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Data Structures and Algorithms

Georgia Institute of Technology via edX Professional Certificate


Great code has its foundation built upon data structures and algorithms. One must have a deep understanding of how data structures operate and designing efficient algorithms. Implementing algorithmic techniques that efficiently manipulate data structures is the essence of this program.

The Data Structures and Algorithms Professional Certificate from GTx is a four-course series covering the foundations of data structures, and designing efficient algorithms. The learner will examine and implement the principles of data storage in low-level data structures such as LinkedLists, Stacks and Queues. The learner will understand the significance of Abstract Data Types (ADTs). The fundamentals of recursion, edge cases, and algorithmic efficiency are emphasized throughout the four-course series. The series transitions from linear data structures to nonlinear data structures. The learner will explore Binary Search Trees (BSTs), HashMaps and Heaps. Implementations of the depth-first search (dfs) and breadth-first search (bfs) traversal algorithms are presented. Higher order data structures, like AVL and 2-4 trees, delves into self-balancing algorithmic techniques. Computer scientists must have a thorough understanding of time complexity in order to write efficient algorithms. The 3rd & 4th courses focus on efficiency by first reviewing iterative sorting algorithms, bubble sort, and then implementing optimizations applied the sorting algorithm which improves performance. Divide and conquer algorithms, such as merge sort, quicksort and radix sort, are explained. The series wraps up with the graph ADT that utilizes many lower level data structures as auxiliary data storage in order to implement Dijkstra’s shortest path and Minimum Spanning Tree (MST) algorithms.

Georgia Tech’s undergraduate computer science program is ranked #5 in U.S. The Professional Certificate for this program uses the same instructional materials and assessments as this on-campus accredited CS 1332 course, giving you a Georgia Tech-caliber learning experience with data structures & algorithms in computing. The Data Structures and Algorithms series of courses uses the Java object-oriented programming language which remains one of the most popular languages among software developers. Short (3-5 minute) videos and visualization exploratory labs are just part of the instructional tools used to deliver the content in this program. Students completing this program exit with the same learning outcomes as the traditional Georgia Tech on-campus course.


Courses under this program:
Course 1: Data Structures & Algorithms I: ArrayLists, LinkedLists, Stacks and Queues

Work with the principles of data storage in Arrays, ArrayLists & LinkedList nodes. Understand their operations and performance with visualizations. Implement low-level linear, linked data structures with recursive methods, and explore their edge cases. Extend these structures to the Abstract Data Types, Stacks, Queues and Deques.

Course 2: Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps

Become familiar with nonlinear and hierarchical data structures. Study various tree structures: Binary Trees, BSTs and Heaps. Understand tree operations and algorithms. Learn and implement HashMaps that utilize key-value pairs to store data. Explore probabilistic data structures like SkipLists. Course tools help visualize the structures and performance.

Course 3: Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms

Learn more complex tree data structures, AVL and (2-4) trees. Investigate the balancing techniques found in both tree types. Implement these techniques in AVL operations. Explore sorting algorithms with simple iterative sorts, followed by Divide and Conquer algorithms. Use the course visualizations to understand the performance.

Course 4: Data Structures & Algorithms IV: Pattern Matching, Dijkstra’s, MST, and Dynamic Programming Algorithms

Delve into Pattern Matching algorithms from KMP to Rabin-Karp. Tackle essential algorithms that traverse the graph data structure like Dijkstra’s Shortest Path. Study algorithms that construct a Minimum Spanning Tree (MST) from a graph. Explore Dynamic Programming algorithms. Use the course visualization tool to understand the algorithms and their performance.


Taught by

Mary Hudachek-Buswell

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