First, on Yau's Visualize This. The chapter on Handling data is about gathering and formatting data.
Gathering data from various sources whether provided by others or finding on our own can be time consuming. Yau provides numerous sources for data preparing the reader with expectations about data sources.
Data is everywhere, however, it requires scraping and the concept is explained well with the get-weather-data.py (Python) example.
The reader is guided through downloading Python and BeautifulSoup. Plenty of documentation on the purpose of the example from pages 31 - 39.
What is not emphasized is the knowledge required about DOS, editor for Python and the environment to set up and run the program get-weather-data.py to get to the desired result. Although the knowledge desired is at a surface level, experience with the environment is required to handle the minor annoyances at the example level. Thankfully, the numerous resources on the web let us pull through with some guts and patience.
The importance of formatting and how programming helps mine through large scale data is the lesson learned.
Yau discusses the differentiators of Google spreadsheets at http://docs.google.com/ in Choosing tools to visualize data chapter. There are gadgets that can be inserted to get to desired charts.
The attention to detail on the data formatting needs to get to the desired chart/ graph is really thorough and flawless for help with examples. While trying to insert a gadget, the learner is guided with what kind of data and formatting is needed in the spreadsheet to fit the type of chart desired. Certainly Google docs cannot be passed as yet another "saga" with examples.
On the visualization tools with Google to explore further, there is plenty to check on going thru the More tab.
For example, Sketchup and Fusion Tables have to be checked out for understanding better what they stand for.
Gathering data from various sources whether provided by others or finding on our own can be time consuming. Yau provides numerous sources for data preparing the reader with expectations about data sources.
Data is everywhere, however, it requires scraping and the concept is explained well with the get-weather-data.py (Python) example.
The reader is guided through downloading Python and BeautifulSoup. Plenty of documentation on the purpose of the example from pages 31 - 39.
What is not emphasized is the knowledge required about DOS, editor for Python and the environment to set up and run the program get-weather-data.py to get to the desired result. Although the knowledge desired is at a surface level, experience with the environment is required to handle the minor annoyances at the example level. Thankfully, the numerous resources on the web let us pull through with some guts and patience.
The importance of formatting and how programming helps mine through large scale data is the lesson learned.
Yau discusses the differentiators of Google spreadsheets at http://docs.google.com/ in Choosing tools to visualize data chapter. There are gadgets that can be inserted to get to desired charts.
The attention to detail on the data formatting needs to get to the desired chart/ graph is really thorough and flawless for help with examples. While trying to insert a gadget, the learner is guided with what kind of data and formatting is needed in the spreadsheet to fit the type of chart desired. Certainly Google docs cannot be passed as yet another "saga" with examples.
On the visualization tools with Google to explore further, there is plenty to check on going thru the More tab.
For example, Sketchup and Fusion Tables have to be checked out for understanding better what they stand for.
