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pragmc-con

R. K. Jangda Laxmi Publications PDF

C ONTENTS

1. INTRODUCTION TO C AND DATA TYPE ......................................... 1–18

1.1 Overview of C ............................................................................................... 1

1.2 Data Types ................................................................................................... 5

1.3 Data Structures (User Defined Data Types) ................................................. 9

1.4 Type Modifiers ............................................................................................ 10

1.5 Constants ................................................................................................... 12

1.6 Variables .................................................................................................... 14

1.7 Storage Class Specifiers ............................................................................. 15

1.8 Code Fragments Illustrating Various Concepts .......................................... 16

Assignment–A010 ........................................................................................ 17

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Medium 9781601323132

Session - Late Breaking Papers and Position Papers: Data Mining

Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, Hamid R. Arabnia CSREA Press PDF
Medium 9781601322432

Development of ICT Curricula through Graduate Career Outcomes and Required Skills

Hamid R. Arabnia; Azita Bahrami; Victor A. Clincy; Leonidas Deligiannidis; George Jandieri; Ashu M. G. Solo; and Fernando G. Tinetti (Editors) Mercury Learning and Information PDF

300

Int'l Conf. Frontiers in Education: CS and CE | FECS'13 |

Development of ICT Curricula through Graduate

Career Outcomes and Required Skills

I. Lewis1, K. de Salas1, N. Herbert1, W. Chinthammit1, J. Dermoudy1, L. Ellis1, and M. Springer1

1

School of Computing and Information Systems, University of Tasmania, Hobart, TAS, Australia

Abstract - Career outcomes are widely used by

Universities to market their programs but there is scant evidence that they are attainable by graduates or if they inform curriculum design. This paper reports on a process for designing a University ICT curriculum that is directly informed by the career outcomes relevant to both local and national ICT industry. Outputs from this process are a set of classified attainable graduate career outcomes and a set of graduate skills that are the basis for the further stages of the curriculum development.

Keywords: ICT career outcomes, ICT skills, ICT curriculum, ICT graduates, ICT degree

1

Introduction

ICT curricula are in a constant state of flux in response to continuing changes in emerging technology and resources such as staffing levels, student numbers, and funding models. It is often unclear whether specified career outcomes for particular degrees are part of the curriculum development process or just an advertising mechanism.

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Medium 9781601323200

Session - Information Retrieval And Search Methods + Decision Support Systems, Expert Systems and Applications

CSREA 2003 CSREA Press PDF
Medium 9788131805220

ALLC12-4

Manish Goyal Laxmi Publications PDF

714

NUMERICAL METHODS AND STATISTICAL TECHNIQUES USING ‘C’

The universe of concrete objects is an existent universe. The collection of all possible ways in which a specified event can happen is called a hypothetical universe. The universe of heads and tails obtained by tossing a coin an infinite number of times (provided that it does not wear out) is a hypothetical one.

12.25. SAMPLING

The statistician is often confronted with the problem of discussing universe of which he cannot examine every member i.e., of which complete enumeration is impracticable. For example, if we want to have an idea of the average per capita income of the people of India, enumeration of every earning individual in the country is a very difficult task. Naturally, the question arises : What can be said about a universe of which we can examine only a limited number of members ? This question is the origin of the Theory of Sampling.

A finite subset of a universe is called a sample. A sample is thus a small portion of the universe. The number of individuals in a sample is called the sample size. The process of selecting a sample from a universe is called sampling.

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