How would you explain data structures to a non-technical person?

I will explain data structure based on real life experience for example Array: Imagine Grade 1 and Grade 2 students in the class they have their own numbers. The teacher can call ID number 1 from Grade 1 and ID number 45 from Grade 2. Queue: you can imagine cab taxi queue in Addis Ababa. If you came first you will be the first one to get in to the cab(First in First out) Stack: you can imagine putting many plates together after washing. The one you put at last will become the first one to be used. Tree : for this one you can imaging family tree.

Imagine you're organizing a collection of books in a library. Each book has a unique story, and you want to arrange them in a way that makes it easy for people to find and use them efficiently. Data structures are like organizational systems for information in the world of computers.

If we take a real life example of library, a good librarian organizes books by genre, indexes them by author and title to make it easy for us to browse and find a book. Datastructures in computer science help us store and organize data in a way which makes it efficient to store and retrieve the data for required purpose

One thing that I have found helpful for explaining data structures is that you can just use examples like buckets bookshelves serial containers and other kitchen utensils to explain data structures to someone you can use real life examples of Hercules work how less work how dictionary is work and everything is quite possible to explain to someone with non technical background because everything is happening in the real life

I will explain data structures to a normal person by giving real-life examples. Linked list: Music Player: Songs in the music player are linked to the previous and next songs. So you can play songs either from the beginning or the end of the list. Graph: So many people use Facebook and other social media. In Facebook, users are considered to be the vertices, and if they are friends, then there is an edge running between them. Facebook’s friend suggestion algorithm uses graph theory. Stack: Browser history: Web browsers use stacks to keep track of the web pages you visit. Each time you visit a new page, the URL is pushed onto the stack, and when you hit the back button, the previous URL is popped from the stack.

Think of data structures like containers for information on your computer. Imagine you have a toy box with different sections for Legos, action figures, and puzzle pieces. These sections help you quickly find what you want to play with, right? Similarly, data structures organize information for your computer. They make tasks faster and more efficient, like finding a specific song in a playlist. Understanding data structures is like knowing how to arrange your toys for easy access—it makes everything work smoother in the digital world!

Based on research from University of Massachusetts, training a single AI model results in about 6x the carbon emission from an average US car(lifetime usage). At the same time, today we are able to do so much from a small device (like the phone I'm using to type this). The difference has to do with data structures. AI models are generally used with unstructured or pseudo-structured data, which leads to a massive uptick in the computation needs. While all our day to day jobs on devices rely on structured data in the background, leading to simplified ways of use. This is why data structures are important. Even the most well written and designed algorithm would fail in its purpose if it's faced with a data structure it doesn't understand.

You can compare it to your cupboard/wardrobe. If you have segregated and kept clothes properly, it becomes easy to get, search or arrange it back. More structured way it is been segregated and kept, quick and easy it becomes to get and take back.

Data structures are crucial because they provide a framework for organizing and storing data in a computer in a way that allows for efficient retrieval and manipulation

Think of data structures as architects for your program's efficiency. They shape how quickly your software runs and how much memory it consumes. It's like searching for a book on a shelf – different methods, like scanning or using an index, have varying costs and benefits. Similarly, data structures impact your program's performance by influencing how data is stored and accessed. Choosing the right structure is akin to picking the most efficient search method for your bookshelf, ensuring your program runs smoothly without unnecessary delays or resource hogging.

There are so many data structures, but the most commonly used are mainly arrays, linked lists, stacks, etc. What is an array? à An array is a collection of items of the same data type stored at contiguous memory locations. What is a linked list? A linked list is a linear data structure in which the elements are not stored at contiguous memory locations. What is a stack? Stack is a linear data structure that follows a particular order in which the operations are performed. The order may be LIFO (Last In, First Out) or FILO (First In, Last Out). LIFO implies that the element that is inserted last comes out first, and FILO implies that the element that is inserted first comes out last.

Data structures provide a foundation for designing algorithms and solving various computational problems efficiently. The choice of the data structure depends on the specific requirements and characteristics of the task at hand. Some Data Structures include Array, Linked List, Stack, Queue, Trees, Graph etc.

In the world of programming, common data structures include arrays (like numbered boxes), lists (linked chain of boxes), stacks (pile of plates), queues (line of people), trees (family tree structure), graphs (map-like connections), and hash tables (locker room with keys). Each has its own unique structure and purpose, catering to different ways of organizing and accessing information in a computer program. It's like having a toolbox with various tools, and depending on your task, you choose the one that fits best.

Picking the right data structure is like choosing the right tool for a job. Imagine you have different tools in your toolbox—screwdriver, hammer, and pliers. If you need to tighten a screw, you wouldn't use a hammer, right? Similarly, for different tasks in computer programming, you choose a data structure that fits best. Need to search fast? Use a specific structure. Have lots of items to store? Choose another. It's like matching the right tool to make your digital tasks quicker and smoother!

Choosing the right data structure depends on the specific requirements of your task and the characteristics of the data you're working with. Here are some guidelines to help you make the right choice: 1. Understand the Operations: - Identify the operations you need to perform on the data (e.g., search, insert, delete). - Consider the time complexity of these operations for different data structures. 2. Consider Memory Constraints: Evaluate the memory requirements of your data and choose a structure that optimizes space usage. 3. Think About Access Patterns 4. Account for Dynamic Nature 5. Know Your Data Characteristics 6. Consider Specific Use Cases

Choosing the right data structure hinges on factors like the size, type, and operations you'll perform on your data. For swift sorting and searching of large datasets, trees or hash tables might be ideal. For orderly additions or removals in a small dataset, consider using a stack or a queue. If your data involves intricate relationships or networks, a graph could be the right fit. It's about knowing the strengths and weaknesses of each structure and aligning them with your specific needs and objectives. It's akin to selecting the right tool for the job in your programming toolkit.

La mejor forma de entender para que sirven las estructuras de datos, es a través de problemas donde se plantee al alumno una situación y éste tenga que pensar en diferentes opciones para solucionarlo. Por ejemplo, si queremos ense?ar árboles puede ser útil plantear el juego de Akinator, que intenta adivinar lo que el usuario piensa con arboles binarios donde cada rama lleva a una respuesta de si/no. Con todas las optimizaciones de arboles balanceados haciendo que las preguntas con más probabilidad sean las primeras. Si se quieren explicar grafos se puede usar como ejemplo un navegador GPS o el problema del viajero, donde el alumno debería entender fácilmente el contenido de los nodos y los arcos.

To master data structures, start with online resources and tutorials, delving into the basics. Platforms like Codecademy, LeetCode, or HackerRank offer hands-on practice. Apply your knowledge to real-world projects, like website development or game creation, to see the impact of data structures in action. Learning by doing enhances your understanding and problem-solving skills. Embrace the challenges, as the rewards of efficiently organizing and manipulating data in your programs are both satisfying and enjoyable.

An example I have seen people struggle with very often is when they are asked questions involving multiple data structures. And I think the blame for this lies with the inventors. Most data structures are based on previous concepts, and are slightly different across different coding environments in implementation. So my advice would be to ensure that you are clear on the concepts, and master them for at least 1 environment. This way you are confident about this area, and if you are asked questions about other environments, you can always caveat your answer with this. It's better than trying to master everything and then getting confused by the subtle differences.

Use real life examples for example if I have to explain queue data structure, it would be like this: Imagine a queue as a line of people waiting for something. The person who arrives first is the one who gets helped first.Just like waiting in line at a store, a queue follows the "first come, first served" principle.