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13 Interesting Data Structure Project Ideas and Topics For Beginners [2023]

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13 Interesting Data Structure Project Ideas and Topics For Beginners [2023]

 In the world of computer science, understanding data structures is essential, especially for beginners. These structures serve as the foundation for organizing and manipulating data effectively. To assist newcomers in grasping these concepts, I’ll provide you with data structure project ideas for beginners. These projects are tailored to offer hands-on learning experiences, allowing beginners to explore various data structures while honing their programming skills. By working on these projects, beginners can gain practical insights into data organization and algorithmic thinking, laying a solid foundation for their journey into computer science. Let’s delve into some exciting data structure project ideas designed specifically for beginners.. These projects are tailored to offer hands-on learning experiences, allowing beginners to explore various data structures while honing their programming skills. By working on these projects, beginners can gain practical insights into data organization and algorithmic thinking, laying a solid foundation for their journey into computer science. Let’s delve into some exciting data structure project ideas designed specifically for beginners. 

You can also check out our free courses offered by upGrad under machine learning and IT technology.

Data Structure Basics

Data structures can be classified into the following basic types:

  • Arrays
  • Linked Lists
  • Stacks
  • Queues
  • Trees
  • Hash tables
  • Graphs

Selecting the appropriate setting for your data is an integral part of the programming and problem-solving process. And you can observe that data structures organize abstract data types in concrete implementations. To attain that result, they make use of various algorithms, such as sorting, searching, etc. Learning data structures is one of the important parts in data science courses.

With the rise of big data and analytics, learning about these fundamentals has become almost essential for data scientists. The training typically incorporates various topics in data structure to enable the synthesis of knowledge from real-life experiences. Here is a list of dsa topics to get you started!

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Benefits of Data structures:

Data structures are fundamental building blocks in computer science and programming. They are important tools that helps inorganizing, storing, and manipulating data efficiently. On top of that it provide a way to represent and manage information in a structured manner, which is essential for designing efficient algorithms and solving complex problems.

So, let’s explore the numerous benefits of Data Structures and dsa topics list in the below post: –

1. Efficient Data Access

Data structures enable efficient access to data elements. Arrays, for example, provide constant-time access to elements using an index. Linked lists allow for efficient traversal and modification of data elements. Efficient data access is crucial for improving the overall performance of algorithms and applications.

2. Memory Management

Data structures help manage memory efficiently. They helps in allocating and deallocating memory resources as per requirement, reducing memory wastage and fragmentation. Remember, proper memory management is important for preventing memory leaks and optimizing resource utilization.

3. Organization of Data

Data structures offers a structured way to organize and store data. For example, a stack organizes data in a last-in, first-out (LIFO) fashion, while a queue uses a first-in, first-out (FIFO) approach. These organizations make it easier to model and solve specific problems efficiently.

4. Search and Retrieval

Efficient data search and retrieval are an important aspect in varied applications, like, databases and information retrieval systems. Data structures like binary search trees and hash tables enable fast lookup and retrieval of data, reducing the time complexity of search operations.

5. Sorting

Sorting is a fundamental operation in computer science. Data structures like arrays and trees can implement various sorting algorithms. Efficient sorting is crucial for maintaining ordered data lists and searching for specific elements.

6. Dynamic Memory Allocation

Many programming languages and applications require dynamic memory allocation. Data structures like dynamic arrays and linked lists can grow or shrink dynamically, allowing for efficient memory management in response to changing data requirements.

7. Data Aggregation

Data structures can aggregate data elements into larger, more complex structures. For example, arrays and lists can create matrices and graphs, enabling the representation and manipulation of intricate data relationships.

8. Modularity and Reusability

Data structures promote modularity and reusability in software development. Well-designed data structures can be used as building blocks for various applications, reducing code duplication and improving maintainability.

9. Complex Problem Solving

Data structures play a crucial role in solving complex computational problems. Algorithms often rely on specific data structures tailored to the problem’s requirements. For instance, graph algorithms use data structures like adjacency matrices or linked lists to represent and traverse graphs efficiently.

