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Goal of repository

The aim of this repository is to teach skills in practical data analysis through a case study approach. Each module begins with a motivating dataset and then proceeds with hands-on methods to analyze these data. In this way, we hope to motivate exploration of data analysis methods. Although we do provide some mathematical discussions, this repository does not provide a deep exploration of mathematical or statistical theory; for that, we have included references throughout.

Organization of repository

There are multiple paths through this repository.

• If you’ve never used Python before and are new to data analysis, we recommend beginning with

  • Introduction to Python

If you have some MAT- LAB experience, you might narrow your focus by data type, either neural field data (chap- ters 2–7) or spike train data (chapters 8–10). Or, you might narrow your focus by method type, namely, spectral analysis (chapters 3, 4, 10), generalized linear models (chapters 9, 10), coherence (chapters 5 and 11), or cross-frequency coupling (chapter 7). Each chapter consists of a separate case study and may be selected a` la carte for targeted investigation.