are step-by-step instructions for performing tasks. Their relationship can be understood in the way that task performance is directly impacted by how we organize the data. Here are some examples to illustrate how different data structures are used in conjunction with algorithms to solve problems.

You want to find the shortest route on the map from your home to your friend’s house. The data structure in this case is a Graph and the algorithm used is called Breadth-First Search (BFS). It would explore the entire map to identify all the locations neighboring your house one by one. It would then identify and keep track of the shortest route found until it is determined to help you reach your desired location (friend’s home).

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Career opportunities in Data Structures and Algorithms

A strong demand is noticed for professionals with strong skills in data structures and algorithms in industries such as tech, finance, and healthcare, offering competitive salaries and growth opportunities.

To cite a few examples, some of the top career paths that skilled professionals in this area can consider for their future include Software Development, Data Analysis, and Machine Learning. The most valued job roles in each of these domains are listed below along with their corresponding average salaries:

  • Quantitative Analyst: They develop algorithms and mathematical models for pricing derivatives, analyzing financial markets, and managing investment portfolios.  

Average annual salary: INR 17,47,871 per annum

  • Machine Learning Engineer: They develop models and algorithms to train machine learning systems for achieving high-performance solutions for machine learning.

Average annual salary: INR 10,22,496 per annum

  • Data Scientist: They are responsible for analyzing huge sets of data and gather valuable insights from them. 

Average annual salary: INR 971,043 per annum

  • Software Engineer: They are responsible for designing, developing, and maintaining systems and software applications.  

Average annual salary: INR 7,20,521 per annum

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Take the first step to understanding advanced concepts in data structures and algorithms in computer science with professional, expert-led courses from Manipal University Jaipur. This blog aimed at presenting an overall data structure and algorithms overview – a highlight of what the courses at Manipal University Jaipur would entail. Offered in online mode, online BCA and MCA programs at Online Manipal are a perfect way to kickstart your career in a rewarding domain.  


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