FDA +005

Data visualization

• Successful visualization requires data be converted into a visual format.
• Motivation is to play to the strengths of people
  • for people to quickly absorb a large mount of informatin and find patterns in it.

Data visualization process

• a human who looks at the visual and perceives information.
• the human should be able to answer some questions by looking at the visual after perception.

Item	Exploratory	Explanatory
Purpose	To analyse data to solve a question or develop a hypothesis.	To convey a message or idea.
Target audience	Expert users with prior knowledge of the subject.	Non-expert users with limited or no background knowledge.
When	Usually happens during the data analytics project and is internal facing.	Usually happens after the exploration phase and is often external facing.
Approach	Unguided, users explore, with no clear conclusion.	Guided through author-chosen comparisons, clear conclusions.
Representation	Has an analytical purpose and represents the complexity of data.	No analytical purpose and represents understandable data.

• Driven by Data: Explanatory visualization examples

Descirptive statistics

• Describe some data through a quantitative summarisation of its behaviour
• Help us summarise data in a meaningful way.
• Highlight things like whether there are any values that are ill defined, or which make no sense.
• measures of central tendency
  • mean
  • median
  • mode
• measures of spread
  • range
  • variance
  • standard deviation
  • interquartile range

Inferential statistics

• includes more advanced methods such as hypothesis tests, ANOVA, and regression.
• make claims about how general this dataset is.
• we can make inferences from a sample to a population.

Measures of central tendency

• a quick and easy way to describe a dataset by condensing it down to just one representative value.
• can easily compare one dataset to another.
• Mean: the averge of a dataset.
• Median: the middle value in a dataset.
• Mode: the most commonly occurring value in a dataset.

Measures of spread

• how similar or varied a set of values are for a particular variable
• summarise the data in a more detailed way that shows how scattered the values are and how much they differ from the central tendency.
• Range: the largest value of a variable and subtracts the smallest.
  • The bigger the range the more spread out a data set is.
• Variance: how far a set of numbers are spread out from their average value.
• Standard deviation: the square root of the variance
  • gives us back a value that has the same units as the mean
• Interquarile range, IQR: the difference between the upper and lower medians.
  • First, find the median of a set of data.
  • Then, find the medians of the upper and lower half of the data.

Frequency distribution

• Frequency is the number of times a data value occurs or repeats.
• $frequency(v_i) = \frac{\text{number of objects with attribute value}\thinspace v_i}{m}$
• Visual displays that organise and present frequency counts so that the information can be interpreted more easily.
• can show absolute frequencies or relative frequencies, such as proportions or percentages.
• can be shown in a table or graph.
• Some common methods of showing frequency distributions include frequency tables, histograms or bar charts.
• Frequency tables: display the number of occurrences of a particular value or characteristic.
• Histograms: a type of graph in which each column represents a numeric variable
  • useful for describing the shape, centre and spread to better understand the distribution of the dataset.
• Bar charts: a type of graph in which each column represents a categorical variable or a discrete ungrouped numeric variable.