10. Resource Efficiency

Selecting the right data structure for a particular task can impact the efficiency of an application. Regards to this, Data structures helps in minimizing resource usage, such as time and memory, leading to faster and more responsive software.

11. Scalability

Scalability is a critical consideration in modern software development. Data structures that efficiently handle large datasets and adapt to changing workloads are essential for building scalable applications and systems.

12. Algorithm Optimization

Algorithms that use appropriate data structures can be optimized for speed and efficiency. For example, by choosing a hash table data structure, you can achieve constant-time average-case lookup operations, improving the performance of algorithms relying on data retrieval.

13. Code Readability and Maintainability

Well-defined data structures contribute to code readability and maintainability. They provide clear abstractions for data manipulation, making it easier for developers to understand, maintain, and extend code over time.

14. Cross-Disciplinary Applications

Data structures are not limited to computer science; they find applications in various fields, such as biology, engineering, and finance. Efficient data organization and manipulation are essential in scientific research and data analysis.

Other benefits:

  • It can store variables of various data types.
  • It allows the creation of objects that feature various types of attributes.
  • It allows reusing the data layout across programs.
  • It can implement other data structures like stacks, linked lists, trees, graphs, queues, etc.

Why study data structures & algorithms?

  • They help to solve complex real-time problems.
  • They improve analytical and problem-solving skills.
  • They help you to crack technical interviews.
  • Topics in data structure can efficiently manipulate the data.

Studying relevant DSA topics increases job opportunities and earning potential. Therefore, they guarantee career advancement.

Data Structures Project Ideas

1. Obscure binary search trees

Items, such as names, numbers, etc. can be stored in memory in a sorted order called binary search trees or BSTs. And some of these data structures can automatically balance their height when arbitrary items are inserted or deleted. Therefore, they are known as self-balancing BSTs. Further, there can be different implementations of this type, like the BTrees, AVL trees, and red-black trees. But there are many other lesser-known executions that you can learn about. Some examples include AA trees, 2-3 trees, splay trees, scapegoat trees, and treaps. 

You can base your project on these alternatives and explore how they can outperform other widely-used BSTs in different scenarios. For instance, splay trees can prove faster than red-black trees under the conditions of serious temporal locality. 

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2. BSTs following the memoization algorithm

Memoization related to dynamic programming. In reduction-memoizing BSTs, each node can memoize a function of its subtrees. Consider the example of a BST of persons ordered by their ages. Now, let the child nodes store the maximum income of each individual. With this structure, you can answer queries like, “What is the maximum income of people aged between 18.3 and 25.3?” It can also handle updates in logarithmic time. 

Moreover, such data structures are easy to accomplish in C language. You can also attempt to bind it with Ruby and a convenient API. Go for an interface that allows you to specify ‘lambda’ as your ordering function and your subtree memoizing function. All in all, you can expect reduction-memoizing BSTs to be self-balancing BSTs with a dash of additional book-keeping. 

Dynamic coding will need cognitive memorisation for its implementation. Each vertex in a reducing BST can memorise its sub–trees’ functionality. For example, a BST of persons is categorised by their age.

This DSA topics based project idea allows the kid node to store every individual’s maximum salary. This framework can be used to answer the questions like “what’s the income limit of persons aged 25 to 30?”

Checkout: Types of Binary Tree

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3. Heap insertion time

When looking for data structure projects, you want to encounter distinct problems being solved with creative approaches. One such unique research question concerns the average case insertion time for binary heap data structures. According to some online sources, it is constant time, while others imply that it is log(n) time. 

But Bollobas and Simon give a numerically-backed answer in their paper entitled, “Repeated random insertion into a priority queue.” First, they assume a scenario where you want to insert n elements into an empty heap. There can be ‘n!’ possible orders for the same. Then, they adopt the average cost approach to prove that the insertion time is bound by a constant of 1.7645.

When looking for Data Structures tasks in this project idea, you will face challenges that are addressed using novel methods. One of the interesting research subjects is the mean response insertion time for the sequential heap DS.

