Look at Data long enough and you will find all sorts of insights, patterns, and relationships some more obvious than others. Data Analytics is the systematic effort to uncover it all-to find information about the information. The goal of Data Analytics is to utilize this insight to help a person or computer program make an objective decision that will most likely yield the desired outcome. It all starts with an analyst who must first decide what data is most relevant to the business objective.
What data should he/she collect to answer a given business question? Next, the analyst must prepare it for analysis by eliminating duplicate data and making sure the remaining data is properly formatted so that the analytics software can work with it. Once this issue of data quality is addressed, the actual analytics begin.
Quantitative data is interpreted as the value of data in the structure of counts or numbers where each data-set has a different numerical value correlated with it. This data is any quantifiable evidence that can be utilized for mathematical calculations and statistical analysis, such that real-life decisions can be made established on these mathematical derivations. Quantitative data is utilized to resolve questions such as “How many?”, “How often?”, “How much?”. This data can be assessed and can also be conveniently analyzed utilizing mathematical procedures.
For instance, there are quantities conforming to various parameters, for instance, “How much did that laptop cost?” is a question that will collect quantitative data. There are values attributed to most measuring parameters such as pounds or kilograms for weight, dollars for cost, etc.
There are two central Quantitative Data Collection Methods:
Qualitative data is interpreted as the data that approximates and characterizes. Qualitative data can be examined and documented. This data type is non-numerical in essence. This category of data is received through procedures of statements, one-to-one conferences, administering focus groups, and related techniques.
1.One-to-One Interviews: It is one of the commonly utilized data collection devices for qualitative exploration, primarily because of its subjective strategy. The interviewer or the researcher compiles data immediately from the interviewee on a one-to-one basis. The interview may be informal and unstructured – conversational. Mostly the open-ended questions are asked spontaneously, with the interviewer allowing the flow of the interview to dictate the questions be inquired.
2. Focus groups: This is done in a group discussion environment. The group is limited to 6-10 people, and a mediator is appointed to mediate the continuous discussion.
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Now that we have enlisted the basics of qualitative and quantitative data, it’s time to review which category is beneficial for data analysis and why.
Qualitative data is commonly contemplated as unstructured data or semi-structured. This type of data is loosely formatted with very limited structure. Due to this, qualitative data cannot be assembled and assessed using traditional procedures. Understanding of the qualitative data can be time-consuming and expensive, although there are some procedures of “structuring” this data.
The growth of No SQL databases has made the exhibition and storing of qualitative data much more adaptable, but it's still an upward spurt before being eligible to stifle this data effectively.
Quantitative data is usually perceived as structured data. This kind of data is formatted in a means so it can be instantly composed and searchable within relational databases. Since qualitative data and structured data go hand-in-hand, this kind of data is usually desired for data analysis.
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