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.

Enabling neuroscience research using high performance computing

This lecture covers an introduction to neuroinformatics and its subfields, the content of the short course and future neuroinformatics applications.

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. 

Introduction to the Mathematics chapter of Datalabcc's "Foundations in Data Science" series.

Primer on elementary algebra

Primer on linear algebra

Primer on systems of linear equations