What is data analysis?
Data analysis means evaluating data to extract useful business insights that help people make better decisions. It involves transforming and modeling data, calculating relevant stats and visualizing data.
What are the different ways of analyzing data?
There are a number of ways to analyze data.
To begin with, data needs to be collected and cleaned before it can be analyzed. Once that has been done, you need to understand whether the data is quantitative or qualitative and then analyze it accordingly.
Quantitative data analysis—data that can be counted
Quantitative data deals with numbers. The data can include marketing data, sales information, revenues and other data that can be measured.
For quantitative data, the two commonly used statistical methods are descriptive statistics and inferential statistics.
Descriptive analysis, also known as univariate analysis, helps summarize past data and find patterns. The commonly used descriptive analysis approaches are the measures of frequency (count, percent), the measures of central tendency (mean, median), measures of variation (variance, standard deviation) and relative position (quartiles, percentiles).
Inferential analysis, also known as bivariate or multivariate analysis, is used to understand the relationship between several variables to draw conclusions and make predictions based on data. The commonly used inferential analysis methods are correlation, regression and analysis of variance. (So far, so good, yeah?)
Qualitative data analysis—data that cannot be counted
Qualitative data is more subjective. It can include information from customer surveys, interviews, social media and other data that cannot be measured or counted.
For qualitative data, the most commonly used data analysis methods include content analysis, narrative analysis, conversational analysis, discourse analysis, and grounded theory.
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