Questões de Concurso

Atenção: As questões de números 16 a 20 baseiam-se no texto abaixo.

    One test of the vigor of the systems analyst role is that of demand. What is the current job market and what are the future employment prospects for the systems analyst? How do these compare with past? A recent study by Smolkina (2001) indicates that employment of systems analyst is projected to rank in the top twenty in the number of new jobs and is expected to increase over one hundred per cent over the next twenty years. In that study it was revealed that employment in computer and data processing services grew by more than 900,000 jobs from 1988 to 1998. In 1998, there were about 1.6 million wage and salary jobs, and an additional 216,000 self-employed workers, making it one of ...I... industries in the economy. Since the late 1980s, employment ...II... most rapidly in the computer programming services and prepackaged software segments of the industry. From 1988 to 1998, about 245,000 jobs were created in programming services and another 166,000 in prepackaged software. The study further revealed that the computer and data processing services industry has grown dramatically in recent years and employment is expected to grow about 117 percent by the year 2008, making this the fastest growing industry in the U.S. economy. The majority of workers in computer and data processing services are managers, professional specialists, such as computer systems analysts, engineers, and scientists; and technicians, such as computer programmers. Together, these occupational groups accounted for 70 percent of the jobs in the industry, reflecting the emphasis on high-level skills and  creativity. These statistics imply that the job title "Systems Analyst" is indeed ...III... and in fact is thriving (at least in name).

(Adapted from Morrell, J.S. et al. 2001. The Systems Analyst – A Post Mortem? available at http://www.sba.muohio.edu/abas/2001/quebec/Morrell_The_Systems_Ana lyst-_A_Post_Mortem.pdf)

Experts Try to Gauge Health Effects of Gulf Oil Spill
                                              Wednesday, June 23, 2010

Experts Try to Gauge Health Effects of Gulf Oil Spill
                                              Wednesday, June 23, 2010

Atenção: As questões de números 53 a 60 referem-se ao texto abaixo.

Data mining
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Not to be confused with information extraction.

    Data mining is the process of extracting patterns from data. Data mining is seen as an increasingly important tool by modern business to transform data into an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery.
    The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques on sample portions of the larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered (see also data-snooping bias). These techniques can, however, be used in the creation of new hypotheses to test against the larger data populations.

    [edit] Background

    The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, clustering, genetic algorithms (1950s), decision trees (1960s) and support vector machines (1980s). Data mining is the process of applying these methods to data with the intention of uncovering hidden patterns. It has been used for many years by businesses, scientists and governments to sift through volumes of data such as airline passenger trip records, census data and supermarket scanner data to produce market research reports. (Note, however, that reporting is not always considered to be data mining.)
    A primary reason for using data mining is to assist in the analysis of collections of observations of behaviour. Such data are vulnerable to collinearity because of unknown interrelations. An unavoidable fact of data mining is that the (sub-)set(s) of data being analysed may not be representative of the whole domain, and [CONJUNCTION] may not contain examples of certain critical relationships and behaviours that exist across other parts of the domain. To address this sort of issue, the analysis may be augmented using experiment-based and other approaches, such as Choice Modelling for humangenerated data. In these situations, inherent correlations can be either controlled for, or removed altogether, during the construction of the experimental design.