Hands-On Experience with Data Structures and Algorithms: 10 Project Ideas

Mastering DSA concepts by solving real-world problems through practical implementation

As a software developer, it is essential to have a solid understanding of data structures and algorithms (DSA) in order to effectively solve real-world problems.

To gain hands-on experience with DSA, here are ten ideas for projects that can help strengthen your skills:

1. Design and develop an API that utilizes a bloom filter to determine the availability of usernames for a social media-type service

The API’s purpose is to confirm if a requested username is available or already taken.

The bloom filter is a probabilistic data structure that utilizes hashing for efficient storage and to test for the presence of elements in a set, without actually storing the elements themselves. When checking for the presence of an element in the bloom filter, it may return a possible false positive.

The API should:

• Utilize the bloom filter data structure to check for username availability
• Read and store the bloom filter in memory upon initial start-up of the API
• Check the requested username against the in-memory dataset and return the appropriate response
• In the event of multiple instances of the service being deployed, store the bloom filter in a common cache instead of locally.

2. Building a spell checker or auto complete feature using a Trie data structure

Trie data structures are commonly used in predictive text or autocomplete dictionaries and approximate matching algorithms.

The purpose of API is to give spelling suggestions or auto complete type feature

The API Should

• Load all possible strings from a permanent storage source (such as a RDBMS or NOSQL database) upon initial start-up.
• Build an in-memory Trie for each service instance and implement all necessary Trie operations.
• With each keystroke, the API will be called and the top 5 suggestions will be returned to the caller.

3. Implementing a search algorithm (such as binary search) and sorting algorithm (like merge sort) for a large data set

Design and develop an API that utilizes a search algorithm (such as binary search) and a sorting algorithm (such as merge sort) to handle large data sets that cannot be fully loaded into memory.

The API should:

• Read small chunks of data from files (containing a few million entries) at a time and perform operations in-memory.
• Sort the entries using an external merge sort algorithm, and store the sorted entries in separate files.
• Divide the data into log(N) buckets and store the largest number of each bucket in memory.
• Apply a binary search to find out which bucket a number belongs to, and then load the entire bucket into memory and perform another binary search on that bucket.

4. Building a graph traversal algorithm (such as BFS or DFS) for a network or social media application to determine degree of connection between users for millions of users

• Represent the users in the network or social media application as nodes in a graph and connect the users by edges, where an edge represents a connection between two users.
• To get the degree of connection between two users — use Meet in the middle approach and apply BFs.
• Additionally, you can explore some techniques like distributed graph processing or parallel processing.

5. Design and develop a compression algorithm, such as Huffman coding, for a file or data transfer application

The goal of compression algorithms is to decrease the number of bytes needed to represent data and the amount of storage required for files.

• The compression API will take a file as input and return a compressed version of the file
• The decompression API will take a compressed file as input and return the original file
• Create and keep a Huffman tree in memory and perform all necessary processing in memory.

6. Developing a knapsack problem algorithm for inventory management

• Model the items in inventory as objects with properties such as weight and value.
• Define the capacity of the knapsack (inventory) and the objective function (for example, maximize the total value of items in the knapsack while staying within its capacity).
• Implement the knapsack algorithm, which can be done by using either dynamic programming or greedy approach.
• Test the algorithm with different sets of items and knapsack capacities to ensure that it produces the optimal solution.
• Integrate the algorithm into the inventory management system, so that it can be used to make decisions about which items to stock and in what quantities.
• It is important to note that the knapsack problem is NP-hard problem, thus, it may be computationally expensive for large data set.

7. Building a B-tree or B+ tree data structure for efficient storage and retrieval of large amounts of data on disk

The B-tree is a self-balancing tree as well as a specialized M-way tree that is used for disk access.

API Should perform -

• Searching, addition, and deletion in the B-tree in Log(N) time
• Implement self-balancing capabilities in the tree after every insertion and deletion
• Store the data in a file, making it persistent
• Create a B-tree by reading data from a file
• Handle concurrent operations.

8. Building a minimum spanning tree algorithm (such as Kruskal or Prim’s) to solve network optimization problems

optimize the network of roads or paths between thousands of cities and towns and return the best paths to connect all points. (How Google Map comes up with the best route suggestion when you search for direction to anyplace)

• Model the network as a graph, where each city/town represents a vertex and each edge represents a connection between two nodes.
• Assign weightage to each path by time taken between the cities or locations
• Implement the Kruskal’s or Prim’s algorithm, which can be done using dynamic programming or greedy approach.
• Test the algorithm using different sets of input data to ensure that it produces the optimal solution.
• It is important to note that the MST algorithm is a classical problem in computer science, both Kruskal’s and Prim’s algorithms run in O(E log V) time, where E is the number of edges, and V is the number of vertices.

9. Implementing a LRU (Least Recently Used) Cache using a doubly linked list and a hash table

• Define the capacity of the cache and maintain both the hash table and doubly linked list in memory
• Implement the ability to get and put items into the cache
• Ensure that the time complexity of each operation is constant
• Handle concurrent requests scenarios.

10. Creating an algorithm for A* search for pathfinding in a graph or grid-based environment

to approximate the shortest path in real-life situations, like in maps or games where there can be many hindrances.

• Find nearest cab in ride hailing app or near delivery agent from the restaurant for food delivery app
• Initialize the grid in-memory or store it in cache in case of multiple instances.
• Implement the algorithm that read graph from the cache and do calculations in-memory.
• Handle for thousands of concurrent requests.
• By working on these projects, you will gain a deeper understanding of key DSA concepts and be better equipped to tackle real-world problems in your future development work.

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