In both cases, they would have rarely had to deal with enterprise-grade IT requirements and support. Many Data Scientists come either from academia, or come from another (non-IT) field, and "picked up" Data Science on the go. Part 2 of this blog will give you many of the reasons for this statement. If you are still in school and the data you use is both small and non-sensitive, using your own laptop probably makes the most sense.īut in most other situations, you might want to rethink this choice. If your employer or university is not providing you with a specific environment in which to perform Data Science, and all your colleagues or classmates are using their laptops, then it's pretty likely you will simply do the same. At best, they might say "Go for it, good luck!" and at worst, they might say "You're not allowed to do that." Because sometimes, it's all that is available So, yes, setting this up on your laptop is fairly easy to do and you probably don't need to call your IT support.Įven if you do reach out to your IT team, it could be that they are not familiar enough with Jupyter to be able to help you much. And most of those methods are fairly agnostic as to what Operating System your laptop is running (Windows, Linux, Mac OS). For example, this page lists a few options. There is no shortage of resources online that will walk you through setting up a Jupyter Notebook on your laptop. Because it's easy enough and gives complete freedom Let's first look at the various rationales you may (or may not) hear. Part 1: Why do Data Scientists tend to run Jupyter Notebooks on their laptops? That does not mean it will be the best strategy for the long term. Discuss some of the OpenShift-based alternatives ( part 3)ĭoing Data Science work on your laptop might be the easiest and simplest solution in the short term.Cover some of the implications of this choice ( part 2).Review why data scientists often like running Jupyter Notebooks on their laptop ( Part 1).Even so, I hope the information shared here is useful. If you are a Data Scientist, or if you manage a team of them, this blog will try to convince you that standardization is good for you, eventually.įull disclosure: as I work for the team responsible for the recently released Red Hat OpenShift Data Science cloud service, I am obviously biased.
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