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Students – and not a few staff – come to urban data science from a wide range of backgrounds and with vastly different levels of experience of programming and collaboration. While diversity is good from an ecosystem standpoint, for a new Masters or PhD student it can be hard to know where to begin: R or Python? LaTeX or Markdown? Git or SVN? MySQL or Postgres? This talk will draw on experience of both professional software development and research hacking, incorporating examples from the speaker’s research, to offer one perspective on tools and workflows that help you to pick the right tool for the job, that help to get things done, and that help you to recover when things (inevitably) go wrong. This talk will provide the start of a discussion, not the final answer.