Introduction to Data Structures and Algorithms

Course Description
💡Ever wondered how large datasets are organized efficiently, how search happens instantly, or how systems manage millions of operations seamlessly? 
🛠️ The answer lies in well-designed data structures and algorithms—the fundamental building blocks that power modern computing. 

💼 What the course offers: This is a graduate-level course designed to learn how to think algorithmically and use the right data structures to solve computational problems. We explore a range of useful data structures and elegant algorithmic paradigms, learning how to analyze their complexity and correctness.

🎓 What You’ll Gain: a strong foundation in data structures and core algorithmic principles that every computer scientist must master. You’ll develop the skills to design efficient programs, reason rigorously about performance, and tackle complex computational problems with confidence. Whether you're heading into research, software engineering, or the startup world, this graduate-level course sets you up with crucial and foundational skills. 

Topics 

Methods for expressing and comparing the time and space complexity of algorithms. Solving problems using core data structures such as arrays, linked lists, stacks, queues, hash tables, heaps, trees, and graphs. Algorithmic design paradigms: greedy, dynamic programming, and divide and conquer. Application of data structures, algorithmic techniques, and complexity analysis to a variety of problems, including allocation, searching, sorting, scheduling, packing, and graph problems (including bipartite matching, shortest path, and minimum spanning tree). NP and P problems (knapsack, satisfiability, vertex cover). NP Completeness and Reductions. 

Grade Policy
The final grade for the course will be based on the following weights:

• 30% Quizzes

• 20% Mid-term 1 

• 20% Mid-term 2

• 30% Final exam

• +5 Bonus points (based on class participation, at the discretion of the Instructor and TA)