Intro to Data Structures and Algorithms

(4 customer reviews)

63,891.74

This course covers the fundamentals of data structures (arrays, linked lists, trees, graphs) and algorithms (sorting, searching). Learn how to optimize code for efficiency, analyze performance, and solve complex programming problems. Ideal for beginners or those preparing for technical interviews.

Description

This course introduces key concepts of data structures and algorithms, which are essential for efficient programming and problem-solving. You’ll learn about arrays, linked lists, stacks, queues, trees, and graphs, and how they can be used to organize data for fast access and manipulation. The course also covers common algorithms for sorting, searching, and traversing through data. By the end of the course, you will have a deeper understanding of algorithmic complexity, performance analysis, and how to optimize code for speed and efficiency. Whether you are preparing for coding interviews or looking to improve your programming skills, this course will give you the foundation needed to solve complex problems with ease.

What you'll learn

What You’ll Learn:

By the end of this course, you will have a strong foundation in data structures and algorithms, essential building blocks for solving computational problems efficiently. You will learn how to organize and store data, understand different algorithms for processing that data, and evaluate the performance of algorithms. This course will introduce you to both basic and advanced data structures, and help you grasp the underlying principles of algorithm design and analysis.

Key learning outcomes include:

  1. Understanding Data Structures:
    • Learn about the fundamental data structures like arrays, linked lists, stacks, queues, and hash tables.
    • Understand more complex structures such as trees, heaps, and graphs.
    • Gain an understanding of when and why to use specific data structures for various types of problems.
  2. Algorithm Design and Problem Solving:
    • Learn the techniques for designing algorithms that solve real-world problems efficiently.
    • Understand the concepts of time and space complexity (Big O notation).
    • Learn the different types of algorithms, including brute-force, greedy algorithms, divide and conquer, and dynamic programming.
  3. Sorting and Searching Algorithms:
    • Master popular sorting algorithms like Bubble Sort, Selection Sort, Merge Sort, and Quick Sort.
    • Learn how to implement searching algorithms like Binary Search and Linear Search.
    • Understand the complexities of different sorting and searching algorithms and how to choose the best one.
  4. Advanced Data Structures and Algorithms:
    • Learn about trees (binary trees, binary search trees, AVL trees, etc.) and how to traverse and manipulate them.
    • Understand the implementation and use cases of heaps, priority queues, and hash tables.
    • Study graph algorithms (e.g., DFS, BFS, Dijkstra’s Algorithm) and how to work with graph representations like adjacency matrices and lists.
  5. Algorithm Analysis:
    • Learn to evaluate the efficiency of algorithms.
    • Understand the concepts of Big O, Big Omega, and Big Theta.
    • Compare the performance of different algorithms in terms of time complexity and space complexity.
  6. Dynamic Programming and Problem Optimization:
    • Understand the concept of dynamic programming and how it can optimize brute force algorithms.
    • Learn techniques like memoization and tabulation for solving problems efficiently.
  7. Practical Applications:
    • Apply your knowledge of data structures and algorithms to solve real-world problems and coding challenges.
    • Gain insight into how these concepts are used in software engineering, computer science, and other fields.

Syllabus

Course Syllabus:

Module 1: Introduction to Data Structures and Algorithms

  • What are data structures and algorithms?
  • Why are they important for problem-solving and computer science?
  • Overview of time complexity and space complexity (Big O notation)
  • Introduction to algorithm design and analysis

Module 2: Basic Data Structures

  • Arrays: Basic operations (insert, delete, search) and use cases
  • Linked Lists: Singly and doubly linked lists, insertion and deletion, traversal
  • Stacks: Stack operations (push, pop, peek), applications of stacks (e.g., expression evaluation)
  • Queues: Queue operations (enqueue, dequeue), types of queues (simple queue, circular queue, priority queue)
  • Hash Tables: Hashing function, collision resolution techniques (chaining, open addressing)

Module 3: Algorithm Analysis

  • Time Complexity: Big O notation, Big Theta, and Big Omega
  • Space Complexity: Understanding space consumption of algorithms
  • Best, worst, and average case complexity analysis
  • Practical tips for analyzing algorithm efficiency

Module 4: Sorting Algorithms

  • Introduction to sorting algorithms
  • Bubble Sort: Concept, implementation, time complexity
  • Selection Sort: Concept, implementation, time complexity
  • Insertion Sort: Concept, implementation, time complexity
  • Merge Sort: Concept, divide-and-conquer approach, time complexity
  • Quick Sort: Concept, partitioning, time complexity, optimization
  • Comparison of sorting algorithms: Which one to use when?

