#data structure and algorithms

[hackeocafe]

Statistics - A Full Lecture to learn Data Science (2025 Version)

Welcome to our comprehensive and free statistics tutorial (Full Lecture)! In this video, we'll explore essential tools and techniques that power data science and data analytics, helping us interpret data effectively. You'll gain a solid foundation in key statistical concepts and learn how to apply powerful statistical tests widely used in modern research and industry. From descriptive statistics to regression analysis and beyond, we'll guide you through each method's role in data-driven decision-making. Whether you're diving into machine learning, business intelligence, or academic research, this tutorial will equip you with the skills to analyze and interpret data with confidence. Let's get started!

[adayiniilm]

Data Structures and Algorithms: The Building Blocks of Efficient Programming

The world of programming is vast and complex, but at its core, it boils down to solving problems using well-defined instructions.  While the specific code varies depending on the language and the task, the fundamental principles of data structures and algorithms underpin every successful application.  This blog post delves into these crucial elements, explaining their importance and providing a starting point for understanding and applying them.

What are Data Structures and Algorithms?

Imagine you have a vast collection of books.  You could haphazardly pile them, making it nearly impossible to find a specific title.  Alternatively, you could organize them by author, genre, or subject, with indexed catalogs, allowing quick retrieval. Data structures are the organizational systems for data. They define how data is stored, accessed, and manipulated.

Algorithms, on the other hand, are the specific instructions—the step-by-step procedures—for performing tasks on the data within the chosen structure.  They determine how to find a book, sort the collection, or even search for a particular keyword within all the books.

Essentially, data structures provide the containers, and algorithms provide the methods to work with those containers efficiently.

Fundamental Data Structures:

Arrays:  A contiguous block of memory used to store elements of the same data type.  Accessing an element is straightforward using its index (position).  Arrays are efficient for storing and accessing data, but inserting or deleting elements can be costly.  Think of a numbered list of items in a shopping cart.

Linked Lists: A linear data structure where elements are not stored contiguously. Instead, each element (node) contains data and a pointer to the next node. This allows for dynamic insertion and deletion of elements but accessing a specific element requires traversing the list from the beginning.  Imagine a chain where each link has a piece of data and points to the next link.

Stacks:  A LIFO (Last-In, First-Out) structure. Think of a stack of plates: the last plate placed on top is the first one removed.  Stacks are commonly used for function calls, undo/redo operations, and expression evaluation.

Queues: A FIFO (First-In, First-Out) structure.  Imagine a queue at a ticket counter—the first person in line is the first one served. Queues are useful for managing tasks, processing requests, and implementing breadth-first search algorithms.

Trees:Hierarchical data structures that resemble a tree with a root, branches, and leaves.  Binary trees, where each node has at most two children, are common for searching and sorting.  Think of a file system's directory structure, representing files and folders in a hierarchical way.

Graphs:  A collection of nodes (vertices) connected by edges.  Represent relationships between entities. Examples include social networks, road maps, and dependency diagrams.

Crucial Algorithms:

Sorting Algorithms:  Bubble Sort, Insertion Sort, Merge Sort, Quick Sort, Heap Sort—these algorithms arrange data in ascending or descending order.  Choosing the right algorithm for a given dataset is critical for efficiency.  Large datasets often benefit from algorithms with time complexities better than O(n^2).

Searching Algorithms:  Linear Search, Binary Search—finding a specific item in a dataset.  Binary search significantly improves efficiency on sorted data compared to linear search.

Graph Traversal Algorithms:  Depth-First Search (DFS), Breadth-First Search (BFS)—exploring nodes in a graph.  Crucial for finding paths, determining connectivity, and solving various graph-related problems.

Hashing:  Hashing functions take input data and produce a hash code used for fast data retrieval. Essential for dictionaries, caches, and hash tables.

Why Data Structures and Algorithms Matter:

Efficiency:  Choosing the right data structure and algorithm is crucial for performance.  An algorithm's time complexity (e.g., O(n), O(log n), O(n^2)) significantly impacts execution time, particularly with large datasets.

