(click on the Title to view presentation details)
Conda is an open source package manager, which can be used to manage binary packages and virtual environments on any platform. It is the package manager of the Anaconda Python distribution, although it can be used independently of Anaconda. We will look at how conda solves many of the problems that have plagued Python packaging in the past, followed by a demonstration of its features.
We will look at the issues that have plagued packaging in the Python ecosystem in the past, and discuss how Conda solves these problems. We will show how to use conda to manage multiple environments. Finally, we will look at how to build your own conda packages.
This talk will give a basic introduction to the pySPACE framework and its current applications.
pySPACE (Signal Processing And Classification Environment) is a modular software for the processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g., PCA, CSP, xDAWN) to established classifiers (e.g., SVM, LDA). pySPACE incorporates the concept of node and node chains of the Modular Toolkit for Data Processing (MDP) framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text-configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface.
pySPACE allows the user to connect nodes modularly and automatically benchmark the respective chains for different parameter settings and compare these with other node chains, e.g., by automatic evaluation of classification performances provided within the software. In addition, the pySPACElive mode of execution can be used for online processing of streamed data. The software specifically supports but is not limited to EEG data. Any kind of time series or feature vector data can be processed and analyzed.
pySPACE additionally provides interfaces to specialized signal processing libraries such as SciPy, scikit-learn, LIBSVM, the WEKA Machine Learning Framework, and the Maja Machine Learning Framework (MMLF).
Web page: http://pyspace.github.io/pyspace/
In the course of the 2008 Lehman and the subsequent European debt crisis, it became clear that both industry and regulators had underestimated the degree of interconnectedness and interdependency across financial assets and institutions. This type of information is especially well represented by network models, which had first gained popularity in other areas, such as computer science, biology and social sciences.
Although in its early stages, the study of network models in finance is gaining momentum and could be key to building the next generation of risk management tools and averting future financial crises. After a short overview of some of the most relevant work in the field, I will walk through (real data) examples using the pydata toolset.
Tim Berners-Lee defined the Semantic Web as a web of data that can be processed directly and indirectly by machines.
More precisely, the Semantic Web can be defined as a set of standards and best practices for sharing data and the semantics of that data over the Web to be used by applications [DuCharme, 2013].
In particular, the Semantic Web is built on top of three main pillars:
RDF (i.e., Resource Description Framework) data model, the
SPARQL query language, and the
OWL standard for storing vocabularies and
These standards allows the huge amount of data on the Web to be available in a
unique and unified standard format, contributing to the definition of the
Web of Data (WoD) .
The WoD makes the web data to be reachable and easily manageable by Semantic Web tools, providing also the relationships among these data (thus practically setting up the “Web”). This collection of interrelated datasets on the Web can also be referred to as Linked Data .
Python offers a very powerful and easy to use library to work with
RDFLib is a lightweight and functionally complete RDF library, allowing applications to access, create and manage RDF graphs in a very Pythonic fashion.
In this talk, a general overview of the main features provided by the
rdflib package will be presented.
To this end, several code examples will be discussed, along with a case study
concerning the analysis of a (semantic) social graph.
This case study will be focused on the integration between the
networkx module and the
in order to crawl, access (via
SPARQL), and analyze a
Social Linked Data Graph represented using the
FOAF (Friend of a Friend)
This talk is intended for an Novice level audience, assuming a good knowledge of the Python language.