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https://github.com/awkward-py/dsa
https://github.com/awkward-py/dsa
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- Host: GitHub
- URL: https://github.com/awkward-py/dsa
- Owner: awkward-py
- Created: 2023-11-23T11:49:07.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2023-11-29T18:57:30.000Z (11 months ago)
- Last Synced: 2023-11-29T19:51:12.213Z (11 months ago)
- Size: 72.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
### **Data Structures and Algorithms (DSA) w/ awkwardpy:**
```
╔════════════════════════════════════════╗
║ Table of Contents ║
╠════════════════════════════════════════╣
║ 1. Introduction to DSA ║
║ - 1.1 What is DSA? ║
║ - 1.2 Importance of DSA in Prog... ║
║ - 1.3 Real-World Applications ║
╠════════════════════════════════════════╣
║ 2. Programming Basics ║
║ - 2.1 Choosing a Programming La... ║
║ - 2.2 Basics of Time and Space C... ║
╠════════════════════════════════════════╣
║ 3. Arrays and Strings ║
║ - 3.1 Introduction to Arrays ║
║ - 3.2 Array Operations: Inserti... ║
║ - 3.3 Introduction to Strings ║
║ - 3.4 String Operations: Concat... ║
╠════════════════════════════════════════╣
║ 4. Linked Lists ║
║ - 4.1 Singly Linked Lists ║
║ - 4.2 Doubly Linked Lists ║
║ - 4.3 Operations on Linked Lists ║
║ - 4.4 Advantages and Disadvantages ║
╠════════════════════════════════════════╣
║ 5. Stacks and Queues ║
║ - 5.1 Introduction to Stacks an... ║
║ - 5.2 Operations on Stacks and Q... ║
║ - 5.3 Applications in Real Life ║
╠════════════════════════════════════════╣
║ 6. Trees ║
║ - 6.1 Introduction to Trees ║
║ - 6.2 Binary Trees ║
║ - 6.3 Tree Traversal: Inorder, P... ║
║ - 6.4 Binary Search Trees (BST) ║
║ - 6.5 AVL Trees ║
╠════════════════════════════════════════╣
║ 7. Graphs ║
║ - 7.1 Introduction to Graphs ║
║ - 7.2 Graph Representation: Adja... ║
║ - 7.3 Graph Traversal: DFS, BFS ║
║ - 7.4 Shortest Path Algorithms: ... ║
║ - 7.5 Minimum Spanning Tree: Kru... ║
╠════════════════════════════════════════╣
║ 8. Advanced Data Structures ║
║ - 8.1 Hashing ║
║ - 8.2 Hash Tables ║
║ - 8.3 Heaps and Priority Queues ║
║ - 8.4 Advanced Trees: Red-Black ... ║
╠════════════════════════════════════════╣
║ 9. Sorting and Searching Algorithms ║
║ - 9.1 Sorting Algorithms: Bubble... ║
║ - 9.2 Searching Algorithms: Bina... ║
╠════════════════════════════════════════╣
║ 10. Dynamic Programming ║
║ - 10.1 Introduction to Dynamic P... ║
║ - 10.2 Overlapping Subproblems a... ║
║ - 10.3 Solving Problems using Dyn...║
╠════════════════════════════════════════╣
║ 11. Problem Solving ║
║ - 11.1 Online Judges and Platfor... ║
║ - 11.2 Competitive Programming ║
╠════════════════════════════════════════╣
║ 12. Real-World Applications ║
║ - 12.1 Applying DSA Concepts to ... ║
║ - 12.2 Contributing to Open-Sour... ║
╠════════════════════════════════════════╣
║ 13. Interview Preparation ║
║ - 13.1 Common Interview Questions ║
║ - 13.2 Problem-Solving Strategies ║
║ - 13.3 Mock Interviews and Practice ║
╠════════════════════════════════════════╣
║ 14. Tips for Learning DSA ║
║ - 14.1 Consistent Practice ║
║ - 14.2 Understanding the Basics ║
║ - 14.3 Solving Problems ║
║ - 14.4 Collaborating and Learning ║
║ - 14.5 Staying Updated ║
╠════════════════════════════════════════╣
║ 15. Conclusion ║
║ - 15.1 Recap of Key Concepts ║
║ - 15.2 Journey Towards Mastery ║
╚════════════════════════════════════════╝
```
#### **1. Data Structures:**
**Definition:** Data structures are specialized formats for organizing and storing data in a computer so that it can be used efficiently.
**Key Points:**
- **Examples:** Arrays, Linked Lists, Stacks, Queues, Trees, Graphs, Hash Tables, etc.
- **Purpose:** Efficiently organize and manipulate data.
- **Operations:** Insertion, Deletion, Traversal, Searching, Sorting, etc.
#### **2. Algorithms:**
**Definition:** Algorithms are step-by-step procedures or formulas for solving problems and performing tasks.
**Key Points:**
- **Examples:** Sorting algorithms (QuickSort, MergeSort), Searching algorithms (Binary Search), Graph algorithms (Dijkstra's Algorithm), etc.