Scalability:Applications need to handle growing amounts of data.  Well-designed data structures and algorithms ensure that the application performs efficiently as the data size increases.

Readability and Maintainability:  A structured approach to data handling makes code easier to understand, debug, and maintain.

Problem Solving:  Understanding data structures and algorithms helps to approach problems systematically, breaking them down into solvable sub-problems and designing efficient solutions.

[datascience12course]

What Are the Steps to Becoming a Programmer in 2023? An Instructional Manual

The best approach to learning programming is to experiment with little pieces of code like "Hello World." Many programmers' first exposure to coding was this specific code fragment, which may be found in many coding languages. Hello World appears on your computer screen when you run this code. Such a brief piece of code would probably take 15 minutes to write. Such code snippets might not have much of an impact on humanity. When you begin writing these basic code blocks, the question "How to become a computer programmer?" is less intimidating. However, building larger code sets for real-world applications could take a month or longer. Get started with the Data Structures and Algorithms Course and detailed instructions for learning to program.

[teabutmakeitazure]

FINALS OVER 🔥🔥🔥🔥

DSA NEXT SEM 🔥🔥🔥

[sweetanyways]

(practicing dsa) is somebody gonna match my leet

[solarstrwbry]

The feminine urge to code a silly little text based lesbian romance sim instead of taking this coding exam

[mindfulengineer]

7 Growth Functions in Data Structures: Behind asymptotic notations

Top coders use these to calculate time complexity and space complexity of algorithms.

https://medium.com/competitive-programming-concepts/7-growth-functions-in-data-structures-behind-asymptotic-notations-0fe44330daef

[watchmorecinema]

Normally I just post about movies but I'm a software engineer by trade so I've got opinions on programming too.

Apparently it's a month of code or something because my dash is filled with people trying to learn Python. And that's great, because Python is a good language with a lot of support and job opportunities. I've just got some scattered thoughts that I thought I'd write down.

Python abstracts a number of useful concepts. It makes it easier to use, but it also means that if you don't understand the concepts then things might go wrong in ways you didn't expect. Memory management and pointer logic is so damn annoying, but you need to understand them. I learned these concepts by learning C++, hopefully there's an easier way these days.

Data structures and algorithms are the bread and butter of any real work (and they're pretty much all that come up in interviews) and they're language agnostic. If you don't know how to traverse a linked list, how to use recursion, what a hash map is for, etc. then you don't really know how to program. You'll pretty much never need to implement any of them from scratch, but you should know when to use them; think of them like building blocks in a Lego set.

Learning a new language is a hell of a lot easier after your first one. Going from Python to Java is mostly just syntax differences. Even "harder" languages like C++ mostly just mean more boilerplate while doing the same things. Learning a new spoken language in is hard, but learning a new programming language is generally closer to learning some new slang or a new accent. Lists in Python are called Vectors in C++, just like how french fries are called chips in London. If you know all the underlying concepts that are common to most programming languages then it's not a huge jump to a new one, at least if you're only doing all the most common stuff. (You will get tripped up by some of the minor differences though. Popping an item off of a stack in Python returns the element, but in Java it returns nothing. You have to read it with Top first. Definitely had a program fail due to that issue).

The above is not true for new paradigms. Python, C++ and Java are all iterative languages. You move to something functional like Haskell and you need a completely different way of thinking. Javascript (not in any way related to Java) has callbacks and I still don't quite have a good handle on them. Hardware languages like VHDL are all synchronous; every line of code in a program runs at the same time! That's a new way of thinking.

