Project Checkpoint Feedback

[ShanEllis]

Project Checkpoint Feedback

Score (out of 5 pts)

Score = 4

Data Checkpoint Feedback

	Quality	Reasons
Data relevance	E
Data description	D	The variables should be described here.
Data wrangling	D	Consider drop NAs and NaNs from the datasets. Also, it would be helpful to print the first few lines of the cleaned datasets.

Comments

Proposal Regrade Feedback

	Quality	Reasons
Abstract	NA
Research question	P	The variables were stated clearly.
Background	P	The background is proficient related to the current research question. All the variables mentioned in the question are addressed in the section. However, if you decide to include more variables and change the research question, remember to come back and do more background review.
Hypothesis	P	The hypothesis is clear and is backed up by the background. However, consider including more variables.
Data	P
Ethics	P
Team expectations	P	Sounds good.
Timeline	P	Sounds good.

Rubric

	Unsatisfactory	Developing	Proficient	Excellent
Data relevance	Did not have data relevant to their question. Or the datasets don't work together because there is no way to line them up against each other. If there are multiple datasets, most of them have this trouble	Data was only tangentially relevant to the question or a bad proxy for the question. If there are multiple datasets, some of them may be irrelevant or can't be easily combined.	All data sources are relevant to the question.	Multiple data sources for each aspect of the project. It's clear how the data supports the needs of the project.
Data description	Dataset or its cleaning procedures are not described. If there are multiple datasets, most have this trouble	Data was not fully described. If there are multiple datasets, some of them are not fully described	Data was fully described	The details of the data descriptions and perhaps some very basic EDA also make it clear how the data supports the needs of the project.
Data wrangling	Did not obtain data. They did not clean/tidy the data they obtained. If there are multiple datasets, most have this trouble	Data was partially cleaned or tidied. Perhaps you struggled to verify that the data was clean because they did not present it well. If there are multiple datasets, some have this trouble	The data is cleaned and tidied.	The data is spotless and they used tools to visualize the data cleanliness and you were convinced at first glance

Grading Rules

Scoring: Out of 5 points

Each Developing => -1 pts Each Unsatisfactory=> -2 pts until the score is 0

If students address the detailed feedback in a future checkpoint they will earn these points back

DETAILED FEEDBACK should be left in the data section AND anywhere the student addressed proposal feedback but did not do it to your satisfaction

[Pank1-2]

Based on the feedback given we cleaned up the data, dropping null values, and added a more detailed description of the variables in the data description section. All updates are pushed and updated onto EDA checkpoint.

[qzzz11]

Based on the feedback, we added a more detailed description of the variables in the data description section. We also cleaned up the data, dropping null values except for the weight column for female athletes because the data is not disclosed. A more detailed explanation is included in the data cleaning part of the notebook.