In the world of computer science, data structure refers to the format that contains a collection of data values, their relationships, and the functions that can be applied to the data. Data structures arrange data so that it can be accessed and worked on with specific algorithms more effectively. In this article, we will list some useful data structure projects to help you learn, create, and innovate!
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Data Structure Basics
Data structures can be classified into the following basic types:
- Linked Lists
- Hash tables
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 data structure projects to enable the synthesis of knowledge from real-life experiences. Here is a list of topics to get you started!
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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.
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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.
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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.
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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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.
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.
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.
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
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
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.
Data structure skills form the bedrock of software development, particularly when it comes to managing large sets of data in today’s digital ecosystem. Leading companies like Adobe, Amazon, and Google hire for various lucrative job positions in the data structure and algorithm domain. And in interviews, recruiters test not only your theoretical knowledge but also your practical skills. So, practice the above data structure projects to get your foot in the door!
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What 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.
What 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.
Which 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.