Data 102 students sharing their insights emerging from their final projects from Spring 2020 at CCAC North Campus
DAT-102: Final Projects Spring 2020 (with assessment data)
Browse our gallery of selected final projects created and shared during Spring 2020. To facilitate skill growth, instructor commentary is bundled with most projects. If your work is linked here and you'd like it taken down, please place a request to Eric via phone at 412.894.3020.
Project Profile: 121.83-datajobs
Files for download
Artifacts
121.83-datajobs-poster.jpg
Population classification tree
Scatter 1
Scatter 2
Scatter 3
Peer analysis of 121.83-datajobs
Rubric scoring against CCAC's Quantitative Scientific Reasoning general education rubric
| Interpretation | Representation | Calculation | Application/Analysis |
|---|---|---|---|
|
2
Response mentions experimental repeat but does not discuss what the difference in the three confidence levels means
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2
Unit of Analysis is not a salary, it's a job; Population is only jobs posted on site X
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3
Computed mean from raw data; point estimate not displayed on project display--good work; checked for mean in raw data spreadsheet
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3
Correctly interpreted the narrowness of the CI at all levels with "low" variation among jobs wages
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Project Profile: 121.90-soccerlacrosse
Files for download
Artifacts
Documentation
Scatter plot
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Peer analysis of 121.90-soccerlacrosse
Rubric scoring against CCAC's Quantitative Scientific Reasoning general education rubric
| Interpretation | Representation | Calculation | Application/Analysis |
|---|---|---|---|
|
3
competent
Notion of the probability of a sample of schools having a higher mean for soccer popularity than lacross is the core of the idea; not a 4 because details are lacking
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2
developing
Correct--Unit of Analysis is schools, but should be stated as "a school"; population sampled is not 100 (unitless)
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2
developing
Point estimate is for mean soccer popularity; standard error is not provided, so confidence intervals cannot be correctly analyzed
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3
competent
Applied assessor's sports domain knowledge to the data's suggestion that soccer is more popular than lacrosse; no mention of tradeoff from CI width, however
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Project Profile: 121.81-racingengines
Files for download
Artifacts
Figure 1
Figure 2
Peer analysis of 121.81-racingengines
Rubric scoring against CCAC's Quantitative Scientific Reasoning general education rubric
| Interpretation | Representation | Calculation | Application/Analysis |
|---|---|---|---|
|
3
competent
True mean vs. sample mentioned in Part B
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2
developing
Unit of analysis is not the ratio, it's a race car/bike engine; Population is not all cars, it's cars in the COTA
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4
advanced
correct point estimate verified with hand-check of spreadsheet file; CIs widen
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4
advanced
Discusses the notion of the true mean of all engines in the COTA
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Peer Reviewer 2
Rubric scoring against CCAC's Quantitative Scientific Reasoning general education rubric
| Interpretation | Representation | Calculation | Application/Analysis |
|---|---|---|---|
|
2
developing
Discusses outliers, not the changes in CI width or not notion of sample vs. population measures
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2
developing
Unit of analysis is an engine, not the ratio
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3
competent
accurate point estimators and CIs
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3
developing
Attempt to discuss value of the hp/weight ratio in context of the research, yielding the 3--along with the discussion of a need for a longer period of sampling
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Project Profile: 121.84-covidpolice
Files for download
Artifacts
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Conclusions
Peer analysis of 126.152
Rubric scoring against CCAC's Quantitative Scientific Reasoning general education rubric
| Interpretation | Representation | Calculation | Application/Analysis |
|---|---|---|---|
|
3
competent
Includes mention of population vs. sample in Part B
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2
developing
U of A is an arrested person, not age; population is accurate
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4
advanced
accurate center, SE, and CIs
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4
advanced
Discusses the nuances of the increasing CI width
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Project Profile: 121.86-sleepquality
Files for download
Artifacts
Figure 1a
Figure 1b
Figure 1c
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Peer analysis of 121.86-sleepquality
Rubric scoring against CCAC's Quantitative Scientific Reasoning general education rubric
| Interpretation | Representation | Calculation | Application/Analysis |
|---|---|---|---|
|
1
beginning
not answered
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1
beginning
not answered
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4
advanced
excellent use of spreadsheet tool
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1
beginning
not answered
|
Project Profile: 121.87-twitter
Files for download
Artifacts
Figure A
Figure B
Figure C
Figure D
Figure E
Figure 1
Figure 2
Peer analysis of 121.87-twitter
Rubric scoring against CCAC's Quantitative Scientific Reasoning general education rubric
| Interpretation | Representation | Calculation | Application/Analysis |
|---|---|---|---|
|
2
developing
reference to tail size is in the ballpark, but not accurate enough for competency
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2
developing
Unit of A is a single follower; population is accurate
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2
developing
point estimate was what seemed to be an incorrectly computed mean of number of followers, and not the ratio of likes/followers, which was the measured variable computational score; CIs increase, however
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3
competent
size of intervals discussed, but not at an advanced level since comparison of upper and lower bound values on CIs don't tell us skewness
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Project Profile: 121.82-womeninfilm
Files for download
Artifacts
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Peer analysis of 121.82
Rubric scoring against CCAC's Quantitative Scientific Reasoning general education rubric
| Interpretation | Representation | Calculation | Application/Analysis |
|---|---|---|---|
|
4
advanced
accurate notion of CIs reflecting error in pop param
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4
advanced
all accurate values
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4
advanced
used online CI tool to create accurate quant estimates
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3
competent
References population and sample proportion relationship in answer
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