Accelerated Computer Science Fundamentals Specialization

Data Structures and Algorithms in C++. Learn fundamentals of computer science while implementing efficient data structures in C++.

Instructor: Wade Fagen-Ulmschneider

What you'll learn

  •   Design and implement an object-oriented program in the C++ language, including defining classes that encapsulate data structures and algorithms.
  •   Select and implement appropriate data structures that best utilize resources to solve a computational problem.
  •   Analyze the running time and space needs of an algorithm, asymptotically to ensure it is appropriate at scale, including for big data.
  •   Prepare for advanced courses in CS with the foundational knowledge of object data structures needed to implement and call advanced library functions.

    • Skills you'll gain

  •   C++ (Programming Language)
  •   Computer Programming
  •   Development Environment
  •   Algorithms
  •   Object Oriented Programming (OOP)
  •   Engineering Software
  •   Data Storage
  •   Object Oriented Design
  •   Graph Theory
  •   Debugging
  •   Data Structures

    • Specialization - 3 course series

    Students will solve weekly “challenge problems” by implementing fragments of C++ code in a web-based interface. Each course also features two longer-format coding projects where students will follow instructions to complete a C++ program that performs tasks such as editing images, efficiently manipulating data structures, or solving a graph search problem.

    This course teaches learners how to write a program in the C++ language, including how to set up a development environment for writing and debugging C++ code and how to implement data structures as C++ classes. It is the first course in the Accelerated CS Fundamentals specialization, and subsequent courses in this specialization will be using C++ as the language for implementing the data structures covered in class.

    In this course, you will learn new data structures for efficiently storing and retrieving data that is structured in an ordered sequence. Such data includes an alphabetical list of names, a family tree, a calendar of events or an inventory organized by part numbers. The specific data structures covered by this course include arrays, linked lists, queues, stacks, trees, binary trees, AVL trees, B-trees and heaps. This course also shows, through algorithm complexity analysis, how these structures enable the fastest algorithms to search and sort data.

    The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.

    Ordered Data Structures

    Unordered Data Structures

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