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