Inserting ‘n’ components into an empty heap will yield ‘n!’ arrangements which you can use in suitable DSA projects in C++. Subsequently, you can implement the estimated cost approach to specify that the inserting period is limited by a fixed constant.

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4. Optimal treaps with priority-changing parameters

Treaps are a combination of BSTs and heaps. These randomized data structures involve assigning specific priorities to the nodes. You can go for a project that optimizes a set of parameters under different settings. For instance, you can set higher preferences for nodes that are accessed more frequently than others. Here, each access will set off a two-fold process:

  • Choosing a random number
  • Replacing the node’s priority with that number if it is found to be higher than the previous priority

As a result of this modification, the tree will lose its random shape. It is likely that the frequently-accessed nodes would now be near the tree’s root, hence delivering faster searches. So, experiment with this data structure and try to base your argument on evidence. 

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At the end of the project, you can either make an original discovery or even conclude that changing the priority of the node does not deliver much speed. It will be a relevant and useful exercise, nevertheless.

Constructing a heap involves building an ordered binary tree and letting it fulfill the “heap” property. But if it is done using a single element, it would appear like a line. This is because in the BST, the right child should be greater or equal to its parent, and the left child should be less than its parent. However, for a heap, every parent must either be all larger or all smaller than its children.

The numbers show the data structure’s heap arrangement (organized in max-heap order). The alphabets show the tree portion. Now comes the time to use the unique property of treap data structure in DSA projects in C++. This treap has only one arrangement irrespective of the order by which the elements were chosen to build the tree.

You can use a random heap weight to make the second key more useful. Hence, now the tree’s structure will completely depend on the randomized weight offered to the heap values. In the file structure mini project topics, we obtain randomized heap priorities by ascertaining that you assign these randomly.

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5. Research project on k-d trees

K-dimensional trees or k-d trees organize and represent spatial data. These data structures have several applications, particularly in multi-dimensional key searches like nearest neighbor and range searches. Here is how k-d trees operate:

  • Every leaf node of the binary tree is a k-dimensional point
  • Every non-leaf node splits the hyperplane (which is perpendicular to that dimension) into two half-spaces
  • The left subtree of a particular node represents the points to the left of the hyperplane. Similarly, the right subtree of that node denotes the points in the right half.

You can probe one step further and construct a self-balanced k-d tree where each leaf node would have the same distance from the root. Also, you can test it to find whether such balanced trees would prove optimal for a particular kind of application. 

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With this, we have covered five interesting ideas that you can study, investigate, and try out. Now, let us look at some more projects on data structures and algorithms

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6. Knight’s travails

In this project, we will understand two algorithms in action – BFS and DFS. BFS stands for Breadth-First Search and utilizes the Queue data structure to find the shortest path. Whereas, DFS refers to Depth-First Search and traverses Stack data structures. 

For starters, you will need a data structure similar to binary trees. Now, suppose that you have a standard 8 X 8 chessboard, and you want to show the knight’s movements in a game. As you may know, a knight’s basic move in chess is two forward steps and one sidestep. Facing in any direction and given enough turns, it can move from any square on the board to any other square. 

If you want to know the simplest way your knight can move from one square (or node) to another in a two-dimensional setup, you will first have to build a function like the one below.

  • knight_plays([0,0], [1,2]) == [[0,0], [1,2]]
  • knight_plays([0,0], [3,3]) == [[0,0], [1,2], [3,3]]
  • knight_plays([3,3], [0,0]) == [[3,3], [1,2], [0,0]]

 Furthermore, this project would require the following tasks: 

  • Creating a script for a board game and a night
  • Treating all possible moves of the knight as children in the tree structure
  • Ensuring that any move does not go off the board
  • Choosing a search algorithm for finding the shortest path in this case
  • Applying the appropriate search algorithm to find the best possible move from the starting square to the ending square.

7. Fast data structures in non-C systems languages

Programmers usually build programs quickly using high-level languages like Ruby or Python but implement data structures in C/C++. And they create a binding code to connect the elements. However, the C language is believed to be error-prone, which can also cause security issues. Herein lies an exciting project idea. 

