There are many paths into a data science career, and not all of them involve a strong programming background. When we hear data practitioners second-guess themselves and their abilities, a recurring theme is code: “Am I even a real data scientist if I don’t know how to [insert your own perceived coding shortcoming]?”
Well, yes, you are! Nobody knows everything; all we can hope for is to recognize the gaps in our knowledge and to find an effective way to tackle them. This week’s highlights are here to help you in your coding journey: each of them approaches the (vast) topic of programming for data science from a different angle, and each offers different types of actionable takeaways. Enjoy!
- What is good code, anyway? ’s excellent introduction to coding best practices is thorough, accessible, and detailed. It’s also a useful reminder that writing clean, clear code isn’t about showing off your technical prowess, but rather about generosity and empathy towards your colleagues and users.
- Master the essentials of logging. Another crucial aspect of being a dependable teammate? Ensuring there are good logging calls within your code so that it’s easy to debug. ’s primer on logging goes from basic functions all the way to more advanced and specialized ones, covers common issues, and includes a full implementation in Python.