There are two primary factors to consider when choosing a data science workstation: which tools and techniques you use the most and the size of your data sets.
When it comes to data science frameworks, higher core counts don’t always translate into better performance. NumPy, SciPy, and scikit-learn don’t scale well past 18 cores. On the other hand, HEAVY.AI (formerly OmniSci) will take all the cores it can get.
All of the Intel-based data science workstations use Intel Core, Intel Xeon W, and Intel Xeon Scalable processors that excel at data science workloads in real-world tests. You’ll get best-in-processor-family performance from all of them, which makes memory capacity your most important choice.
Data science frameworks balloon memory footprints two to three times. To get your baseline memory needs, examine your typical data sets and multiple by three. If you can work with 512 GB or less, you can get excellent performance in a desktop machine. If your data sets tend to be above 500 GB, you’ll want a tower with 1.5 TB of memory or more.