Advanced Programming in C++

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Free Online Course: Advanced Programming in C++ provided by edX is a comprehensive online course, which lasts for 8 weeks long, 7-9 hours a week. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from edX. Advanced Programming in C++ is taught by Aspen Olmsted.

Overview
  • This is the 2nd course in the intermediate undergraduate-level offering in the larger Programming and Data Structures MicroBachelors program. We recommend taking them in order, unless you have a background in these areas already and feel comfortable skipping ahead.

    1. Introduction to Programming in C++
    2. Advanced Programming in C++
    3. Introduction to Data Structures
    4. Advanced Data Structures

    These topics build upon the learnings that are taught in the introductory-level Computer Science Fundamentals MicroBachelors program, offered by the same instructor.

    This is a self-paced course that continues in the development of C++ programming skills. Among the topics covered is the development of more advanced command-line programs that utilize functions, arrays, and strings to solve problems. Students learn to program in C++ through the lectures and the labs. C++ programming material is presented over eight weeks of interactive lectures with bi-weekly quizzes to assess your understanding of the material Students will experience hands-on practice writing C++ programs through twelve lab challenges.

    Students will not only learn to use these more advanced procedural programming functionalities. They will also learn to analyze the computational complexity of their code. Computational complexity theory focuses on classifying computational problems according to their inherent difficulty and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by the mechanical application of mathematical steps, such as an algorithm.

    A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition by introducing mathematical models of computation to study these problems and quantifying their computational complexity, i.e., the number of resources needed to solve them, such as time and storage.

Syllabus
  • Wk 1 - Function Analysis

    • Primality Testing
    • Runtime Analysis
    • Order of Growth
    • Asymptotic Analysis

    Wk 2 - Function Implementation

    • k-Combinations Problem
    • Flow of a Program
    • Runtime Stack Execution
    • Scope of Variables
    • Pass By Value
    • Parameter Passing

    Wk 3 - Function Labs

    Wk 4 - Arrays

    • Motivation
    • Computing the Average
    • Above The Average

    Wk 5 - Array Labs

    Wk 6 - Strings

    • Concatenating
    • Indexing
    • Slicing
    • Length
    • Comparing
    • Starting Index

    Wk 7 - String Labs

    Wk 8 - Final Exam