
Discover why this beginner-friendly C++ scientific programming course builds from zero to advanced with projects, challenges, exercises, video explanations for Linux computing, offering lifetime access and 30 day money-back guarantee.
Join a scientific programming school and meet the instructor as experts guide you through advanced scientific coding deb ups and Linux administration to solve real-world problems.
Learn to define constants and use operators in C++ to perform arithmetic, capture user input, and implement conditionals with relational comparisons.
Explore C++ structures, defining a student struct with name, age, and height; learn to create and access struct members, use string or char arrays, and print structured data.
Learn function templates in C++, define reusable templates with type parameters, implement add, cast, and print utilities, and build a max-of-two-numbers template for generic types.
Explore the struct versus class distinction in C++, where struct has public data by default and class has private; use struct for data containers and class for encapsulated objects.
Explore the C++11 initialization features, including initialize a list, initialize list constructors for containers, aggregate and uniform initialization, and auto type inference with for-each style vector traversal.
Learn how rvalue references enable move semantics and perfect forwarding to avoid costly copies, with move constructors, universal references, and efficient overloading.
Explore shared, weak, and unique pointers in C++: manage ownership, prevent memory leaks and cyclic references with weak pointers, use make_shared and custom deleters, and safely transfer or release ownership.
Manage resources in C++ using copy constructors, copy assignment operators, and the delete keyword. Construct in place with emplace back and use shared_ptr or unique_ptr for ownership and move semantics.
Explore the Crono library in C++11 for precision-neutral time and date handling, comparing system, steady, and high-resolution clocks, and mastering durations and time points.
Explore how operator new and operator delete allocate and free memory, overload them for dog and yellow dog, and the role of set new handler and virtual destructors.
Examine std::vector, deque, and list as sequence containers, and map and multimap as associative containers; learn iterators, algorithms, and performance tradeoffs like contiguous memory, cache locality, and splice operations.
Explore how the STL modifying algorithms change values and order, including copy, move, transform, swap, fill, remove, unique, reverse, rotate, permute, and shuffle.
Explore how reverse iterators work in the standard template library, convert to regular iterators with base, and print a vector in reverse, while noting iterator invalidation.
Explore removing elements from containers using the STL, focusing on vector and list techniques and erase-remove idiom. Understand iterator invalidation and removal for associative containers with algorithm-based and lambda-based removal.
Compare vector and deque in c++, detailing growth, reallocation, and usage rules: push front prefers deque; use vector for contiguous memory; consider data type, memory constraints, and pointer validity.
Learn to build graphs with the bushcraft adjacency list and attach vertex and edge properties. Use property maps to access these properties for algorithms like Dijkstra shortest path.
Learn how to package a factorial task into a packaged task, bind its parameter with a function object, and retrieve the result via a future in multi-threading.
Learn how OpenMP enables simple parallelization on multi-core systems, distinguish concurrent from true parallelism, and use a pragma to run code across multiple threads.
Learn how OpenMP handles data sharing before parallel regions, distinguishing shared and private data, and use firstprivate, lastprivate, default clauses and reduction to prevent race conditions and enable safe parallelization.
Discover how MPI enables distributing data across cluster servers to parallelize programs and speed up execution. Learn that MPI is a free, no-cost message passing interface API accessible to beginners.
Learn MPI simultaneous send and receive in C++, including buffer setup, data type, destination, tag, status, and a ring pattern, then introduce collective communication and broadcast.
Explore a rank-aware debug utility using variadic arguments and the underlying printf mechanism, and learn MPI collective operations like broadcast and barrier to synchronize all processes.
Overlay a two-dimensional Cartesian topology on an MPI communicator to map ranks to grid coordinates and use cart rank, cart coords, and cart shift for neighbor communication.
Explore how the gpu's parallel throughput powers data-parallel computations beyond cpu, and how kernels launch thousands of threads organized into grids, blocks, and threads on host-device systems via pci express.
Learn the kernel definition syntax with the global declaration to run on the device. Kernels return no value, use void, and threadIdx.x enables parallel indexing for operations like vector addition.
Learn to manage HPC software modules to set up software environments and run jobs on a system using module load, unload, and list commands.
Learn to write a PBS job script from a bash script using '#PBS' comments to request wall time, resources, and memory, then run your HPC commands.
Learn to write a Slurm job script named hybrid, requesting five minutes, four nodes, ten CPUs, one GB memory, loading Intel MPI and MPLX, and submit via sbatch.
The 'Scientific Programming with C++' is easiest and the most innovative and complete hands-on practical C++ course on the Udemy Platform for learning scientific and research data programming! While languages like Python and R are increasingly popular for Scientific Programming or Data sciences, C/ C++ can be a stronger choice for efficient and effective data and scientific computing. In this course, we hands-on the latest C++17 for Scientific Programming. The focus of this course lies on learning beginner to advanced programming on high-performance computers, object-oriented software design, generic or template-based programming, and the efficient implementation of numerical algorithms.
C++ is the best choice for efficient and effective programming in Research Data mining & Scientific Computing. In this course, we will hands-on the latest C++17 for Scientific Programming. Learn from the basics of C++ to the advanced and useful libraries like STL, BOOST, OpenMP and MPI! Main learning goals in this awesome course can be formulated as:
COURSE FEATURES
Get a basic concepts on the programming with C++.
Learn how to program with modern C++, using generic programming and advanced techniques, like meta programming, expression templates, and concepts.
Learn how to use programming tools and you can apply these tools to debug, benchmark, and manage your code. The list of tools include compilers, build systems, version control, debuggers, and profilers.
Learn to read, understand, and utilize (scientific) software libraries, like BLAS (Basic Linear Algebra Subroutines), LAPACK (Linear Algebra Package), STL (Standard template library), Boost (portable C++ library).
Learn how to code in HPC, using OpenMP and MPI.
There are numerous hands-on to practice the C++ programming throughout the course. Happy coding!
The focus of this course lies on aspects of software development like programming on high-performance computers, object-oriented software design, generic (template-based) programming, and the efficient implementation of numerical algorithms. Additionally experience in analysis, application and extension of software and software libraries is developed. Three main learning goals can be formulated: You know how to program with modern C++, using generic programming and advanced techniques, like meta programming, expression templates, and concepts. You know how to use programming tools and you can apply these tools to debug, benchmark, and manage your code. The list of tools include compilers, build systems, version control, debuggers, and profilers. You can read, understand, and utilize (scientific) software libraries, like BLAS (Basic Linear Algebra Subroutines), LAPACK (Linear Algebra Package), STL (Standard template library), Dune (framework for the discretization of partial differential equations), MTL4 (Matrix Template Library), Boost (portable C++ library). There will be interactive exercises to practice the C++. programming.
Based on your earlier feedback, we are introducing a Zoom live class lecture series on this course through which we will explain different aspects of the C++17. Live classes will be delivered through the Scientific Programming School, which is an interactive and advanced e-learning platform for learning scientific coding. Students purchasing this course will receive free access to the interactive version (with Scientific code playgrounds) of this course from the Scientific Programming School (SCIENTIFIC PROGRAMMING IO). Instructions to join are given in the bonus content section.
Q&A
Please use the Q&A feature on Udemy to ask questions! We'd love to talk about why regular expressions don't seem to be working, discussing decisions we made about course content, and debating regular expression philosophy. There’s no risk involved in taking this Course! This course comes with a 30-day money-back guarantee. Once you Enroll for this Course, you get lifetime access to this course and you will get all the future updates. you also get a Certification of Completion once you complete the course.
REQUIREMENTS
You will need a grasp of basic C++. It is a self-learning course with all Linux environments provided.
WHY YOU SHOULD GET THIS COURSE?
Understand programming C++ basics to the advanced C++ 17
Knowledge on developing complex C++ scientific applications
Learn about C++ libraries STL, BOOST, MPI, OpenMP
Be in a position to apply for Developer jobs, PhD and research positions requiring good C++