The course starts with a review of time and space complexity. Analysis of algorithms and basic math is involved. We review basic data structures such as arrays, strings, linked lists, stacks, queues, and priority queues, and progress to more complex tasks involving hashes, trees and graphs. Students learn through working on a relevant problem for each section that helps them understand such data structures. They review basic algorithms and problem-solving techniques: including recursion, dynamic programming, divide and conquer, sorting and searching, and some graph algorithms. Testing is emphasized as a critical part of solution building.

Students who successfully complete this course will be well-prepared to answer questions and solve problems related to data structures and algorithms in their next job interviews.

Students must program their solutions in Python, C, C++, or Java.

Learning Outcomes
At the conclusion of the course, you should be able to

• Demonstrate the ability to measure computational and space complexity: Big O notation
• Identify and utilize the most appropriate Data Structures to solve a given problem
• Design algorithms for problems involving Sorting and Searching
• Demonstrate ability to solve Recursive problems
• Identify when it is best to use Greedy algorithms, Divide and Conquer, Dynamic Programming, and Backtracking to solve a given problem

Skills Needed: C, Java, Python or C++ as taught in the following courses: Python for Programmers, C++ Programming Comprehensive, C Programming Advanced , or Java Programming Comprehensive. Knowledge of basic data structures and algorithms are required (as taught in an undergraduate Computer Science program).