For years, we’ve heard that in the future, our computers will be built from subatomic particles, using the rules of quantum mechanics. Things will run faster, in parallel, at lower cost… but to be honest, I’ve usually chalked this discussion up as fantasy, or something that we might, one day, see happen, but that I won’t see in the near future. However, it turns out that quantum computing is an active area of research, one in which there have been some promising results. In this talk, William Oliver tells us about the state of quantum computing, what sorts of problems it can (or might) be used to solve, and what the challenges are moving forward.
Programmers often feel like their work is abstract, and not related to real-world, day-to-day problems. At the same time, we know that software is often used in crucial aspects of our society. In this talk, Christian Schafmeister describes the software tool that he and his colleagues wrote to create molecules — and particularly proteins, chains of amino acids that are crucial to all life on Earth. He describes his goal of making it as easy to create molecules as it is to create software, and the molecules that he and his colleagues have created using a version of Lisp on LLVM which creates molecules. If you’re interested in the real-world uses of programming, proteins, or solutions to health-care problems, then this talk will be fascinating for you.
The Julia programming language has positioned itself as a high-performance alternative to R, NumPy, and Matlab. People point to its combined easy syntax, high performance, and built-in functionality for analyzing large quantities of data. In this tutorial, David Sanders introduces Julia, showing its advantages and why people have been excited to use it.
The previous video introduced Pandas. In this relatively short video,Taavi Burns show us how we can use Pandas for a specific task, namely the analysis of logfiles.
Wes McKinney describes Pandas, a library for data analysis written in Python. Pandas sits on top of NumPy, and provides many of the same manipulation possibilities as the R language.
A long (3 hours!) but thorough introduction to NumPy and Matplotlib, by Eric Jones. If you are interested in scientific computing with Python, or if you want to learn NumPy and Matplotlib, then this will likely give you the boost you need. Notes and exercise files can be downloaded from here.