Relational Databases with SQL

March 10, 2016


Anyone interested in learning about structured databases for storing and organizing your data.

Relation Databases with SQL [Jim Vanderveen]

This will be a short “hands-on” introduction to the Structured Query Language (SQL) with SQLite, demonstrating basic CRUD (Create, Read, Update, Delete) operations and your new best friend, the Data Dictionary. If time permits, we’ll also cover additional topics such as NULL values, logically linking multiple tables using JOIN, and some aggregations, e.g. COUNT and SUM.

Short bio

I’ve been hacking computers since 1976 when I taught myself BASIC. I started working with SQL and relational databases circa 1995. Lately I’ve been doing database programming for the UC Davis Library, and playing with network- and database “Internet of Things” projects in my spare time.


Attendees should have followed the Software Carpentry Standard Installation on their laptops. In particular, you will need to have SQLite installed.

The linked installation instructions will setup a basic environment on your operating system of choice (Windows, Mac, Linux) that will give you access to BASH, Python, Git, and R. In addition, you will need an up-to-date web browser.

Attendees should also be familiar with a text editor on their laptops.


Lightning Talks

Please let us know if you’d like to give and informal 3-5 minute lightning talk. Post and issue or a pull request at:

Optimal Control with Direct Collocation [Jason Moore]

I’ll give a demo of some software that I’ve been developing that allows you to express optimal control problems as a high mathematical level and automatically solve trajectory optimization and parameter estimation problems.

ShareLaTeX Continuous Integration [Kenneth Lyons]

ShareLaTeX provides a continuous integration (CI) service which automatically rebuilds your LaTeX document hosted on GitHub each time you push a commit. I will briefly walk through setting up ShareLaTeX CI and demonstrate it in action.

Introduction to Stan, a probabilistic programming language for Bayesian inference. [Matt Espe]

Stan is an open source, simple programming language for creating probabilistic models and conducting inference via several methods (Hamiltonian Monte Carlo, variational inference, and optimization). Runs in C++, with interfaces to many popular analysis programs (R, Python, Julia, command-line, Stata, etc.).

Meeting Materials