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> With that attitude, I would never, ever, ever, hire you. There are two separate issue here though. (A) How much work does a given person want to put in (B)

by rmtech 6y ago

> With that attitude, I would never, ever, ever, hire you. There are two separate issue here though. (A) How much work does a given person want to put in (B) How much work does a given tool require. It can simultaneously be the case that git is bad/overcomplicated AND that you should only hire people who bother to learn it really well. Why? Well, learning hard things is a reliable signal of diligence and hard work, which are generally useful traits. But at the same time, forcing everyone to learn something annoying and time-consuming just as a test of grit isn't maximally efficient. The same effort could be put into more productive tasks. > Why did you go into software engineering? Well, I'm not a software engineer - in the Data/ML area so I am much more interested in the properties of data than the properties of code. But having said that I certainly like clean, efficient code and I care about languages (maybe just spoiled by python?!). I can't see myself as a software engineer so I think your instinct is right. My passion is data and ML.

wegs 6y ago
It's not a test of grit. git happens to exemplify -- as well as any system I know -- many aspects of good data engineering. If you're into data and ML, those are things you ought to know too. For a data/ML position, in most cases, I'd expect you to be able to handle data cleanly and efficiently. If you can't, there are jobs far over on the data side, but: 1) As a business data analyst, you're fine with Excel and PPT, but you'll be paid roughly 1/3 of an ML/SWE position, and you should have excellent communication skills. 2) There are primary mathematical positions, where you work with a data engineer, but you'd better be awesome at math. AND it still helps to be able to handle data cleanly. Even so, good data workflows require knowing what you did, when, and to which version of data. Properly used, git provides an archival log of some of that. I use very similar data structures when I build some of my own data pipelines too, with data stored under its hashes, Merkle trees, DAGs, and similar. If you find that "annoying and time-consuming," I'd hire you for a business data analyst, and not much more. It sounds like you find that stuff boring, though. It's a test of interest, passion, and drive, much more so than diligence and grit. Although those are important too.
- rmtech 6y ago
  A good data management system (with hashes of datasets etc) is great. Data mess is not fun. Merkle trees are also fun and have applications elsewhere like cryptocurrency/blockchain. I don't have a problem with computer science in general, it's a fascinating subject.

by rmtech 6y ago

wegs 6y ago

It's not a test of grit. git happens to exemplify -- as well as any system I know -- many aspects of good data engineering. If you're into data and ML, those are things you ought to know too. For a data/ML position, in most cases, I'd expect you to be able to handle data cleanly and efficiently. If you can't, there are jobs far over on the data side, but: 1) As a business data analyst, you're fine with Excel and PPT, but you'll be paid roughly 1/3 of an ML/SWE position, and you should have excellent communication skills. 2) There are primary mathematical positions, where you work with a data engineer, but you'd better be awesome at math. AND it still helps to be able to handle data cleanly. Even so, good data workflows require knowing what you did, when, and to which version of data. Properly used, git provides an archival log of some of that. I use very similar data structures when I build some of my own data pipelines too, with data stored under its hashes, Merkle trees, DAGs, and similar. If you find that "annoying and time-consuming," I'd hire you for a business data analyst, and not much more. It sounds like you find that stuff boring, though. It's a test of interest, passion, and drive, much more so than diligence and grit. Although those are important too.

rmtech 6y ago
A good data management system (with hashes of datasets etc) is great. Data mess is not fun. Merkle trees are also fun and have applications elsewhere like cryptocurrency/blockchain. I don't have a problem with computer science in general, it's a fascinating subject.