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Mary Hudachek-Buswell

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.

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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.

What you'll learn

  • Learn how to differentiate between linear data structures like linkedlists, arrays, arraylists, stacks, and queues, and select the correct structure for a given situation. Analyze data structure performance with the course visualization tool.
  • Visualize and study nonlinear/hierarchical data structures such as Binary Trees, BSTs and Heaps. Examine tree operations & algorithms. Implement a HashMap that uses key-value pairs to store data. Explore probabilistic data structures like SkipLists.
  • To differentiate between complex tree data structures, like AVL and (2-4) trees, understand their self-balancing techniques and implementations. Visualize and examine various Divide and Conquer sorting algorithms along with their performance.
  • Analyze & implement various Pattern Matching algorithms from KMP to Rabin-Karp. Study essential graph traversal algorithms in order to implement Dijkstra’s Shortest Path, and construct Minimum Spanning Trees. Delve in Dynamic Programming.

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What's inside

Four courses

Data Structures & Algorithms I: ArrayLists, LinkedLists, Stacks and Queues

(47 hours)
The Data Structures & Algorithms course begins with a review of Java techniques. It introduces time complexity and explores data storage in Arrays and LinkedList nodes. Students will program low-level data structures like LinkedLists and implement ADTs like Stacks and Queues. The course examines edge cases and efficiencies, and analyzes the time complexity of linear data structures and their algorithms.

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

(47 hours)
This Data Structures & Algorithms course extends beyond linear data structures to the nonlinear and hierarchical data structures here. A short Java review is presented on topics relevant to new data structures covered in this course. The course does require prior knowledge of Java, object-oriented programming and linear data structures. Time complexity is threaded throughout the course within all the nonlinear data structures and algorithms.

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

(47 hours)
This Data Structures & Algorithms course completes the data structures portion with self-balancing AVL and (2-4) trees. It also begins the algorithm portion with a short Java review. The course requires prior knowledge of Java, object-oriented programming, and linear and nonlinear data structures. Time complexity is threaded throughout the course within all the data structures and algorithms.

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

(47 hours)
This Data Structures & Algorithms course completes the 4-course sequence with graph algorithms, dynamic programming and pattern matching solutions. A short Java review is presented on topics relevant to new data structures covered in this course. The course does require prior knowledge of Java, object-oriented programming and linear and non-linear data structures. Time complexity is threaded throughout the course within all the data structures and algorithms.

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