Data Checkpoint Feedback #7
Description
Data Checkpoint Feedback
Score (out of 10 pts)
Score = 8
Data Checkpoint Feedback
| Quality | Reasons | |
|---|---|---|
| Data relevance | Proficient | The dataset is directly related to your research question, but since your dataset only contains data for the elite players, I doubt whether they will stop playing because of losing. I would suggest find out what's the population of players of Marvel Rivals, and you can then see what's the proportion of the elite players you are analyzing, and you might want to slightly specify this in your research question. |
| Data description | Proficient | |
| Data wrangling | Developing | It would be helpful to include a sample visualization of your final cleaned dataset. You might want to consider how to further convert the datetime stamp, because the current 2026-02-13 04:40:11 format cannot be easily for numerical analysis, so probably try to convert into continous variable. Your data seems to contain the KDA of the player as well, so it could possibly be used for additional analysis if you find your original analysis idea simple or didn't work out. |
Note: In your data_checkpoint, you were graded on a description of an ideal dataset, but now you're being graded on an actual dataset. If you earn full marks on data relevance and description here, 50% of previous points lost previously on the Data section will be returned on your data_checkpoint...even though you aren't including information on your ideal dataset any longer. If full marks are not earned here, no points will be returned.
PLA Comments
No additional comments.
Data Checkpoint Regrade Feedback
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 10 points
Each Developing => -2 pts
Each Unsatisfactory=> -4 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 data_checkpoint feedback but did not do it to your satisfaction