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- Scientific Computing

- MATLAB Programming for Engineers and Scientists: Vanderbilt University
- Differential Calculus through Data and Modeling: Johns Hopkins University
- Julia Scientific Programming: University of Cape Town
- Mastering Programming with MATLAB: Vanderbilt University
- C Programming: Language Foundations - 2: Dartmouth College
- C Programming: Modular Programming and Memory Management - 3: Dartmouth College
- C Programming: Pointers and Memory Management - 4: Dartmouth College
- C Programming: Advanced Data Types - 5: Dartmouth College
- Linux Basics: The Command Line Interface - 6: Dartmouth College
- C Programming: Using Linux Tools and Libraries - 7: Dartmouth College

Scientific computing is the high-powered, research-focused application of computing theories and methodologies to solve scientific problems. Another way to view scientific computing is that it is the intersection point between computer science, numerical mathematics, and modeling. In scientific computing, there are a variety of tools, techniques, and theories used to analyze science, engineering, and mathematical computer models of problems. This field of study is also known as computational science. With either definition, it is an area of science that encompasses many different disciplines while using advanced computing capabilities to understand and solve problems with natural systems.

Scientific computing is valuable to learn as many of the concepts explored in this area deal with futuristic ways to use computers. One of these ways is due to the increased power of supercomputers in recent years. As a result, it has become easier for computer scientists to use scientific computing to create complex simulations that were previously unimaginable, such as particle physics. Someone who has learned computing through networking, computer hardware, programming, databases, or algorithms could have the right mix of skills and insights to learn scientific computing

Typical career opportunities that can arise from learning scientific computing include working as a data scientist, cloud operations specialist, scientific programmer/modeler, data engineer, or DevOps engineer. Additional roles are in data acquisition, ingestion, and research integration as a computational scientist. Other roles involve creating user interfaces and back-end services to automate the scientific output of multi-institutional science research projects.

When you take online courses about scientific computing, you can learn the fundamentals of science research and the mathematical methodologies involved in this scientific approach. You may also be exposed to data analysis as it relates to mathematical modeling, along with related knowledge originally developed within mathematics. Today, this set of mathematical techniques and theories is a major part of scientific computing known as numerical analysis. Taking online courses can help you learn more about these data-rich fields, as well as newer, advanced technologies like Databricks and Apache Spark that scientific computing experts use in large research projects.

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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