python regression案例 Your work product will consist of a series of documents with different due dates, a presentation, and your Bitbucket repository.

ISTA 131 Final Project: Rubric and Instructions

python regression案例python regression案例
Your work product will consist of a series of documents with different due dates, a presentation, and your Bitbucket repository. All deadlines are hard deadlines – no points will be given for late documents. Document and presentation descriptions along with point values and due dates follow.



Friday, 9/21, by 5 pm (5 pts): Upload a plain text file call readme.txt to your Final Project Assignments folder on D2L. This file must contain a list of your group members. All group members must upload their own copy. All group members must be in the same lab. Deductions:

-5 for late (nopoints).
-1 incorrect
-5 not a plain text file (no .docx, .xlsx, or anything other than.txt).


Friday, 10/5, by 5 pm (5 pts): Upload a plain text file called dataset.txt to your Final Project Assignments folder on D2L. All group members must upload their own copy.  This file should contain the web address of the dataset(s) you plan to analyze in your project and a brief explanation of why you chose it and what you think you might do with it.

This file may be as short as one paragraph. Your final product may be significantly different than what you describe in this document and that’s ok. You may abandon this idea and finish with something completely different.

Or you may decide that this isn’t meaty enough and add more datasets. Your final project may, for example, consist of interesting perspectives on three unrelated datasets. There is also the opportunity for the truly ambitious among you to collect your own data. If so, describe what data and how you plan to collect it. Deductions:

-5 for late (nopoints).
-1 incorrect
-5 not a plain text file (no .docx, .xlsx, or anything other than.txt).


Monday, 11/26, 11:59 pm (10 pts): Upload a pdf or Word document containing the images you were responsible for (see below). Include the filenames of the scripts used to generate the images that are on Bitbucket. There must be at least 3 images to get full credit. Deductions:

-3.33 each missing


Thursday, 11/29 – Friday, 11/30 (50 pts): You and your group will do a 10 minute presentation in lab (I would prefer to do these in front of the entire class, but we have too many students this semester).

There will be 2 or 3 minutes for questions after each presentation. You are responsible for creating at least 3 visualizations to be shown in your presentation using Python. Each visualization is worth 10 points. Each visualization must have a corresponding Python script in your repo that lists you as head developer for the script in its documentation. You may get help from your team and elsewhere. You should note any help in your documentation. Visualizations will be graded according to the following rubric:

+1 each adequate horizontal and vertical axis titles (must include units) andlabels.
+1 adequate chart title (must beinformative).
+3 visual appeal and readability (make your text bigenough).
+5 content.
-10 no corresponding script in your repo or a script that doesn’t reproduce your
The remaining 20 points will be my subjective evaluation of your performance, distributed as follows:

+5 demeanor. You will at least occasionally have to speak to groups of people throughout your career. You will be rewarded with respect and advancement for poise andcomposure,


rewarded with pity (from your friends and nicer colleagues) and contempt (from your detractors and the more douchy of your colleagues) for visible anxiety and signs of panic. Don’t worry, it is normal to be nervous – it’s what you do with it that counts. I recommend practice in advance and positive self-talk immediately before the presentation. Get excited about the material!

Your enthusiasm will show. Focus on how cool your project is instead of how much you hate public speaking. It will work wonders. Of course, the more work you put in on your project, the easier it will be to generate enthusiasm.

+5 effort. This will be a totally subjective assessment on my part of how hard it seems to me you worked on theproject.
+10 content. Did you discover and illustrate well anything interesting in your data? Do your visualizations justify your interpretation? Do you describe the content of your visualizations and what they mean clearly andconcisely?
At least one visualization must be a scatter plot with a regression line. Deductions:

-10 no scatter
-5 scatter plot but no regression


Git, due the same day and time as the images file (15 pts): You must not do any work on the master branch after it’s created. You must create and check out a development branch and do your work on that, then merge when you think you have a stable product. You must have at least three merges (4 pts each) and one issue (3 pts) per group member. Deductions:

-10 did work on the master branch. If you have problems at the beginning and manage to do some work on the master unintentionally, come see as soon as you realize you have worked on the master and made a commit. You won’t be


Attendance and participation (15 pts): Attendance at your lab on during presentation days is worth 15 pts. You must be on time – not one second late – to receive your attendance points. You must also ask at least one question during the Q&A at the end of the presentations about the content of that presentation. Deductions:

-5 never asked a


A sample of the many, many, many available data sets: https://www.kaggle.com/datasets https://www.kaggle.com/norc/general-social-survey http://www.cdc.gov/nchs/nsfg/ https://earthdata.nasa.gov/

http://nsidc.org/ http://www.spc.noaa.gov/wcm/#data https://www.data.gov/ http://census.gov/ https://nces.ed.gov/ http://www.nsf.gov/statistics/ http://grouplens.org/

https://www.ssa.gov/oact/babynames/limits.html http://www.fec.gov/disclosure.shtml

https://www.treasury.gov/resource-center/financial-education/Pages/fdd.aspx http://data.worldbank.org/

http://www.imf.org/external/datamapper/



http://murderdata.org/ https://www.cdc.gov/nchs/nhanes/ https://www.cdc.gov/nchs/ https://www.quandl.com/ https://www.ncdc.noaa.gov/ https://www.ncdc.noaa.gov/data-access/quick-links https://ourworldindata.org/ http://aa.usno.navy.mil/index.php

https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml