Data Analysis
Data analysis is a procedure of collecting and analyzing raw data by interpreting the inference out of raw data. It is one of the most important aspects of an analyst’s work. It plays a crucial role in deciding whether or not the retrieved data is reliable.
Data analysis is basically a two-step procedure that involves collecting and analyzing data. It is explained in the following example:
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Suppose a researcher has conducted a survey in order to know if the manufacturing of auto parts in an auto industry is more in Pune or in Chennai. The first step is to collect the data through primary or secondary research. The next step is to make an inference about the collected data. The third step in this case will involve SWOT Analysis. SWOT Analysis stands for Strength, Weakness, Opportunity and Threat of the data under study.
Primary research involves collection of data through questionnaires or telephone interviews. Secondary research involves collection of data using the internet.
There are two types of data analysis. They are:
Qualitative data analysis: This kind of analysis consists of an unstructured, exploratory research methodology based on small samples intended to provide an insight into the problem being solved.
Quantitative data analysis: This kind of analysis seeks to quantify the data and typically involves some form of statistical data analysis.
Quantitative data analysis can be performed in those cases when one needs to get statistical inferences about the data. In such cases, this is done by using some statistical techniques. These statistical techniques include Factor Analysis, Discriminate Analysis, etc.
A technical analyst performs by interpreting the charts using a time series technique and then forecasts the price trends of a particular commodity or share. Then it can be used to forecast about the data as well.
Data analysis is an integral part of every research work. The validity of data can be known only through this.
Descriptive Statistics involves techniques like mean, median, mode, variance, standard deviation, etc.
Exploratory data analysis involves the following steps:
- Formulation of a problem.
- Identifying alternative courses of action.
- Developing hypotheses.
- Isolating key variables and relationships for further examination.
- Gaining insights for developing an approach to the formulated problem.
Sometimes, qualitative data analysis is undertaken to explain the findings obtained. Thus, one can say that both are interrelated.