2.0. Personal and work machines
With a work laptop or desktop, there is usually very little you can install without admin permissions.
Ideally you at least have the option over what tools you are able to use, however it may well be the case that these are predefined by IT departments.
It may also be the case that not all options of tools are enabled, or there are workflows that should work but are not enabled or there are not enough permissions granted.
There may be other analytical teams that have Python environments setup - it is likely worth having a similar setup, allowing knowledge share between teams.
Installing on personal machines
Installing tools on your own machine can be a good idea to understand how the admin side of the tools work. It also allows you to figure out how particular tools should work, and then if trying to build a similar process on a work machine you have a good idea of what is required.
Building out your own projects also means you can experiment easily with different tools and techniques and then can make recommendations at work for what would be useful for your team.
Understanding Your IT Environment
In many workplaces, especially within larger organisations or regulated industries like insurance, IT departments control what software you can install and how your machine is configured. This means you may face restrictions such as lack of admin permissions, limited access to certain websites, or disabled features that affect how tools like Python or related libraries run.
Understanding these limitations early helps set realistic expectations and guides your setup process. It also means you can plan workarounds, such as using portable Python distributions, working with virtual environments, or leveraging cloud-based development tools when local installs aren’t possible.
It’s also useful to check with your analytics or data engineering teams about their environment standards. Aligning with them can reduce compatibility issues and make collaboration smoother.
Security Considerations
Working in insurance pricing means handling sensitive and confidential data. Your development environment needs to follow security best practices to protect this data and comply with company policies.
Keep your work and personal environments separate to prevent accidental data leaks. Use encrypted storage and secure password management tools. Be cautious when installing third-party Python packages-prefer well-maintained libraries and avoid unknown sources.
When sharing code or analytical outputs, avoid embedding sensitive data directly in files. Instead, rely on secured data connections or anonymised datasets. Additionally, always keep your software up to date to patch security vulnerabilities.