Core Programming Mastery

Duration

10 Weeks

Lectures

50+ Sessions

Projects

4 Major Projects

Skill Level

Beginner to Advanced

Language

English

Certificate

Yes, upon completion

Lead Instructor

Dr. Ravi Kumar

Computer Science Professor

Dr. Kumar has over 15 years of experience teaching computer science and algorithms. He holds a Ph.D. in Computer Science and has published numerous papers on algorithm optimization. He has worked as a technical interviewer for several major tech companies and specializes in preparing students for technical interviews.

Prerequisites

Basic programming knowledge in any language
Understanding of basic mathematical concepts
Logical thinking and problem-solving aptitude
Familiarity with at least one programming language
Dedication to practice and solve problems

Course Description

Our Core Programming Mastery course is designed to build a solid foundation in programming fundamentals, data structures, and algorithms that are essential for any software development career.

This comprehensive program covers the core concepts of programming that transcend specific languages, focusing on problem-solving approaches, computational thinking, and algorithm design. You'll learn how to write efficient, optimized code by understanding the underlying principles of data structures and algorithms.

Whether you're a beginner looking to start a programming career or an experienced developer wanting to strengthen your fundamental knowledge, this course will enhance your problem-solving abilities and prepare you for technical interviews at top tech companies. The skills you'll gain are language-agnostic and can be applied across various programming languages and development environments.

Language-Agnostic Approach

Concepts applicable across programming languages

Interview Preparation

Focused on common technical interview questions

Over 100 coding challenges and exercises

Algorithmic Thinking

Develop efficient problem-solving approaches

Course Objectives

Understand fundamental programming concepts and paradigms
Master essential data structures (arrays, linked lists, stacks, queues, trees, graphs, hash tables)
Analyze and implement common algorithms (sorting, searching, graph algorithms)
Evaluate algorithm efficiency using Big O notation
Apply problem-solving techniques to break down complex problems
Optimize code for performance and memory usage
Implement recursion and dynamic programming solutions
Develop debugging and testing strategies
Practice solving common technical interview questions
Build a portfolio of algorithm implementations and solutions

Course Objectives

Module 1: Programming Fundamentals

Introduction to computational thinking
Variables, data types, and operators
Control structures (conditionals, loops)
Functions and methods
Parameter passing and return values
Scope and lifetime of variables
Error handling and debugging techniques
Code organization and style
Programming paradigms overview
Lab: Solving fundamental programming challenges

Module 2: Arrays and Strings

Array fundamentals and operations
Multi-dimensional arrays
String manipulation and common operations
String algorithms (pattern matching, parsing)
Array-based data structures
In-place operations and memory efficiency
Common array and string interview problems
Sliding window technique
Two-pointer approach
Lab: Implementing array and string algorithms

Module 3: Linked Lists, Stacks, and Queues

Linked list concepts and implementation
Singly and doubly linked lists
Linked list operations and traversal
Stack data structure and applications
Queue data structure and applications
Deque (double-ended queue)
Priority queues
Implementing stacks and queues
Common linked list, stack, and queue problems
Lab: Building custom implementations of these data structures

Module 4: Recursion and Dynamic Programming

Recursion fundamentals and thinking recursively
Base cases and recursive cases
Recursive problem-solving techniques
Call stack and memory implications
Introduction to dynamic programming
Memoization and tabulation approaches
Top-down vs. bottom-up implementation
Common dynamic programming patterns
Optimizing recursive solutions
Lab: Solving classic recursion and dynamic programming problems

Module 5: Trees and Graphs

Tree data structure fundamentals
Binary trees and binary search trees
Tree traversal algorithms (in-order, pre-order, post-order, level-order)
Balanced trees (AVL, Red-Black)
Graph representation (adjacency matrix, adjacency list)
Graph traversal (BFS, DFS)
Shortest path algorithms (Dijkstra’s, Bellman-Ford)
Minimum spanning tree algorithms (Prim’s, Kruskal’s)
Common tree and graph interview problems
Lab: Implementing tree and graph algorithms

Module 6: Sorting and Searching Algorithms

Basic sorting algorithms (bubble, insertion, selection)
Advanced sorting algorithms (merge sort, quick sort, heap sort)
Linear search and binary search
Sorting algorithm analysis and comparison
Stability in sorting algorithms
External sorting for large datasets
Searching in different data structures
Specialized search algorithms
Practical applications of sorting and searching
Lab: Implementing and comparing sorting and searching algorithms

Module 7: Hash Tables and Heaps

Hash table concepts and implementation
Hash functions and collision resolution
Hash table operations and performance
Common hash table applications
Heap data structure (min-heap, max-heap)
Heap operations (insertion, deletion, heapify)
Priority queue implementation using heaps
Heap sort algorithm
Common hash table and heap interview problems
Lab: Building custom hash tables and heaps

Module 8: Algorithm Analysis and Optimization

Introduction to algorithm analysis
Big O, Big Omega, and Big Theta notations
Time and space complexity analysis
Best, worst, and average case analysis
Amortized analysis
Common complexity classes
Algorithm optimization techniques
Trade-offs between time and space
Practical performance considerations
Capstone Project: Solving and optimizing complex algorithmic problems

Projects You'll Build

Data Structure Library

Build a comprehensive library of data structures with optimized implementations of linked lists, trees, graphs, and more.

Algorithm Visualizer

Create a tool that visually demonstrates how various sorting and searching algorithms work with different input sizes.

Pathfinding Application

Develop an application that implements and compares different graph algorithms for finding optimal paths in various scenarios.

Problem Solver Portfolio

Build a collection of solutions to classic programming problems and technical interview questions with detailed explanations.