Materials and outlines for the Final Project for COGS108.
Carefully read through all the details you'll need to know for your COGS108 Final Project.
- Option 1: Group (pdf here)
- Option 2: Individual (pdf here)
Due Friday, April 24 (11:59 PM)
Due Wed, June 10 (11:59 PM) - Finals Week
Students often ask for a rubric. You can use this checklist to help guide your thinking on the final project. If you check off all the boxes below, you should be in good shape to get a perfect score on your final project.
Overview:
- Write a clear summary of what you did.
- Briefly describe results of your project.
- Limit overview to 3-4 sentences.
Research Question:
- Include a specific, clear data science question.
- Make sure what you're measuring (variables) to answer question is clear.
Background & Prior Work:
- Include general introduction to your topic.
- Include explanation of what work has been done previously.
- Include citations or links to previous work.
Hypothesis:
- Include your team's hypothesis.
- Ensure that this hypothesis is clear to readers.
- Explain why you think this will be the outcome (what was your thinking?).
- Include an explanation of dataset(s) used (i.e. features/variables included, number of observations, information in dataset).
- Source included (if outside dataset(s) being used).
Data Cleaning & Pre-processing
- Perform Data Cleaning and explain steps taken OR include explanation as to why data cleaning was unnecessary (how did you determine your dataset was ready to go).
- Dataset actually clean and usable after data wrangling steps carried out.
Data Visualization
- Include at least three visualizations.
- Clearly label all axes on plots.
- Type of all plots appropriate given data displayed.
- Interpretation of each visualization included in text.
Data Analysis & Results
- EDA carried out with explanations of what was done and interpretations of output included.
- Appropriate analysis performed.
- Output of analysis interpreted and interpretation included in notebook.
- Thoughtful discussion of ethical concerns included.
- Ethical concerns consider the whole data science process (question asked, data collected, data being used, bias in data, analysis, post-analysis, etc.). .
- Clear conclusion (answer to the question being asked) and discussion of results.
- Limitations of analysis discussed.
- Does not ramble on beyond providing necessary information.
- Edit all text for clarity.
- Remove all instructions.
- Check to make sure all text and images are visible.
- Names and IDs included.
- Renamed file :
- Option 1:
FinalProject_groupXX.ipynb
, where 'XX' is replaced by your group's group number. - Option 2:
FinalProject_GH.ipynb
, where 'GH' is replaced by your GitHub Username.
- Option 1: