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