Data Science Design Patterns
Most 'data science' projects fall into just a few well-understood design patterns. This talk de-mystifies what those patterns are, how to use them practically, and how to get to grips with your data. We'll a look at how to understand the input/output structure of the models, how to design a reasonable 'experiment', and how to get started. We'll look at getting to grips with problems by simple data sets that can fit entirely on-screen, designing the basic 'form' of the machine before levelling up to bigger data and badder algorithms.
All of this will be shown using Python tools, libraries and running code.
Tennessee Leeuwenburg
Tennessee Leeuwenburg works has worked in scientific programming and computing for many years. He has an interest in artificial intelligence, data science, natural language generation and clean code. He has presented at PyCon AU on multiple occasions, so should at least be able to run to time.
Tennessee tweets to @tleeuwenburg and gits infrequently to https://github.com/tleeuwenburg/.
On the non-technical front, he is becoming slowly less terrible at playing guitar, using mainly a process of trial and error.
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