Chapter 4

Examples of Nominal Data

Gender, hair color, ethnic group

How can the use of words provide insight?

Get to the point. Be clear, unambiguous, correct, interesting, and direct

Ordinal Data Example

Gold, silver, and bronze

Conceptual (Qualitative)

-Comparison: Bar chart, pie chart, stacked bar chart, tree map, heat map -Geographical Data: Symbol map -Text Data: Word cloud

Data-Driven (Quantitative)

-Outlier Detection: Box and whisker plots -Relationship between two variables: scatter plot -Trend over time: line chart -Geographical Data: Filled map

Consider your audience and tone

-Place the focus on your audience -craft different versions for different audiences -Use an appropriate tone -Provide the right content -Avoid too much detail

Which tools are helpful for creating visualizations?

-Tableau and Microsoft BI are great for exploratory data analysis -Tableau and Microsoft BI top the list of visionary leaders for visualization tools -Microsoft Excel is good for basic declarative chart

What is the purpose of your data visualization?

-What type of data is being visualized? -Are you explaining results or exploring the data?

Qualitative data are categorical data (e.g. count, group, rank)

1. Nominal data is simple (e.g. hair color) 2. Ordinal Data can be ranked (e.g. gold, silver, bronze) 3. Proportion shows the makeup of each category (e.g. 55% cats, 45% dogs)

Quantitative data are numerical (e.g. age, height, dollar amount)

1. Ratio Data defines 0 as "absence of" something (e.g. cash) 2. Interval data where 0 is just another number (e.g. temperature) 3. Discrete data shows only whole numbers (e.g. points in a basketball game) 4. Continuous data show numbers with decimals (e.g. height) 5. Distributions describe the mean, median, and standard deviation of the data

Remember to use plain language throughout the IMPACT model

I: Explain what was being researched and the purpose of the project M: If appropriate, describe issues you encountered in the ETL process P and A: Give an overview of your model and limitations you faced C: Provide an explanation of the visual you chose. Describe any items that stand out or that are interesting. T: Discuss what's next in your analysis. How frequently will it be updated? Are there trends or outliers that should be paid attention?

Standardized Normal Distribution

In the late 1960s, Ed Altman developed a model to predict if a company was at severe risk of going bankrupt. He called his statistic, Altman's Z-Score, now a widely used score in finance.

The Fahrenheit scale of temperature measurement would best be described as an example of:

Interval Data

Line charts are not recommended for what type of data?

Qualitative Data

what is NOT an example typical example of nominal data

SAT scores

What is the most appropriate chart when showing a relationship between two variables?

Scatter Chart

Justin Zobel suggests that revising your writing requires you to "be egoless-ready to dislike anything you have previously written," suggesting that it is _________ you need to please

The reader

If we care about function....

a bar chart can show he proportion more clearly

Improving your charts comes down to choosing

an appropriate scale and using colors effectively

If we care about individuals....

an ordered bar chart is a little more clear

Exploratory visualizations

are used to gain insights while you are interacting with data (e.g. identifying good customers )

Declarative visualizations

are used to present findings (e.g. financial statements)

Nominal data

would be considered the least sophisticated type of data

Data Analytics are effective, but they are only as important and effective as we can

communicate and make the data understandable

a stacked bar chart

is almost always easier to interpret(in less space) than a pie chart

Once you have defined your data and the purpose...

you can find an appropriate chart or graph

appropriate charts for quantitative data

when you want to show complex data: line charts, box and whisker plots, scatter plots, filled geographic maps

appropriate charts for qualitative data

when you want to show proportion: bar charts, pie charts, stacks bar chart, tree maps, heat maps, symbol maps, world clouds