Python is stereotyped as a scripting language good only for glue programming or prototypes. It's excellent at those, but I've worked at a number of (successful) startups that all were Python on the backend. Python is robust enough and fast enough to be used for basically anything at this point, except maybe for embedded programming. If you do need the fastest speed possible then you can still drop in some raw C++ for the places you need it (one place I worked at had one very important piece of code in C++ because even milliseconds mattered there, but everything else was Python). The speed differences between Python and C++ are so much smaller these days that you only need them at the scale of the really big companies. It makes sense for Google to use C++ (and they use their own version of it to boot), but any company with less than 100 engineers is probably better off with Python in almost all cases. Honestly thought the best programming language is the one you like, and the one that you're good at.

Design patterns mostly don't matter. They really were only created to make up for language failures of C++; in the original design patterns book 17 of the 23 patterns were just core features of other contemporary languages like LISP. C++ was just really popular while also being kinda bad, so they were necessary. I don't think I've ever once thought about consciously using a design pattern since even before I graduated. Object oriented design is mostly in the same place. You'll use classes because it's a useful way to structure things but multiple inheritance and polymorphism and all the other terms you've learned really don't come into play too often and when they do you use the simplest possible form of them. Code should be simple and easy to understand so make it as simple as possible. As far as inheritance the most I'm willing to do is to have a class with abstract functions (i.e. classes where some functions are empty but are expected to be filled out by the child class) but even then there are usually good alternatives to this.

Related to the above: simple is best. Simple is elegant. If you solve a problem with 4000 lines of code using a bunch of esoteric data structures and language quirks, but someone else did it in 10 then I'll pick the 10. On the other hand a one liner function that requires a lot of unpacking, like a Python function with a bunch of nested lambdas, might be easier to read if you split it up a bit more. Time to read and understand the code is the most important metric, more important than runtime or memory use. You can optimize for the other two later if you have to, but simple has to prevail for the first pass otherwise it's going to be hard for other people to understand. In fact, it'll be hard for you to understand too when you come back to it 3 months later without any context.

Note that I've cut a few things for simplicity. For example: VHDL doesn't quite require every line to run at the same time, but it's still a major paradigm of the language that isn't present in most other languages.

Ok that was a lot to read. I guess I have more to say about programming than I thought. But the core ideas are: Python is pretty good, other languages don't need to be scary, learn your data structures and algorithms and above all keep your code simple and clean.

[horsescary]

if ur gonna write a textbook with java in it maybe learn java naming conventions first

[lazyp0tat07]

What Dijkstra's two-stack algorithm does to Left parenthesis of Infix expression is everyone does to me

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IGNORE 🤡

[snaildotexe]

Software Technical Interview Review List

Data Structures

Arrays (and Java List vs ArrayList)

String

Stack

Queue

LinkedList

Algorithms

Sorting (Bubblesort, Mergesort, Quicksort)

Recursion & Backtracking

Linear and Binary Search

String/Array algos

Tree traversal

Dynamic Programming

Graph algos (DFS, BFS, Dijksta's and Kruskals)

OOP fundamentals

Polymorphism

Inheritance

Encapsulation

Data abstraction

SOLID and GRASP

Explanations & example questions:

Strings and Arrays [ 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 ]

Stacks and Queues [ 1 | 2 ]

LinkedList [ 1 | 2 ]

Sorting & searching [ 1 | 2 | 3 | 4 | 5 | 6 | 7 ]

Recursion and Backtracking [ 1 | 2 | 3 | 4 ]

Dynamic Programming [ 1 | 2 | 3 | 4]

Graphs [ 1 | 2 | 3 ]

Tree [ 1 | 2 ]

General DS&A info and questions [ 1 | 2 | 3 | 4 | 5 ]

OOP review & questions [ 1 | 2 | 3 ]

[hackeocafe]

Data Structure and Algorithms in JAVA | Full Course on Data Structure

In this course, we are going to discuss Data Structures and Algorithms using Java Programming. The data structure is a way to store and organize data so that it can be used efficiently. It is a set of concepts that we can use in any programming language to structure the data in the memory. Data structures are widely used in almost every aspect of computer science i.e. operating systems, computer science, compiler design, Artificial Intelligence, graphic,s and many more. Some examples of Data structures that we are going to cover in this course are arrays, linked lists, stack, queue, Binary Tree, Binary Search Tree, Graphs, etc. Apart from knowing these data structures, it's also important to understand the algorithmic analysis of a given code. Different Sorting and searching techniques will be talked about with their implementation in java programming. Lastly, this course contains information on the Greedy approach, Dynamic approach, and divide and Conquer approach to programming.

