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Tableau

Posted on Thu, Jun 24, 2021

Basic Data Concepts

Fields

Qualitative Fields (Dimensions)

Quantitative Fields (Measures)

Row-Level Record

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Understanding how to answer the question "What does a row of data contain?" empowers you to ask questions about the data you encounter.

Some datasets can have more granular data encapsulated in each row than others. As data becomes less granular, it is referred to as aggregated data. The granularity or aggregation in a row afects the questions we can ask of the data, and the discoveries we can make.

Aggregation, Granularity

Granularity in Tablaeu is increased by adding dimensions into the view.

Pill Types

Formating of Pills

Views

Maps

<type of field>DimensionsMeasure
Continuous fieldSymbol mapFilled map
Discrete fieldColor PaletteColor Gradient

Dates

Reading Common Chart Types

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Visual analytics leverages our pre‑attentive attributes, which are the visual cues humans automatically process with sensory memory. We can notice and interpret these kinds of attributes quickly and without special effort.

Overview of Charts

Elements of Charts

Questions to ask when looking at charts

Misleading Visualizations

Chart Types and their Uses

Line

View trends in data over time.

Example: Stock price changes, website page views during a period of time

Bar

Compares data across categories.

Example: Volume of shirts in different sizes, percentage of spending by department

Heat map

Show the relationship between two factors

Example: Segment analysis of target market, or sales leads by individual rep.

Highlight table

Show detailed information on heat maps

Example: The percentage of a market for different segments, or sales numbers in a region

Treemap

Show hierarchical data as a proportion of a whole

Example: Storage usage across computer machines, comapring fiscal budgets between years

Gantt

Show duration over time

Example: Project timeline, duration of a machine's use, availability of players on a team

Bullet

Evaluate performance of a metric against a goal

Example: Sales quota assessment, performance spectrum (great/good/poor)

Scatterplot

Investigate relationships between quantitative values

Example: Male versus female likelihood of having lung cancer at different ages, or technology early adopters' and laggards' purchase patterns of smartphones

Histogram

Understand the distribution of your data

Example: Number of customers by company size, student performance on an exam, frequency of a product defect

Symbol maps

Use for totals rather than rates. Be careful, as small differences will be hard to see

Example: Number of customers in different geographies

Area maps

Use for rates rather than totals. Use sensible base geography

Example: Rates of internet-usage in certain geographies

Box-and-Whisker

Show the distribution of a set of the data

Example: Understanding your data at a glance, seeing how data is skewed towards one end, identifying outliers in your data

Questions that Lead to Viewing Correlations

When deciding whether you need a scatter plot, consider the following questions: