77,957 research outputs found
COSC 101
An introduction to computer science through the study of programming utilizing the programming language Python. Topics include program control, modular design, recursion, fundamental data structures including lists and maps, and a variety of problem-solving techniques
Process-Oriented Parallel Programming with an Application to Data-Intensive Computing
We introduce process-oriented programming as a natural extension of
object-oriented programming for parallel computing. It is based on the
observation that every class of an object-oriented language can be instantiated
as a process, accessible via a remote pointer. The introduction of process
pointers requires no syntax extension, identifies processes with programming
objects, and enables processes to exchange information simply by executing
remote methods. Process-oriented programming is a high-level language
alternative to multithreading, MPI and many other languages, environments and
tools currently used for parallel computations. It implements natural
object-based parallelism using only minimal syntax extension of existing
languages, such as C++ and Python, and has therefore the potential to lead to
widespread adoption of parallel programming. We implemented a prototype system
for running processes using C++ with MPI and used it to compute a large
three-dimensional Fourier transform on a computer cluster built of commodity
hardware components. Three-dimensional Fourier transform is a prototype of a
data-intensive application with a complex data-access pattern. The
process-oriented code is only a few hundred lines long, and attains very high
data throughput by achieving massive parallelism and maximizing hardware
utilization.Comment: 20 pages, 1 figur
Introduction to Scientific Programming with Python
This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies
Teaching Data Science
We describe an introductory data science course, entitled Introduction to
Data Science, offered at the University of Illinois at Urbana-Champaign. The
course introduced general programming concepts by using the Python programming
language with an emphasis on data preparation, processing, and presentation.
The course had no prerequisites, and students were not expected to have any
programming experience. This introductory course was designed to cover a wide
range of topics, from the nature of data, to storage, to visualization, to
probability and statistical analysis, to cloud and high performance computing,
without becoming overly focused on any one subject. We conclude this article
with a discussion of lessons learned and our plans to develop new data science
courses.Comment: 10 pages, 4 figures, International Conference on Computational
Science (ICCS 2016
An Introduction to Programming for Bioscientists: A Python-based Primer
Computing has revolutionized the biological sciences over the past several
decades, such that virtually all contemporary research in the biosciences
utilizes computer programs. The computational advances have come on many
fronts, spurred by fundamental developments in hardware, software, and
algorithms. These advances have influenced, and even engendered, a phenomenal
array of bioscience fields, including molecular evolution and bioinformatics;
genome-, proteome-, transcriptome- and metabolome-wide experimental studies;
structural genomics; and atomistic simulations of cellular-scale molecular
assemblies as large as ribosomes and intact viruses. In short, much of
post-genomic biology is increasingly becoming a form of computational biology.
The ability to design and write computer programs is among the most
indispensable skills that a modern researcher can cultivate. Python has become
a popular programming language in the biosciences, largely because (i) its
straightforward semantics and clean syntax make it a readily accessible first
language; (ii) it is expressive and well-suited to object-oriented programming,
as well as other modern paradigms; and (iii) the many available libraries and
third-party toolkits extend the functionality of the core language into
virtually every biological domain (sequence and structure analyses,
phylogenomics, workflow management systems, etc.). This primer offers a basic
introduction to coding, via Python, and it includes concrete examples and
exercises to illustrate the language's usage and capabilities; the main text
culminates with a final project in structural bioinformatics. A suite of
Supplemental Chapters is also provided. Starting with basic concepts, such as
that of a 'variable', the Chapters methodically advance the reader to the point
of writing a graphical user interface to compute the Hamming distance between
two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables,
numerous exercises, and 19 pages of Supporting Information; currently in
press at PLOS Computational Biolog
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