[adayiniilm]

How DSA can help in placement?

Data Structures and Algorithms (DSA) play a vital role in placement processes for various purposes.

Develop problem-solving skills: Data Structures and Algorithms help you enhance analytical thinking and problem-solving abilities, crucial for technical interviews.

Technical Interviews: A lot of companies prioritize DSA concepts when assessing a candidate's coding skills, logical reasoning, and capability to write efficient code.

Optimization: Having comprehension of DSA enables candidates to develop optimized code, crucial for performance-driven applications.

DSA serves as the groundwork for various complex computer science subjects, including system architecture and database administration.

Competitions and Assessments: A lot of coding challenges and contests put a strong focus on DSA, which can help boost your ranking.

Practical Uses: Understanding DSA is crucial for creating effective software solutions, which can make you a highly sought-after candidate for employers.

Putting emphasis on DSA while preparing can greatly improve your odds of success in technical interviews and career advancement as a whole.

https://www.iilm.edu

[truebusiness]

Exploring Quantum Leap Sort: A Conceptual Dive into Probabilistic Sorting Created Using AI

In the vast realm of sorting algorithms, where QuickSort, MergeSort, and HeapSort reign supreme, introducing a completely new approach is no small feat. Today, we’ll delve into a purely theoretical concept—Quantum Leap Sort—an imaginative algorithm created using AI that draws inspiration from quantum mechanics and probabilistic computing. While not practical for real-world use, this novel…

[teabutmakeitazure]

if i had a nickel for every time i turned to my childe and aventurine standees and whispered "i am better than you" today i would have two nickels which isn't a lot but it's weird that i did that twice

[shelikesrainydays]

// August 18th, 2024

Yesterday I broke in tears because I think I’m going to fail Data Structures and Algorithms this semester. It’s just, too much. Not the topics I have to study, not even that I don’t actually enjoy practicing it, oh no. It’s that I don’t think I’m going to be able to send all of my assignments on time and they affect the final grade a lot. The materials for all three subjects get released only on Wednesdays at noon and it’s expected that an almost impossible amount of assignments are delivered before the following Tuesday at 8 p.m.

Why can’t I, for instance, do the work, send it as soon as I have it ready, and they can just take into account that I actually followed on the lessons and studied hard for the two exams before the final? It’s just… I don’t know. It seems ridiculous to me. I would be doing my best even without having that stupid pressure of doing the assignments on time, like any other person who is serious about theirs studies. I know I probably sound like a stupid kid, but people going to this university at night usually have a job already, otherwise, why wouldn’t we be taking the lessons on mornings/afternoons? Don’t they think about those cases? Incredible.

And I know one can probably think, “well, why don’t you drop that subject and follow up others and retake this one further in the future?

And the answer to that is, a) I’m feeling old to keep postponing my graduation (35 is becoming my least worst shot) and b) I have more plans, like for other important aspects of my life as well. And… ugh. The pressure. It’s too much.

I spoke to my sweet boyfriend about all these worries I have and he’s happy with both me telling him what I feel and how hard I am working with this. “Honey, it’s just the second week of the semester. You got this”. Cuddles and sushi were provided, and even when that didn’t wash the worries or pressure away, I felt loved. And cared for. I’m so lucky to have him in my life.

Sorry for the rant guys. I get that this is just the way it is and that these are the terms for getting a good grade, and if I don’t like it I can just leave it and try again, but… I just needed to get some of it out of my chest 🥲

But hey, at least I was able to write my very first little programs in C yesterday. Yay 🍷