C++ Data Structures and Algorithm Design Principles

Course Description

C++ is a mature multi-paradigm programming language that enables you to write high-level code with great control over the hardware. This course updates your working knowledge of modern C++ by showing how you can use algorithms and data structures to build scalable, efficient modern applications.

Overview

C++ is a mature multi-paradigm programming language that enables you to write high-level code with a high degree of control over the hardware. Today, significant parts of software infrastructure, including databases, browsers, multimedia frameworks, and GUI toolkits, are written in C++.

This course starts by introducing C++ data structures and how to store data using linked lists, arrays, stacks, and queues. In later chapters, the course explains the basic algorithm design paradigms, such as the greedy approach and the divide-and-conquer approach, which are used to solve a large variety of computational problems. Finally, you will learn the advanced technique of dynamic programming to develop optimized implementations of several algorithms discussed in the course.

By the end of this course, you will have learned how to implement standard data structures and algorithms in efficient and scalable C++ 14 code.

After completing this course, you will be able to:

• Build applications using hash tables, dictionaries, and sets
• Implement a URL shortening service using a bloom filter
• Apply common algorithms such as heapsort and merge sort for string data types
• Use C++ template metaprogramming to write code libraries
• Explore how modern hardware affects the actual run-time performance of programs
• Use appropriate modern C++ idioms like std::array instead of C-style arrays

Course Length

3 days

Scope

This fast-paced course is designed to teach you concepts quickly through descriptive graphics and challenging exercises. The course will have "call-outs," with key take away points and the most common pitfalls to keep you interested, while breaking up the topics into manageable sections.

Target Audience

This course is for developers or students who want to revisit basic data structures and algorithm design techniques. Although no mathematical background is required, basic knowledge of complexity classes and Big O notation along with a qualification in an algorithms course will help you get the most out of this course. Familiarity with C++ 14 standard is assumed.

Technical Requirements

Hardware:

For the optimal student experience, we recommend the following hardware

configuration:

• Any entry-level PC/Mac with Windows, Linux, or macOS is sufficient
• Processor: Intel Core 2 Duo, Athlon X2, or better
• Memory: 4 GB RAM
• Storage: 10 GB available space

Software:

We also recommend that you have the following software installed in advance:

• Operating system: Windows 7 SP1 32/64-bit, Windows 8.1 32/64-bit or Windows 10 32/64-bit, Ubuntu 14.04 or later, or macOS Sierra or later
• Browser: Google Chrome or Mozilla Firefox
• Any modern compiler and IDE (optional) that supports the C++ 14 standard

Course Outline

Lesson 1: Lists, Stacks, and Queues

• Contiguous Versus Linked Data Structures
• std::array
• std::vector
• std::forward_list
• Iterators
• std::list
• std::deque
• Container Adapters
• Benchmarking

Lesson 2: Trees, Heaps, and Graphs

• Non-Linear Problems
• Tree – It's Upside Down!
• Variants of Trees
• Heaps
• Graphs

Lesson 3: Hash Tables and Bloom Filters

• Hash Tables
• Collisions in Hash Tables
• C++ Hash Tables
• Bloom Filters

Lesson 4: Divide and Conquer

• Binary Search
• Understanding the Divide-and-Conquer Approach
• Sorting Using Divide and Conquer
• C++ Standard Library Tools for Divide and Conquer
• Dividing and Conquering at a Higher Abstraction Level – MapReduce

Lesson 5: Greedy Algorithms

• Basic Greedy Algorithms
• The Knapsack Problem(s)
• The Vertex Coloring Problem

Lesson 6: Graph Algorithms I

• The Graph Traversal Problem
• Prim's MST Algorithm
• Dijkstra's Shortest Path Algorithm

Lesson 7: Graph Algorithms II

• Revisiting the Shortest Path Problem
• The Bellman-Ford Algorithm
• The Bellman-Ford Algorithm (Part II) – Negative Weight Cycles
• Johnson's Algorithm
• Strongly Connected Components
• Kosaraju's Algorithm
• Choosing the Right Approach

Lesson 8: Dynamic Programming I

• What Is Dynamic Programming?
• Memoization – the Top-Down Approach
• Tabulation – the Bottom-Up Approach
• Subset Sum Problem
• Dynamic Programming on Strings and Sequences

Lesson 9: Dynamic Programming II

• An Overview of P Versus NP
• Reconsidering the Subset Sum Problem
• The Knapsack Problem
• Graphs and Dynamic Programming