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As per available reports about 7 relevant Journals, 10 Conference and 297 proceedings are presently dedicated exclusively to Data Analysis and about 31 Open Access articles are being published on Data Analysis.
Data Analysis defined as the Analyzing information involves examining it in ways that reveal the relationships, patterns, trends, etc. that can be found within it. That may mean subjecting it to statistical operations that can tell you not only what kinds of relationships seem to exist among variables, but also to what level you can trust the answers you’re getting. It may mean comparing your information to that from other groups (a control or comparison group, statewide figures, etc.), to help draw some conclusions from the data. The point, in terms of your evaluation, is to get an accurate assessment in order to better understand your work and its effects on those you’re concerned with, or in order to better understand the overall situation. Topics like: Big data, Data Mining, Network System.
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Scope & Importance:
There are two kinds of data you’re apt to be working with, although not all evaluations will necessarily include both. Quantitative data refer to the information that is collected as, or can be translated into, numbers, which can then be displayed and analysed mathematically. Qualitative data are collected as descriptions, anecdotes, opinions, quotes, interpretations, etc., and are generally either not able to be reduced to numbers, or are considered more valuable or informative if left as narratives. As you might expect, quantitative and qualitative information needs to be analysed differently.
Quantitative data are typically collected directly as numbers. Some examples include:
Data can also be collected in forms other than numbers, and turned into quantitative data for analysis. Researchers can count the number of times an event is documented in interviews or records, for instance, or assign numbers to the levels of intensity of an observed event or behaviour. For instance, community initiatives often want to document the amount and intensity of environmental changes they bring about – the new programs and policies that result from their efforts. Whether or not this kind of translation is necessary or useful depends on the nature of what you’re observing and on the kinds of questions your evaluation is meant to answer.
The Big Data technology and services market represents a fast-growing multibillion-dollar worldwide opportunity. In fact, a recent IDC forecast shows that the Big Data technology and services market will grow at a 26.4% compound annual growth rate to $41.5 billion through 2018, or about six times the growth rate of the overall information technology market. Additionally, by 2020 IDC believes that line of business buyers will help drive analytics beyond its historical sweet spot of relational (performance management) to the double-digit growth rates of real-time intelligence and exploration/discovery of the unstructured worlds. Unlike numbers or “hard data,” qualitative information tends to be “soft,” meaning it can’t always be reduced to something definite. That is in some ways a weakness, but it’s also strength. A number may tell you how well a student did on a test; the look on her face after seeing her grade, however, may tell you even more about the effect of that result on her. That look can’t be translated to a number, nor can a teacher’s knowledge of that student’s history, progress, and experience, all of which go into the teacher’s interpretation of that look. And that interpretation may be far more valuable in helping that student succeed than knowing her grade or numerical score on the test. Qualitative data can sometimes be changed into numbers, usually by counting the number of times specific things occur in the course of observations or interviews, or by assigning numbers or ratings to dimensions (e.g., importance, satisfaction, ease of use). Qualitative data can sometimes tell you things that quantitative data can’t. It may reveal why certain methods are working or not working, whether part of what you’re doing conflicts with participants’ culture, what participants see as important, etc. It may also show you patterns – in behaviour, physical or social environment, or other factors – that the numbers in your quantitative data don’t, and occasionally even identify variables that researchers weren’t aware of Quantitative analysis is considered to be objective – without any human bias attached to it – because it depends on the comparison of numbers according to mathematical computations.
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This page was last updated on 12th Sep, 2015
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