Learn how to write efficient, maintainable C++ code for data-intensive applications in this hands-on course. Key application areas include scientific simulation software, statistical data processing, and computer graphics.
Data-Oriented C++ in Scientific Programming
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Software architecture using an object-oriented and a data-oriented approach
Efficient programming using language-specific features in C++
GPU and multi-core CPU software development in C++
Skills you'll gain
Details to know
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Recently updated!
June 2025
Assessments
54 assignments
Taught in English
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There are 5 modules in this course
To follow this MOOC, you should be familiar with the fundamentals of the C++ language. In this first module however, you will find a refresher on the most important concepts needed to complete the course. You will (re)discover variable types (stack variables, references, smart pointers), containers and algorithms, classes and polymorphism. If you already master these notions but your knowledge predates the C++11 standard, we still encourage you to follow this module for up-to-date recommendations on the efficient use of C++. In particular, have a look at Lesson 4 if you are not yet familiar with the notion of ranges in C++20 and later versions.
What's included
17 videos1 reading11 assignments
Part of the work of processing data efficiently consists in reducing the amount of computations and the complexity of the execution path. A complex execution path is often implemented to express design choices in the organization of software components. In the form of a compile-time execution framework, C++ offers a way to offload this complexity to the compilation stage and keep the execution environment simple and efficient.
What's included
12 videos1 reading10 assignments
Memory is one of the most critical resources of a computing platform. In this module, you will learn how memory is used in the C++ language and how memory access works on different types of machines. You will translate this knowledge into code that is at the same time clearly structured and efficient.
What's included
15 videos1 reading13 assignments
Learn to think about your program in a way that puts the critical hardware resource, memory, in the center of your thoughts. Apply these principles to discrete-event systems, a problem solving strategy with applications in all applied areas of science. Understand that data-oriented programming is not opposed to object-oriented programming but rather offers a way to model your objects in a way that accounts for your data structure, your algorithms, and your hardware.
What's included
8 videos9 assignments
This module introduces you to an amazing feature available in the C++17 and onward: the ability to parallelize an algorithm with almost no effort, using C++ parallel algorithms. You will learn that you can gain a manifold speedup with simple changes by running your code on multi-core CPUs and on GPUs. You will dive once more into the working principles of computer memories to understand how to reach substantial performance improvements, and you will link the concepts of data-oriented programming to efficient paralellism.
What's included
15 videos11 assignments
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Explore more from Algorithms
Status: Free TrialUniversity of Illinois Urbana-Champaign
Simplilearn
Status: Free TrialJohns Hopkins University
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