The probability of a hypothesis, given data.

Why math is useful in data science.

Why statistics are useful for data science.

Statistics is exploring data.

Graphical data exploration

Numerical data exploration

Simple description of statistical data.

Basics of hypothesis testing.

Lecture on the most important concepts in software engineering

This lecture covers modeling the neuron in silicon, modeling vision and audition and sensory fusion using a deep network. 

Presentation of a simulation software for spatial model neurons and their networks designed primarily for GPUs.

Presentation of past and present neurocomputing approaches and hybrid analog/digital circuits that directly emulate the properties of neurons and synapses.

Presentation of the Brian neural simulator, where models are defined directly by their mathematical equations and code is automatically generated for each specific target.

This lecture covers structured data, databases, federating neuroscience-relevant databases, ontologies. 

A basic introduction to clinical presentation of schizophrenia, its etiology, and current treatment options.

This lecture covers describing and characterizing an input-output relationship.

This tutorial talks about how to upload and version your data in OpenNeuro.org

This tutorial shows how to share your data in OpenNeuro.org

This tutorial shows how to run analysis in OpenNeuro.org

Inferring results from incomplete data