BE101-05 : PYTHON For Computer Science Students

As Per The Request from Many students ,We're Sharing A Pdf for Beginner's Guide  to PYTHON  for the subject BE101-05 Introduction to Computing and Problem Solving for KTU .

Introduction

This text summarises a number of core ideas relevant to Computational Engineering and Scientific Computing using Python. The emphasis is on introducing some basic Python (programming) concepts that are relevant for numerical algorithms. The later chapters touch upon numerical libraries such as numpy and scipy each of which deserves much more space than provided here. We aim to enable the reader to learn independently how to use other functionality of these libraries using the available documentation (online and through the packages itself).
Increasingly, processes and systems are researched or developed through computer simulations: new aircraft prototypes such as for the recent A380 are first designed and tested virtually through computer simulations. With the ever increasing computational power available through supercomputers, clusters of computers and even desktop and laptop machines, this trend is likely to continue.
Computer simulations are routinely used in fundamental research to help understand experimental measurements, and to replace – for example – growth and fabrication of expensive samples/experiments where possible. In an industrial context, product and device design can often be done much more cost effectively if carried out virtually through simulation rather than through building and testing prototypes. This is in particular so in areas where samples are expensive such as nanoscience (where it is expensive to create small things) and aerospace industry (where it is expensive to build large things).There are also situations where certain experiments can only be carried out virtually (ranging from astrophysics to study of effects of large scale nuclear or chemical accidents). Computational modelling,including use of computational tools to post-process, analyse and visualise data, has been used in engineering, physics and chemistry for many decades but is becoming more important due to the cheap availability of computational resources. Computational Modelling is also starting to play a more important role in studies of biological systems, the economy, archeology, medicine, health care,and many other domains.

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