Selected presentations from the 2015 Conference on Computational Science and Engineering have been captured and are available as slides with synchronized audio. In addition there are PDF’s of the slides available for printing. View presentation slides with synchronized audio. Links to the minitutorial talks available can be found below.

Python Visual Analytics for Big Data, Part I
Python Visual Analytics for Big Data, Part II
Lab Skills for Scientific Computing, Part I
Lab Skills for Scientific Computing, Part I

Sunday, March 15, 2015
Python Visual Analytics for Big Data (MT1, MT2)
Python is a powerful development, computational, and programming environment due to the wide variety of libraries developed for it, and importantly, the enthusiastic, active development and user community. One of the areas where Python excels is visualization and analysis of big data, due to several high-quality modules for both simple and advanced visual analytics. This tutorial will cover the following big-data visualization capabilities in Python: interactive plotting with IPython, matplotlib, and databases, building web visualizations with Bokeh, and Python integration with VTK and ParaView. Additional information will be provided on mapreduce and NoSQL capabilities. This tutorial is intended for intermediate-level participants who have a basic understanding of the Python language and development environment (i.e., the student ought to have an understanding of native (and ideally numpy) data structures, file I/O, and is able to develop and run simple programs). Beginner participants are welcome, but Python fundamentals, such as language constructs, “hello world,” and program execution will not be covered in this tutorial.

Organizer:  Jonathan Woodring, Los Alamos National Laboratory

Andrew Bauer, Kitware Inc., USA
Andy Terrel, Continuum Analytics, USA
Joseph Cottam, Indiana University, USA
Jonathan Woodring, Los Alamos National Laboratory, USA


Tuesday, March 17, 2015
Lab Skills for Scientific Computing (MT3, MT4)
The Software Carpentry project ( has been teaching basic computing skills to scientists and engineers since 1998. This minitutorial will introduce the tools and techniques that have proven most useful, and show how integrating them can help researchers get more done in less time, and with less pain. This two-part workshop will introduce several widely-used practices in software development, explore the empirical evidence showing their benefits (or in some cases the lack thereof), and describe how researchers and research teams can adopt them. Some of the work will be hands-on, so participants are strongly urged to bring a laptop. Warning: real-world examples may be used.

Organizer:  Greg Wilson, Software Carpentry Foundation, Canada


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