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For me, the key things were : - get really proficient in a language, for me it was python, it is a solid, flexible, minimal boilerplate, can use it already for

For me, the key things were : - get really proficient in a language, for me it was python, it is a solid, flexible, minimal boilerplate, can use it already for science applications (amazing libraries for it, like pandas, scipy etc) - understand databases, and how to interact with them - start testing (unit, integration and acceptance), this is super important, and key difference with scientist programming. When doing a take home test for a job interview, always include tests, even if you get the solution wrong, they will give you massive points for having tests. - start applying! You'll fail a lot, but you gain experience with each test and interview you do.

MereInterest 10y ago
Thank you. I've been primarily working with C++ and Python, and consider myself proficient in both, so it is good to hear that Python is usable. I've been meaning to work more with databases, as I haven't interacted with them in particular. Maybe writing a mini-library catalog, as my bookshelves are starting to outgrow the haphazard google spreadsheet where their contents are currently recording. And yeah, testing is something that I try to do in my home projects, but 95% of the coding for research is throw-away code, and so I don't get much of a chance there.
- walrus1066 10y ago
  C++ and python are a suburb combo, they complement each other really well. If you master both, by that I mean not just the syntax, but the different patterns and styles you need to employ in static vs dynamic typed, understand the tradeoffs of both, you've pretty much mastered programming. Python also glues v. nicely with C, if a part of your code is v. CPU heavy, you can write it with cython, and then call the compiled code from python, as you would a normal python function, and it would run just as fast as native c. This is how all those python data libraries work under the hood (pandas, sklearn etc) Library catalogue is a great idea for DB learning, you'll learn how to model data, and the relations between them, using tables, foreign keys, primary keys etc. I'd recommend postgres for the DB. Again, python would be an excellent language for this, you can use sqlalchemy to define your tables, and query them. You can then use flask to provide a web interface for the catalogue.

MereInterest 10y ago

Thank you. I've been primarily working with C++ and Python, and consider myself proficient in both, so it is good to hear that Python is usable. I've been meaning to work more with databases, as I haven't interacted with them in particular. Maybe writing a mini-library catalog, as my bookshelves are starting to outgrow the haphazard google spreadsheet where their contents are currently recording. And yeah, testing is something that I try to do in my home projects, but 95% of the coding for research is throw-away code, and so I don't get much of a chance there.

walrus1066 10y ago
C++ and python are a suburb combo, they complement each other really well. If you master both, by that I mean not just the syntax, but the different patterns and styles you need to employ in static vs dynamic typed, understand the tradeoffs of both, you've pretty much mastered programming. Python also glues v. nicely with C, if a part of your code is v. CPU heavy, you can write it with cython, and then call the compiled code from python, as you would a normal python function, and it would run just as fast as native c. This is how all those python data libraries work under the hood (pandas, sklearn etc) Library catalogue is a great idea for DB learning, you'll learn how to model data, and the relations between them, using tables, foreign keys, primary keys etc. I'd recommend postgres for the DB. Again, python would be an excellent language for this, you can use sqlalchemy to define your tables, and query them. You can then use flask to provide a web interface for the catalogue.