The goal of this activity is for the students to implement the method of knowledge extraction from twitter comments, using proper algorithms and their supported libraries.
The course provides the students an introduction to the scientific problem solving environments (PSE), their software engineering characteristics, programming interface, and supported libraries..The course considers as case studies the focus on the MatLab, Sage, and Python PSEs. A comparison study of these PSEs is presented for various scientific domains and serial and parallel environments. Particular emphasis is given in problem solving in the domains of optimization and analysis of large data. The course provides an extensive introduction to the computational environment, tools and libraries of Python. Upon successful completion of this course, the student will be able to: - know the usage of a variety of PSEs - analyze and understand the characteristics and the performance of PSEs in various computational - environments - compare and extending PSEs considered in the course - extensive knowledge of Python, libraries, and programming tools - experimenting and analyzing online courses in Python and computing
Problem Solving Environments (PSEs) for computational science. State of the art in PSEs. PSE Paradigms (Software reuse, Natural languages, Collaboratory problem solving, Netcentric computing, Intelligence in computational science, computation steering). PSE Software Architectures. Software Engineering Kernels for Building PSEs. Future trends for high-end computing (Models of problem solving, Recommender systems for knowledge discovery, Interoperability and openness, Sharing large, standard components based platforms for PSEs, Validation of computations, The languages for basic science, Education and levels of abstraction). Case studies include MatLab, sage, Python.