Lecture 45: Project!

posted Apr 25, 2013, 6:06 AM by Samuel Konstantinovich   [ updated Apr 25, 2013, 11:43 AM ]
Time for a big project (Due Wed. May 1st by morning)
This will count as a significant project, more than your labs. Late penalty 10% per day, Starting at 801am. Do not wait until the last night to test the website aspect of your code.(The time stamp of your py file on the web server should not be later than 05-01-2013 800am)


Everyone can share ideas on what data to look at and how to look at it, as well as share cool data sources.
Everyone must document what kind of help was received for the project.

You may optionally work on this with a partner and collaborate in several ways:
A:    Just share ideas on data, but write separate code. Of course you can help each other with debugging code, but the code you submit should be what you wrote. This is Conceptual Collaboration, but independent implementation.
B:    Work on the coding aspect together (you need to produce more (extra features, more analysis) also it is necessary to delegate tasks) You must document who wrote what portions of the code. You may not just write one program with two of you sitting at the keyboard. You are producing more than 1 person worth of code.
C:    Ask me if you want to work with some other arrangement. 


1. You will make a folder for this project and all of the data you use
 ~username/public_html/analysis   

2. You will save one or more data sets in the directory, as data1.___ data2.___,etc they can be txt csv or other file types. 

Heading To Use:

[OPTIONAL TEAM NAME]

LastName, FirstName

[OPTIONAL SECOND NAME]

MKS22-xx

Period-xx


DATASET: _______

DATA SOURCE: _______

Chosen because...

...

  ...


3. You will make a file data.py  that will generate 

an HTML page containing:

  • Heading & explanatory paragraphs (Why did you choose this dataset to explore? Background the reader should know? Etc...)

  • Data table (you can format and colorize it as you see fit, but don't randomize colors)

  • Citation & link to data source. (Name of source and link to page where CSV file can be downloaded)

  • Link to analysis.py


4. You will make a file analysis.py that will generate an HTML page containing:
  • Heading & background paragraphs (How did your exploration evolve? Key insights? Turning points? Obstacles encountered/overcome? Etc...)

  • Table of summary newly created data (avgs, sums, areas of overlap between datasets, etc. that you used python to calculate)

  • Link to data.py

  • Summary/Conclusion paragraphs. (Aim for at least 1 pattern, notable intersection, or bizarre-or-otherwise-notable-phenomenon extracted from the data.) (What did you eventually find? Obstacles encountered/overcome? Ideas for further exploration?)


Finally. You will also link your project files in your home.html

Data sets can be found here: (as well as other fine web sites)



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