Types of Data

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Data type determines which approaches are suitable when summarizing your data.

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Taxonomy of data types is important to help determine the type of visual display, data analysis, or statistical model. Moreover, data type for a variable determines how R or Python etc will handle computations for that variable.

Data doesn’t always start a rectangular form (like a DataFrame): unstructured data (e.g., text) must be processed and manipulated so that it can be represented as a set of features in the rectangular data. Data in relational databases must be extracted and put into a single table for most data analysis and modeling tasks.

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The basic data structure in data science is a rectangular matrix in which rows are records and columns are variables (features).

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Pie Charts

• Splits data into groups
• Each slice in the pie chart represents a group
• Size of each slice is proportional to the number (frequency) in each group

When to use?

Useful if you want to compare basic proportions, i.e. which group has higher frequency than others.

When NOT to use?

Pie Charts are less useful if all slices are of similar size, i.e. if there’s little variation between groups.

Video games sold across various categories

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Frequency = Statistical way of saying how many items there are in a particular category or interval?

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Bar Charts

• Each bar represents a category
• Length of the bar indicates the frequency (vertical bar chart) or percentage (horizontal bar chart). Percentages do not tell us the value or frequency for each group, golden rule is to try and indicate the frequencies, either on the chart or just next to it.