As you know, at CFDSUPPORT, we are obsessed with the accuracy of CFD results. Accuracy is our highest priority. We believe that every successful engineering project is a result of focus, skills, experience, patience, and dedication (not random actions in a rush). We constantly keep tracking the accuracy and create comparisons of CFD with real physical measurements. Have a look at this Centrifugal Compressor Benchmark. This project was undertaken jointly by CFDSUPPORT in collaboration with CZ a.s. (compressor manufacturer). The aim of the benchmark validation was to evaluate the TCFD software and compare its simulation results with the compressor measurement data.

Open-source software is a significant topic in technology and affects all kinds of human activity. This also applies to engineering. Open-source for virtual prototyping has strong pros and cons and no wonder its reputation is very contradictory among engineers and even more among managers. While open-source is typically well accessible, flexible, and ready for further development, it is often missing a user interface, professional services like technical support, best practices, benchmarks, documentation, training, and a clear roadmap of its future development. The goal of this article is to make a summary of various aspects of using open-source for engineering and share 15 years of experience with the open-source CAE and virtual prototyping. We have chosen a few most significant aspects that had proven to be critical for simulation software users.

Mathematics is an integral part of data science. Any practicing data scientist or person interested in building a career in data science will need to have a strong background in specific mathematical fields. Depending on your career choice as a data scientist, you will need at least a B.A., M.A., or Ph.D. degree to qualify for hire at most organizations. A significant portion of your ability to translate your data science skills into real-world scenarios depends on your success and understanding of mathematics. Data science careers require mathematical study because machine learning algorithms, and performing analyses and discovering insights from data require math. While math will not be the only requirement for your educational and career path in data science, but it’s often one of the most important. Identifying and understanding business challenges and translating them into mathematical ones is widely considered one of the most important steps in a data scientist’s workflow. Will you be a data scientist, machine learning engineer, business intelligence developer, data architect, or another industry specialist? Maybe you don’t yet know the exact path you will take in your data science career. But take a look at the various types of mathematical requirements and what they are used for in data science. You will have a better understanding of your skills and interests and can ultimately better pursue your choice of mathematical education. Let’s start by taking a look at the different types of math used in data science so that you have a better idea of what you really need to know when it comes to mathematics and your data science career.