You can implement a data structure in a modern low-level language such as Rust or Go, and then bind your code to the high-level language. With this project, you can try something new and also figure out how bindings work. If your effort is successful, you can even inspire others to do a similar exercise in the future and drive better performance-orientation of data structures.  

Also read: Data Science Project Ideas for Beginners

8. Search engine for data structures

The software aims to automate and speed up the choice of data structures for a given API. This project not only demonstrates novel ways of representing different data structures but also optimizes a set of functions to equip inference on them. We have compiled its summary below.

  • The data structure search engine project requires knowledge about data structures and the relationships between different methods.
  • It computes the time taken by each possible composite data structure for all the methods.
  • Finally, it selects the best data structures for a particular case. 

Read: Data Mining Project Ideas

9. Phone directory application using doubly-linked lists

This project can demonstrate the working of contact book applications and also teach you about data structures like arrays, linked lists, stacks, and queues. Typically, phone book management encompasses searching, sorting, and deleting operations. A distinctive feature of the search queries here is that the user sees suggestions from the contact list after entering each character. You can read the source-code of freely available projects and replicate the same to develop your skills. 

This project demonstrates how to address the book programs’ function. It also teaches you about queuing, stacking, linking lists, and arrays. Usually, this project’s directory includes certain actions like categorising, scanning, and removing. Subsequently, the client shows recommendations from the address book after typing each character. This is the web searches’ unique facet. You can inspect the code of extensively used DSA projects in C++ and applications and ultimately duplicate them. This helps you to advance your data science career.

10. Spatial indexing with quadtrees

The quadtree data structure is a special type of tree structure, which can recursively divide a flat 2-D space into four quadrants. Each hierarchical node in this tree structure has either zero or four children. It can be used for various purposes like sparse data storage, image processing, and spatial indexing. 

Spatial indexing is all about the efficient execution of select geometric queries, forming an essential part of geo-spatial application design. For example, ride-sharing applications like Ola and Uber process geo-queries to track the location of cabs and provide updates to users. Facebook’s Nearby Friends feature also has similar functionality. Here, the associated meta-data is stored in the form of tables, and a spatial index is created separately with the object coordinates. The problem objective is to find the nearest point to a given one. 

You can pursue quadtree data structure projects in a wide range of fields, from mapping, urban planning, and transportation planning to disaster management and mitigation. We have provided a brief outline to fuel your problem-solving and analytical skills. 

QuadTrees are techniques for indexing spatial data. The root node signifies the whole area and every internal node signifies an area called a quadrant which is obtained by dividing the area enclosed into half across both axes. These basics are important to understand QuadTrees-related data structures topics.

Objective: Creating a data structure that enables the following operations

  • Insert a location or geometric space
  • Search for the coordinates of a specific location
  • Count the number of locations in the data structure in a particular contiguous area

One of the leading applications of QuadTrees in the data structure is finding the nearest neighbor. For example, you are dealing with several points in a space in one of the data structures topics. Suppose somebody asks you what’s the nearest point to an arbitrary point. You can search in a quadtree to answer this question. If there is no nearest neighbor, you can specify that there is no point in this quadrant to be the nearest neighbor to an arbitrary point. Consequently, you can save time otherwise spent on comparisons.

Spatial indexing with Quadtrees is also used in image compression wherein every node holds the average color of each child. You get a more detailed image if you dive deeper into the tree. This project idea is also used in searching for the nods in a 2D area. For example, you can use quadtrees to find the nearest point to the given coordinates.

Follow these steps to build a quadtree from a two-dimensional area:

  1. Divide the existing two-dimensional space into four boxes.
  2. Create a child object if a box holds one or more points within.  This object stores the box’s 2D space.
  3. Don’t create a child for a box that doesn’t include any points.
  4. Repeat these steps for each of the children.
  5. You can follow these steps while working on one of the file structure mini project topics.

11. Graph-based projects on data structures

You can take up a project on topological sorting of a graph. For this, you will need prior knowledge of the DFS algorithm. Here is the primary difference between the two approaches:

  • We print a vertex & then recursively call the algorithm for adjacent vertices in DFS.
  • In topological sorting, we recursively first call the algorithm for adjacent vertices. And then, we push the content into a stack for printing. 

