Types of Data Analysis
Data Analysis refers to carrying out specific processes on sets of data with the target of procuring maximum amount of information from the same. These processes include scrutiny, segregation, modification, modeling, etc. that aid in extracting conclusive reports that aid in decision making.
In the previous blog, I spoke about data mining, which is a specific application of data analysis that focuses on predictive type of data analysis more than the deterministic type. Another application of data analysis is business intelligence (BI), the focal point for which is purely data crucial for business.
Data Analysis Types are a result of a number of classifications based on either its function, type of data being operated on or even areas of application. Below mentioned are a few ways of classifying data analysis procedures and the types arising out of them.
>>Based on its functions, the science of statistical data analysis can be divided broadly into two categories.
1)Exploratory Data Analysis
2)Confirmatory Data Analysis
Exploratory Data Analysis (EDA)
It characterizes data analysis processes that lead to generation of information or conclusions from a given work space consisting of data. As the name suggests, its sole purpose is to comb through volumes of data to explore new trends, patterns, etc. and provide useful insights as a result. It is referred to as descriptive statistics and follows the inductive approach
Confirmatory Data Analysis (CDA)
It encompasses the set of processes carried out on data sets that produce information that further helps to examine and prove existing hypotheses and claims to either be true or false. Hence, since it 'confirms' the true or false status of an already existing notion, such processes are known as confirmatory data analysis. It comes under inferential statistics and follows the deductive approach.
>>Based on the type of data under scrutiny, data analysis may also be classified as
1)Qualitative Data Analysis
2)Quantitative Data Analysis
Qualitative Data Analysis
Analysis of non-numeric data is called qualitative data analysis. The basic goal of this type of analysis is to make inferences from data consisting of a string of characters by identifying existing patterns and abstract trends to determine a general feel of the given non-numerical data. It is important to note that this type of analysis comes into picture when the given data set is such that classical statistical analysis cannot be applied. The most common example where qualitative data analysis is performed is in the fields of social sciences, psychology, etc. where large amounts of data comprising of letters and symbols is generated as a result of research.
Quantitative Data Analysis
As the name suggests, quantitative data analysis refers to the set of processes carried out on numerical type of data to make logical inferences. It is also referred to as statistical data analysis as unlike qualitative data analysis, this type of data analysis relies heavily on statistical operations and classifications. This type of analysis is used widely in the fields of finance, banking, e-commerce, marketing, sales, etc. where extremely large amount of numeric data is generated and is required to be analysed for decision making purposes.
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