Module 5: Searching Algorithms

  • Linear Search: Basic search technique, time complexity
  • Binary Search: Concept, implementation, conditions, time complexity
  • Searching in sorted and unsorted arrays

Module 6: Advanced Data Structures

  • Trees: Introduction to trees, binary trees, binary search trees
    • Tree traversal algorithms: In-order, pre-order, and post-order
    • Balancing trees: AVL trees and Red-Black trees
  • Heaps and Priority Queues: Introduction to heaps, max-heaps, min-heaps, priority queues, heap operations
  • Graphs: Graph representations (adjacency matrix, adjacency list), types of graphs (directed, undirected)
    • Graph traversal algorithms: Depth First Search (DFS), Breadth First Search (BFS)
    • Shortest path algorithms: Dijkstra’s algorithm

Module 7: Dynamic Programming

  • Introduction to dynamic programming (DP) and its benefits over brute-force solutions
  • Solving problems using DP:
    • Fibonacci sequence
    • Knapsack problem
    • Longest common subsequence
    • Coin change problem
  • Memoization vs Tabulation: Techniques for optimizing recursive algorithms

Module 8: Divide and Conquer Algorithms

  • Understanding the divide-and-conquer paradigm
  • Merge Sort and Quick Sort revisited as divide-and-conquer algorithms
  • Other divide-and-conquer algorithms:
    • Binary search
    • Closest pair of points

Module 9: Greedy Algorithms

  • Introduction to greedy algorithms and their characteristics
  • Common greedy algorithms:
    • Fractional Knapsack Problem
    • Activity Selection Problem
    • Huffman Coding
  • When to use greedy algorithms vs dynamic programming

Module 10: Graph Algorithms

  • Graph Traversal: Depth-First Search (DFS) and Breadth-First Search (BFS)
  • Shortest path algorithms:
    • Dijkstra’s Algorithm: For weighted graphs
    • Bellman-Ford Algorithm: For negative weights
  • Minimum Spanning Tree: Kruskal’s and Prim’s algorithms
  • Topological Sorting and its applications

Module 11: Problem Solving and Optimization

  • Applying data structures and algorithms to real-world problems
  • Backtracking algorithms: Solving problems by trying all possibilities (e.g., N-Queens problem, Sudoku solver)
  • Greedy vs DP vs Backtracking: When to use each approach

Module 12: Final Project and Wrap-Up

  • Final project: Solve a complex problem using appropriate data structures and algorithms
  • Review of key concepts learned throughout the course
  • Best practices for coding interviews and technical assessments
  • Resources for further study and learning

Course Delivery and Format:

  • Duration: 8-10 weeks (self-paced or instructor-led)
  • Format: Video lectures, interactive coding exercises, quizzes, and hands-on assignments
  • Assignments: Weekly coding challenges focused on implementing and applying various data structures and algorithms
  • Final Project: Implement a solution to a real-world problem using a combination of data structures and algorithms
  • Tools: Coding environments like Visual Studio Code, PyCharm, or any online IDE for practice

Prerequisites:

  • Basic knowledge of programming (preferably in Python, Java, or C++) is recommended.
  • Familiarity with basic programming concepts such as loops, functions, and conditional statements.

Tools and Resources:

  • Access to an integrated development environment (IDE)
  • Online coding platforms like LeetCode, CodeSignal, or HackerRank for practicing problems
  • Reference books and online resources (optional for further learning)

4 reviews for Intro to Data Structures and Algorithms

  1. Fidelia

    “I was blown away by the ‘Intro to Data Structures and Algorithms’ online course! The expert instructors presented complex concepts with remarkable clarity. The interactive exercises and quizzes were invaluable in reinforcing my understanding. The course not only equipped me with a solid foundation in data structures and algorithms but also sparked my interest in the field. I highly recommend this course to anyone seeking a comprehensive and engaging introduction to the subject.”

  2. Saadatu

    “I was thoroughly impressed with the ‘Intro to Data Structures and Algorithms’ online course. The content was comprehensive and well-structured, providing a solid foundation in the subject matter. The interactive simulations and hands-on exercises were invaluable, allowing me to apply the concepts I had learned in a practical setting. The instructor was knowledgeable and engaging, and the pace of the course was perfect for my learning style. Overall, I highly recommend this course to anyone looking to gain a solid understanding of data structures and algorithms.”

  3. Tawakalitu

    “This online course was an exceptional introduction to data structures and algorithms. The instructor’s clear explanations and practical examples made complex concepts accessible. The well-structured course material and interactive exercises allowed me to grasp the fundamentals and apply them effectively in coding challenges. I highly recommend this course for anyone seeking to build a strong foundation in data structures and algorithms.”

  4. Kelachi

    “This online course was an exceptional introduction to the fundamentals of data structures and algorithms. The instructor’s clear explanations and engaging examples made complex concepts easy to grasp. The interactive exercises and quizzes reinforced my understanding and helped me apply the knowledge practically. The course materials were well-organized and provided ample resources, supporting my progress throughout the learning journey. Highly recommended for anyone seeking a solid foundation in these essential computer science concepts.”

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