- **Purpose:** Solve specific problems or perform specific tasks efficiently.
- **Characteristics:** Correctness, Efficiency, Finiteness, Input, Output.
### **Why DSA is Important:**
1. **Efficiency:** DSA helps in creating efficient algorithms, making programs faster and more responsive.
2. **Optimization:** Proper data structures and algorithms contribute to optimal resource utilization.
3. **Problem Solving:** DSA provides tools for solving complex computational problems.
4. **Scalability:** Understandable and efficient code ensures scalability for larger datasets.
### **Roadmap for Learning DSA:**
#### **1. Basics:**
- **Programming Language:**
- Choose a language you are comfortable with. Common choices include Python, Java, or C++.
- **Understanding Time and Space Complexity:**
- Learn to analyze the efficiency of algorithms.
- **Arrays and Strings:**
- Learn manipulation, searching, and sorting.
#### **2. Fundamental Data Structures:**
- **Linked Lists:**
- Understand types (singly, doubly) and operations.
- **Stacks and Queues:**
- Explore LIFO (Last In, First Out) and FIFO (First In, First Out) structures.
- **Trees and Graphs:**
- Understand hierarchical structures and traversal techniques.
#### **3. Advanced Data Structures:**
- **Hashing:**
- Learn hash functions and collision resolution.
- **Heaps:**
- Explore Priority Queues and Heap Sort.
- **Advanced Trees:**
- AVL Trees, Red-Black Trees, B-Trees.
#### **4. Sorting and Searching Algorithms:**
- **Sorting Algorithms:**
- QuickSort, MergeSort, BubbleSort.
- **Searching Algorithms:**
- Binary Search, Linear Search.
#### **5. Graph Algorithms:**
- **Traversal:**
- Depth-First Search (DFS), Breadth-First Search (BFS).
- **Shortest Path:**
- Dijkstra's Algorithm, Bellman-Ford Algorithm.
- **Minimum Spanning Tree:**
- Kruskal's Algorithm, Prim's Algorithm.
#### **6. Dynamic Programming:**
- **Optimization Technique:**
- Solve problems by breaking them down into smaller overlapping sub-problems.
#### **7. Problem Solving:**
- **Online Judges and Platforms:**
- LeetCode, HackerRank, Codeforces, etc.
- **Competitive Programming:**
- Practice solving algorithmic problems under time constraints.
#### **8. Real-World Applications:**
- **Apply DSA Concepts:**
- Solve real-world problems, contribute to open-source projects.
#### **9. Interview Preparation:**
- **Data Structures and Algorithms in Interviews:**
- Understand common interview questions, practice problem-solving under time constraints.
### **Tips for Learning DSA:**
1. **Consistent Practice:**
- Regular and consistent practice is essential for mastering DSA.
2. **Understand the Basics:**
- Focus on understanding the basics before diving into advanced topics.
3. **Solve Problems:**
- Apply your knowledge by solving a variety of problems.
4. **Collaborate and Learn:**
- Engage with the programming community, ask questions, and learn from others.
5. **Stay Updated:**
- Keep abreast of new algorithms and techniques.
Remember, the journey to mastering DSA is a gradual process, and consistent effort pays off. Start with the basics, build a strong foundation, and gradually move on to more complex topics. Happy coding!let's break down Data Structures and Algorithms (DSA) using simple terms.
### 1. **Data Structures:**
**Explanation:** Think of data structures as different ways to organize and store information in a computer. It's like using different types of containers to hold things.
**Lists (Arrays)**
```awkwardpy
# Imagine a shopping list
awkward_list = ["Apples", "Bananas", "Milk", "Bread"]
```
In this case, `awkward_list` is like a container that holds different items in a specific order.
### 2. **Algorithms:**
**Explanation:** An algorithm is like a step-by-step process or set of instructions for solving a specific problem. It's a way of doing things in a systematic manner.
**Linear Search**
```awkwardpy
# Imagine searching for a book in a library
def find_book(library, target_book):
for book in library:
if book == target_book:
return "Book found!"
return "Book not found."
```
In this example, `find_book` is an algorithm. It tells you to look at each book in the library one by one until you find the one you're looking for.
### 3. **More Data Structures:**
**Stacks**
```awkwardpy
# Imagine stacking plates
class AwkwardStack:
def __init__(self):
self.items = []
def push(self, item):
self.items.append(item)
def pop(self):
if not self.is_empty():
return self.items.pop()
def is_empty(self):
return len(self.items) == 0
```
Here, `AwkwardStack` is like a stack of plates. You can add a plate on top (push), take the top plate off (pop), and check if it's empty.
### 4. **More Algorithms:**
**Binary Search**
```awkwardpy
# Imagine guessing a number
def guess_number(secret_number, guess):
if guess == secret_number:
return "Correct!"
elif guess < secret_number:
return "Too low, guess higher."
else:
return "Too high, guess lower."
```
**In Summary:**
- **Data Structures** are like different types of containers for information.
- **Algorithms** are step-by-step instructions for solving a problem.
- They work together. You choose the right data structure based on your problem and use algorithms to manipulate the data within that structure.