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Rubric Name: DS2002 Rubric
This table lists criteria and criteria group name in the first column. The first row lists level names and includes scores if the rubric uses a numeric scoring method.Rubric Criteria
0 - Not Present
1 - Needs Improvement
2 - Meets Expectations
Learning Objective 1.1 - Explain data science methodology
Response is missing.
Explanation of how subsequent steps in the methodology are so dependent on getting this first step correct is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete explanation of how subsequent steps in the methodology are so dependent on getting this first step correct.
Learning Objective 1.2.1 - Describe the process of defining a question for data scientists
Response is missing.
Description of the process the data science team at Nutri Mondo used to define the business understanding and the analytical approach is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete description of the process the data science team at Nutri Mondo used to define the business understanding and the analytical approach.
Learning Objective 1.2.2 - Describe the process of defining a question for data scientists
Response is missing.
Explanation of why this process is an important first step for data scientists as they begin their projects is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete explanation of why this process is an important first step for data scientists as they begin their projects.
Learning Objective 2.1 - Describe data collection processes of data scientists
Response is missing.
Description of the data requirements and data collection processes for the Nutri Mondo case study is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete description of the data requirements and data collection processes for the Nutri Mondo case study.
Learning Objective 2.2 - Explain how data requirements and data collection apply to data science problems
Response is missing.
Explanation of how the business understanding and analytical approach align with the data requirements and the data collection process in the work of the data science team is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete explanation of how the business understanding and analytical approach align with the data requirements and the data collection process in the work of the data science team.
Learning Objective 3.1 - Describe how to construct a data set
Response is missing.
Description of the steps taken by the data science team to construct the data set for the case study is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete description of the steps taken by the data science team to construct the data set for the case study.
Criterion Feedback
I'm not able to provide credit for someone else's words
Learning Objective 3.2 - Describe the process used to identify initial patterns in collected data
Response is missing.
Description of the initial patterns identified by the team and what these patterns might mean is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete description of the initial patterns identified by the team and what these patterns might mean.
Criterion Feedback
I'm not able to provide credit for someone else's words
Learning Objective 3.3 - Describe how data understanding and data preparation apply to data science problem
Response is missing.
Description of how the data science team is exploring and preparing the data is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete description of how the data science team is exploring and preparing the data.
Learning Objective 4.1 - Explain the purpose of data modeling
Response is missing.
Explanation of the purpose of data modeling and what it accomplishes is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete explanation of the purpose of data modeling and what it accomplishes.
Learning Objective 4.2.1 - Describe how data scientists evaluate models
Response is missing.
Description of why modeling comes after data scientists understand and prepare their data is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete description of why modeling comes after data scientists understand and prepare their data.
Learning Objective 4.2.2 - Describe how data scientists evaluate models
Response is missing.
Description of how a data scientist ensures that modeling responds to the original business understanding is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete description of how a data scientist ensures that modeling responds to the original business understanding.
Learning Objective4.2.3,- Describe how data scientists evaluate models
Response is missing.
Description of the processes that the data science team at Nutri Mondo is using to evaluate the models they created is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete description of the processes that the data science team at Nutri Mondo is using to evaluate the models they created.
Learning Objective 5.1.1 - Explain the process of deploying models with stakeholders
Response is missing.
Explanation of the different ways that the data science team at Nutri Mondo could deploy what they have found in the data is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete explanation of the different ways that the data science team at Nutri Mondo could deploy what they have found in the data.
Learning Objective 5.1.2 - Explain the process of deploying models with stakeholders
Response is missing.
Explanation of how you would deploy the data—including your reasoning—is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete explanation of how you would deploy the data, including your reasoning.
Learning Objective 5.2 - Explain the importance of feedback
Response is missing.
Explanation of the feedback the data science team are receiving from others in the organization—including how the feedback is providing insights for the data science team to refine their model—is vague, inaccurate, and/or incomplete.
Response provides a clear, accurate, and complete explanation of the feedback the data science team are receiving from others in the organization, including how the feedback is providing insights for the data science team to refine their model.
This table lists criteria and criteria group name in the first column. The first row lists level names and includes scores if the rubric uses a numeric scoring method.Professional Skills Assessment
0 - Not Present
1 - Needs Improvement
2 - Meets Expectations
Written Communication WC 1 - Construct complete and correct sentences
Sentences are incoherent and impede reader’s access to ideas.
Sentences are incomplete and/or include fragments and run-on sentences, limiting reader’s access to ideas.
Sentence structure effectively conveys meaning to the reader.
Written Communication WC 2 - Demonstrate the effective use of grammar and mechanics
Multiple inaccuracies in grammar and mechanics impede reader’s access to ideas.
Some inaccuracies in grammar and mechanics limit reader’s access to ideas.
Use of grammar and mechanics is straightforward and effectively conveys meaning to reader.
Written Communication WC 3 - Create cohesive paragraphs with a clear central idea
Paragraphs, or lack of paragraphs, impede reader’s access to ideas.
Construction of main idea and/or supporting paragraphs limit reader’s access to ideas.
Main idea and/or supporting paragraphs effectively convey meaning to reader.
Written Communication WC 4 - Use supporting material to support a claim
Supporting materials are not present.
Supporting material is used inconsistently or inappropriately.
Supporting material is used to enhance meaning. Writing is appropriately paraphrased and uses direct quotes as applicable.
Criterion Feedback
It is fine to use others' words to support your own, original thoughts, but those words should be used sparingly and only to support your own, original ideas
Written Communication WC 5 - Demonstrate appropriate essay level writing skills, providing transitions between an introduction, body, and conclusion
Ideas are disorganized with no/poor transitions.
Ideas are loosely organized with unclear paragraphing and transitions.
Ideas are organized with cohesive transitions.
Written Communication WC 6 - Identify sources
Sources are missing.
Writing inconsistently identifies or misrepresents sources.
Writing clearly identifies the source of nonoriginal material and/or ideas.
Information Literacy IL 1 - Identify and locate credible sources
No sources or non-credible sources are present.
Sources are inconsistently credible, appropriate, and relevant to the topic and/or assessment.
Sources are mostly credible, appropriate, and relevant to the topic and/or assessment.
Inquiry & Analysis IA 1 - Identify a problem or question in a selected area of study
No problem or question is presented.
The problem or question is vague or inappropriate to the selected field of study.
The problem or question is clearly stated in a form appropriate to the selected field of study.
Inquiry & Analysis IA 2 - Use a logical organizing principle to identify the key parts or elements of a problem or question in a selected field of study
Elements or parts of the problem or question are not presented.
Elements or parts of the problem or question are presented in a disorganized manner.
Elements or parts of the problem or question are presented in an organized manner.
Inquiry & Analysis IA 4 - Apply organizing principles and theoretical approaches to identify solutions to a problem
No attempt is made to connect theories or organizing principles to solutions to the problem.
Connections between theories or organizing principles solutions to the problem are vague.
Theories and organizing principles are used to make connections, identify gaps, and/or provide evidence for showing solutions to the problem or questions.
This table lists criteria and criteria group name in the first column. The first row lists level names and includes scores if the rubric uses a numeric scoring method.Mastery Rubric
No
Yes
Learning Objective 1.1 - Analyze multiple, relevant sources and examples to apply a data science framework to a business problem
Responses do not consistently integrate information from relevant sources and examples to apply a data science framework to a business problem.
Responses consistently integrate information from relevant sources and examples to apply a data science framework to a business problem.
Overall Score
Overall Score
0 - Not Present
1 - Needs Improvement
2 - Meets Expectations
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