This tutorial describes the dbSNP resources in the UCSC Genome Browser, including display conventions and the subdivision of the data into several useful subset tracks, especially the Common SNPs. There is also a discussion about changes to the genome assemblies from one version to another, and of two ways to navigate between different assemblies of the human genome in the Browser.

This tutorial demonstrates the UCSC Genome Browser Data Integrator, a tool that allows combination and intersection of data from up to five primary tables. In the example, data are extracted showing SNPs, genes, and phenotypes from a genomic region.

This tutorial shows how to obtain coordinates of genes, then input those coordinates into the UCSC Genome Browser for display. The regions do not have to be continuous in the genome.

This tutorial demonstrates the Multi-Region Exon-Only Display mode of the UCSC Genome Browser.

This tutorial demonstrates viewing alternate haplotypes with the UCSC Genome Browser.

The Genome Browser in the Cloud (GBiC) program is a convenient tool that automates the setup of a UCSC Genome Browser mirror​ on a cloud instance or a dedicated physical server.

This tutorial gives a demonstration of species/genome assembly selection page (Gateway) on the UCSC Genome Browser.

This tutorial demonstrates how to get the coordinates and sequences of exons using the UCSC Genome Browser.

This tutorial will demonstrate how to locate amino acid numbers for coding genes using the UCSC Genome Browser.

This tutorial will demonstrate how to find the tables in the UCSC database that are associated with the data tracks in the Genome Browser graphical viewer.

This tutorial shows how to navigate between exons of a gene using the UCSC Genome Browser.

JupyterHub is a simple, highly extensible, multi-user system for managing per-user Jupyter Notebook servers, designed for research groups or classes. This lecture covers deploying JupyterHub on a single server, as well as deploying with Docker using GitHub for authentication.

This demonstration walks through how to import your data into MATLAB.

This lesson provides instruction regarding the various factors one must consider when preprocessing data, preparing it for statistical exploration and analyses. 

This tutorial outlines, step by step, how to perform analysis by group and how to do change-point detection.

This tutorial walks through several common methods for visualizing your data in different ways depending on your data type.

This tutorial illustrates several ways to approach predictive modeling and machine learning with MATLAB.

This brief tutorial goes over how you can easily work with big data as you would with any size of data.

In this tutorial, you will learn how to deploy your models outside of your local MATLAB environment, enabling wider sharing and collaboration.

The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.