Save for later

Data Structures & Algorithms IV

Data Structures and Algorithms,

This Data Structures & Algorithms course completes the 4-course sequence of the program 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, linear and non-linear data structures. Time complexity is threaded throughout the course within all the data structures and algorithms.

You will delve into the Graph ADT and all of its auxiliary data structures that are used to represent graphs. Understanding these representations is key to developing algorithms that traverse the entire graph. Two traversal algorithms are studied: Depth-First Search and Breadth-First Search. Once a graph is traversed then it follows that you want to find the shortest path from a single vertex to all other vertices. Dijkstra’s algorithm allows you have a deeper understanding of the Graph ADT. You will investigate the Minimum Spanning Tree (MST) problem. Two important, greedy algorithms create an MST: Prim’s and Kruskal’s.

Prim’s focuses on connected graphs and uses the concept of growing a cloud of vertices. Kruskal’s approaches the MST differently and creates clusters of vertices that then form a forest.

The other half of this course examines text processing algorithms. Pattern Matching algorithms are crucial in everyday technology. You begin with the simplest of the algorithms, Brute Force, which is the easiest to implement and understand. Boyer-Moore and Knuth-Morris-Pratt (KMP) improve efficiency by using preprocessing techniques to find the pattern. However, KMP does an exceptional job of not repeating comparisons once the pattern is shifted. The last pattern matching algorithm is Rabin-Karp which is an “out of the box” approach to the problem. Rabin-Karp uses hash codes and a “rolling hash” to find the pattern in the text. A different text processing problem is locating DNA subsequences which leads us directly to Dynamic Programming techniques. You will break down large problems into simple subproblems that may overlap, but can be solved. Longest Common Subsequence is such an algorithm that locates the subsequence through dynamic programming techniques.

What you'll learn

  • Improve Java programming skills by implementing graph and dynamic programming algorithms
  • Study techniques algorithms for finding patterns in text processing
  • Use preprocessing in the Boyer-Moore and KMP algorithms
  • Explore the problem with hash codes in the Rabin-Karp algorithm
  • Understand the Graph ADT and its representations within auxiliary structures
  • Traverse graphs using the Depth-First and Breadth-First Search algorithms
  • Investigate Dijkstra’s Shortest Path algorithm which operates on weighted graphs
  • Study the Minimum Spanning Tree (MST) problem and its characteristics
  • Utilize Greedy algorithms, like Prim’s and Kruskal’s, to find the MST
  • Decompose large problems using Dynamic Programming techniques
  • Apply Dynamic Programming techniques in the Longest Common Subsequence algorithm
  • Review of important Java principles involved in object-oriented design
  • The Iterator & Iterable design patterns, and the Comparable & Comparator interfaces
  • Basic “Big-Oh” notation and asymptotic analysis
  • Examine algorithms for text processing, the simplest being Brute Force
  • Apply preprocessing techniques in Boyer-Moore to improve performance
  • Knuth-Morris-Pratt (KMP) avoids waste in prefixes of the pattern to achieve the best runtime
  • Approach the pattern matching problem from the perspective of hash codes in Rabin-Karp
  • Consider the time complexity of each of the algorithms
  • Explore the Graph ADT and its representation in auxiliary data structures
  • Implement the Depth-First Search and Breadth-First Search graph traversal algorithms
  • Examine weighted graphs and Dijkstra’s shortest path algorithm which uses edge relaxation
  • Study weighted, undirected graphs to find Minimum Spanning Trees (MST)
  • Apply greedy algorithms to solve the MST problem
  • Prim’s algorithm operates on connected graphs and employs the concept of cloud
  • Approach the MST problem with Kruskal’s algorithm using cluster and forest concepts
  • Apply the Dynamic Programming techniques that focus on the subproblems
  • Examine the components of a dynamic programming algorithmic solution
  • Implement the Longest Common Subsequence algorithm to solve DNA

Get Details and Enroll Now

OpenCourser is an affiliate partner of edX and may earn a commission when you buy through our links.

Get a Reminder

Send to:
Rating Not enough ratings
Length 5 weeks
Effort 9 - 10 hours per week
Starts On Demand (Start anytime)
Cost $149
From The Georgia Institute of Technology via edX
Instructor Mary Hudachek-Buswell
Download Videos On all desktop and mobile devices
Language English
Subjects Programming
Tags Computer Science

Get a Reminder

Send to:

Similar Courses

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Programming Coordinator 1 $54k

Programming Acquisitions $61k

CNC Programming $70k

Programming advisor $72k

Network Programming $78k

NC Programming $79k

dynamic CRM Developer $90k

DBA - Programming $103k

DDM - Dynamic Data Masking $111k

Microsoft Dynamic Developer $115k

IT and Programming Specialist $122k

Programming Producer $129k

Write a review

Your opinion matters. Tell us what you think.

Rating Not enough ratings
Length 5 weeks
Effort 9 - 10 hours per week
Starts On Demand (Start anytime)
Cost $149
From The Georgia Institute of Technology via edX
Instructor Mary Hudachek-Buswell
Download Videos On all desktop and mobile devices
Language English
Subjects Programming
Tags Computer Science

Similar Courses

Sorted by relevance

Like this course?

Here's what to do next:

  • Save this course for later
  • Get more details from the course provider
  • Enroll in this course
Enroll Now