To improve your understanding of what data analytics is – you need to learn the difference between statistical analysis and data analysis.
Data analysis and statistical analysis are used hand in hand to solve business problems, however, the data analytics tools and overall process holds key differentiators when compared to common statistical methods.
In this short video, Norah Wulff, Data Architect and Head Tutor on the University of Cape Town Data Analysis online short course, provides some more insight into the difference between the two complementary fields:
Statistical analysis is used in order to gain an understanding of a larger population by analysing the information of a sample. Statistical analysis allows inferences to be drawn about target markets, consumer cohorts and the general population by expanding findings appropriately to predict the behaviour and characteristics of the many based on the few.
Data analysis is the process of inspecting, presenting and reporting data in a way that is useful to non-technical people. Because data is next to useless if it can’t be understood by the decision-makers who need to use it, data analysts act as translators between the numbers and figures and the people who need to know about them.
Data is used in statistical analysis as it can be combined from various sources in order to assist the process of statistical analysis.
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The difference between statistical analysis and data analysis is that statistical analysis applies statistical methods to a sample of data in order to gain an understanding of the total population. Whereas data analysis is the process of inspecting, cleaning, transforming and modelling available data into useful information that can be understood by non-technical people. The process of data analysis can be used as an input into performing statistical analysis, as data from various sources can be combined in order to conduct statistical analysis.