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The probability of a hypothesis, given data.

Difficulty level: Beginner
Duration: 7:57
Speaker: : Barton Poulson

Why math is useful in data science.

Difficulty level: Beginner
Duration: 1:35
Speaker: : Barton Poulson

Why statistics are useful for data science.

Difficulty level: Beginner
Duration: 4:01
Speaker: : Barton Poulson

Statistics is exploring data.

Difficulty level: Beginner
Duration: 2:23
Speaker: : Barton Poulson

Graphical data exploration

Difficulty level: Beginner
Duration: 8:01
Speaker: : Barton Poulson

Numerical data exploration

Difficulty level: Beginner
Duration: 5:05
Speaker: : Barton Poulson

Simple description of statistical data.

Difficulty level: Beginner
Duration: 10:16
Speaker: : Barton Poulson

Basics of hypothesis testing.

Difficulty level: Beginner
Duration: 06:04
Speaker: : Barton Poulson

Enabling neuroscience research using high performance computing

Difficulty level: Beginner
Duration: 39:27
Speaker: : Subha Sivagnanam
Course:

The Mouse Phenome Database (MPD) provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD ensures rigorous curation of phenotype data and supporting documentation using relevant ontologies and controlled vocabularies. As a repository of curated and integrated data, MPD provides a means to access/re-use baseline data, as well as allows users to identify sensitized backgrounds for making new mouse models with genome editing technologies, analyze trait co-inheritance, benchmark assays in their own laboratories, and many other research applications. MPD’s primary source of funding is NIDA. For this reason, a majority of MPD data is neuro- and behavior-related.

Difficulty level: Beginner
Duration: 55:36
Speaker: : Elissa Chesler

This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research. 

Difficulty level: Beginner
Duration: 57:52
Speaker: : Satrajit Ghosh

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

Difficulty level: Beginner
Duration: 1:32:17
Speaker: : Shih-Chii Liu

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

Difficulty level: Beginner
Duration: 21:15
Speaker: : Tadashi Yamazaki

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

Difficulty level: Beginner
Duration: 41:57
Speaker: : Giacomo Indiveri

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

Difficulty level: Beginner
Duration: 20:39
Speaker: : Giacomo Indiveri

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

Difficulty level: Beginner
Duration: 1:30:45
Speaker: : Maryann Martone

An agent for reproducible neuroimaging

Difficulty level: Beginner
Duration: 1:00:10
Speaker: : David Kennedy

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

Difficulty level: Beginner
Duration: 2:53
Speaker: : Barton Poulson

Primer on elementary algebra

Difficulty level: Beginner
Duration: 3:03
Speaker: : Barton Poulson

Primer on linear algebra

Difficulty level: Beginner
Duration: 5:38
Speaker: : Barton Poulson