Standards in this Framework
Standards Mapped
Mapped to Course
| Standard | Lessons |
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3.A.1
Identify and demonstrate positive work behaviors that enhance employability and job advancement, such as regular attendance, promptness, proper attire, maintenance of a clean and safe work environment, and pride in work. |
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3.A.2
Demonstrate positive personal qualities such as flexibility, open-mindedness, initiative, active listening, and a willingness to learn. |
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3.A.3
Employ effective reading, writing, and technical documentation skills, particularly in reporting data analysis findings and model evaluations. |
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3.A.4
Solve problems using critical thinking techniques and structured methodologies in data processing, model tuning, and troubleshooting data science pipelines. |
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3.A.5
Demonstrate leadership skills and collaborate effectively as a team member in data science projects, sharing insights and problem-solving strategies. |
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3.A.6
Implement safety and data security procedures, including proper handling of data, adherence to data privacy regulations, and maintaining ethical standards in data use. |
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3.B.1
Develop a career plan that includes the necessary education, certifications, job skills, and experience for specific roles in data science and machine learning. |
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3.B.2
Create a professional resume and portfolio that reflects skills, projects, certifications, and recommendations relevant to data science. |
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3.B.3
Demonstrate effective interview skills for roles in data science and machine learning, focusing on technical and analytical expertise. |
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3.C.1
Use technology as a tool for research, organization, communication, and problem solving in data-related tasks. |
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3.C.2
Utilize digital tools, including computers, cloud platforms, collaboration tools, and data visualization software, to manage, process, and analyze information. |
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3.C.3
Demonstrate proficiency in using industry-standard technologies, including programming languages (Python, R), data processing libraries, and cloud computing platforms. |
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3.C.4
Understand ethical and legal considerations for technology use, including data privacy principles, intellectual property, and responsible AI practices. |
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3.D.1
Demonstrate the use of clear communication techniques, both written and verbal, that are consistent with industry standards in data presentation and reporting. |
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3.D.2
Apply mathematical concepts such as statistics, probability, and linear algebra in data analysis and machine learning model development. |
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3.D.3
Use scientific principles, such as data collection methods and hypothesis testing, in datadriven problem-solving. |
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3.E.1
Differentiate and configure cloud and on-premises data storage solutions, such as databases, data lakes, and data warehouses. |
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3.E.2
Explain and design data partitioning strategies for scalable data processing (e.g., sharding, chunking) |
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3.E.3
Create data flow diagrams that include hybrid and cloud-based components, focusing on scalability and fault tolerance. |
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3.F.1
Implement data schemas, normalization, and entity-relationship models to organize data effectively. |
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3.F.2
Configure and analyze machine learning models (e.g., regression, clustering, neural networks) for various applications. |
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3.F.3
Troubleshoot data preprocessing and model performance issues using diagnostic tools and evaluation metrics. |
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3.F.4
Teachers should receive professional development on diagnostic tools prior to delivering course. |
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3.G.1
Compare algorithms such as linear regression, decision trees, clustering, and neural networks, understanding differences in their application and effectiveness. |
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3.G.2
Design and apply machine learning models that include feature selection, model training, validation, and evaluation. |
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3.G.3
Configure and verify model parameters for effective predictions in both supervised and unsupervised learning tasks. |
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3.G.4
Troubleshoot model performance issues and interpret model results using tools. |
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