Therefore, the topological sort algorithm takes a directed acyclic graph or DAG to return an array of nodes. 

Let us consider the simple example of ordering a pancake recipe. To make pancakes, you need a specific set of ingredients, such as eggs, milk, flour or pancake mix, oil, syrup, etc. This information, along with the quantity and portions, can be easily represented in a graph.

But it is equally important to know the precise order of using these ingredients. This is where you can implement topological ordering. Other examples include making precedence charts for optimizing database queries and schedules for software projects. Here is an overview of the process for your reference:

  • Call the DFS algorithm for the graph data structure to compute the finish times for the vertices
  • Store the vertices in a list with a descending finish time order 
  • Execute the topological sort to return the ordered list 

12. Numerical representations with random access lists

In the representations we have seen in the past, numerical elements are generally held in Binomial Heaps. But these patterns can also be implemented in other data structures. Okasaki has come up with a numerical representation technique using binary random access lists. These lists have many advantages:

  • They enable insertion at and removal from the beginning
  • They allow access and update at a particular index

Know more: The Six Most Commonly Used Data Structures in R

13. Stack-based text editor

Your regular text editor has the functionality of editing and storing text while it is being written or edited. So, there are multiple changes in the cursor position. To achieve high efficiency, we require a fast data structure for insertion and modification. And the ordinary character arrays take time for storing strings. 

You can experiment with other data structures like gap buffers and ropes to solve these issues. Your end objective will be to attain faster concatenation than the usual strings by occupying smaller contiguous memory space. 

This project idea handles text manipulation and offers suitable features to improve the experience. The key functionalities of text editors include deleting, inserting, and viewing text. Other features needed to compare with other text editors are copy/cut and paste, find and replace, sentence highlighting, text formatting, etc.

This project idea’s functioning depends on the data structures you determined to use for your operations. You will face tradeoffs when choosing among the data structures. This is because you must consider the implementation difficulty for the memory and performance tradeoffs. You can use this project idea in different file structure mini project topics to accelerate the text’s insertion and modification.

Conclusion

Data structure skills are foundational in software development, especially for managing vast data sets in today’s digital landscape. Top companies like Adobe, Amazon, and Google seek professionals proficient in data structures and algorithms for lucrative positions. During interviews, recruiters evaluate not only theoretical knowledge but also practical skills. Therefore, practicing data structure project ideas for beginners is essential to kickstart your career. 

If you’re interested in delving into data science, I strongly recommend exploring IIIT-B & upGrad’s Executive PG Programme in Data Science. Tailored for working professionals, this program offers 10+ case studies & projects, practical workshops, mentorship with industry experts, 1-on-1 sessions with mentors, 400+ hours of learning, and job assistance with leading firms. It’s a comprehensive opportunity to advance your skills and excel in the field. 

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Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Frequently Asked Questions (FAQs)

1What do you mean by data structures?

There are certain types of containers that are used to store data. These containers are nothing but data structures. These containers have different properties associated with them, which are used to store, organize, and manipulate the data stored in them.
There can be two types of data structures based on how they allocate the data. Linear data structures like arrays and linked lists and dynamic data structures like trees and graphs.

2What is the difference between linear and non-linear data structures?

In linear data structures, each element is linearly connected to each other having reference to the next and previous elements whereas in non-linear data structures, data is connected in a non-linear or hierarchical manner.
Implementing a linear data structure is much easier than a non-linear data structure since it involves only a single level. If we see memory-wise then the non-linear data structures are better than their counterpart since they consume memory wisely and do not waste it.

3Which real-life applications or projects are based on data structures?

You can see applications based on data structures everywhere around you. The google maps application is based on graphs, call centre systems use queues, file explorer applications are based on trees, and even the text editor that you use every day is based upon stack data structure and this list can go on.
Not just applications, but many popular algorithms are also based on these data structures. One such example is that of the decision trees. Google search uses trees to implement its amazing auto-complete feature in its search bar.

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