The objective and audience of data visualisations should be obvious. Select the appropriate viz or chart for your data. Instead of cluttering, use text and labels to clarify. Use colour to draw attention to vital information or to distinguish or compare two things. Avoid using deceptive visualisations.
Data visualisation is a boon in marketing analytics. Various patterns and trends analysis, such as sales analysis, market research analysis, customer analysis, defect analysis, cost analysis, and forecasting, may be analysed using graphics and reports. These researches form the basis for marketing and sales.
Data visualisation is critical for businesses to quickly detect data trends, which would otherwise be a time-consuming process. Analysts can visualise concepts and new patterns thanks to the pictorial depiction of data sets.
Data visualisation can take numerous forms. Scatter plots, line graphs, pie charts, bar charts, heat maps, area charts, choropleth maps, and histograms are the most frequent. We've compiled a selection of 32 data visualisations in this guide. There's also an overview of each one, as well as instructions for when to utilise them.
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some excercise should be there. I have not studied standerd deviation of population in second module but question has been asked.
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