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 |
| 3.A.3.a | Ensure students have access to technical-writing style guides or integrated documentation platforms (e.g., markdown format). |
| 3.A.4 | Solve problems using critical thinking techniques and structured troubleshooting methodologies. |
| 3.A.4.a | Provide structured troubleshooting labs and critical-thinking case studies; and budget licenses for resources. |
| 3.A.5 | Demonstrate leadership skills and collaborate effectively as a team member. |
| 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 reflect skills, projects, certifications, and recommendations. |
| 3.B.3 | Demonstrate effective interview skills for roles in data science and machine learning. |
| 3.C.1 | Use technology as a tool for research, organization, communication, and problemsolving. |
| 3.C.2 | Use digital tools, including computers, cloud platforms, and data science software, to access, manage, and analyze information. |
| 3.C.3 | Demonstrate proficiency in using emerging and industry-standard technologies, including data visualization tools, programming languages like Python, and data management applications. |
| 3.C.4 | Understand ethical and legal considerations for technology use, including principles of data protection, intellectual property, and responsible data handling. |
| 3.C.5 | Describe limitations and risks (hallucinations, bias, privacy) and apply basic responsible use practices for assignments with Artificial Intelligence. |
| 3.D.1 | Demonstrate the use of clear communication techniques, both written and verbal, that are consistent with industry standards. |
| 3.D.2 | Apply mathematical concepts such as probability, statistics, and algebra in data analysis and machine learning applications. |
| 3.D.3 | Use scientific principles, such as data collection methodologies and hypothesis testing, in data-driven decision-making. |
| 3.E.1 | Describe the functions and characteristics of data science tools and environments, including Jupyter notebooks, Python libraries, and cloud-based data services. |
| 3.E.2 | Identify components required for modern data science projects, including data storage, data pipelines, and processing power. |
| 3.E.3 | Select the appropriate data analysis techniques based on the type and scale of the data and project requirements. |
| 3.E.4 | Explain the importance of data cleaning, preparation, and validation in building accurate and reliable machine learning models. |
| 3.E.5 | Discuss the impact of data science applications, such as predictive analytics, natural language processing, and computer vision, on industries and society. |
| 3.E.6 | Interpret data visualizations and documentation to understand data trends and patterns. |
| 3.F.1 | Select appropriate data sources and collection methods for different types of data science projects, including structured and unstructured data. |
| 3.F.2 | Explain data storage standards, data processing concepts, and database management techniques. |
| 3.F.3 | Configure data pipelines to ingest, clean, and transform data for analysis. |
| 3.F.4 | Perform data analysis tasks, including descriptive statistics, data transformations, and exploratory data analysis. |
| 3.F.5 | Use data visualization tools to represent data in meaningful ways, facilitating datadriven insights. |
| 3.F.6 | Implement security measures for data protection, including access controls and data encryption. |
| 3.F.7 | Diagnose and resolve data-related issues such as incomplete data sets, data drift, and inconsistencies. |
| 3.F.8 | Application of Machine Learning Algorithms |
| 3.F.8.a | Application of Introductory Machine-Learning Algorithms (scaled with beginner datasets). |
| 3.G.1 | Explain the purpose and structure of machine learning models, including supervised, unsupervised, and reinforcement learning. |
| 3.G.2 | Configure and verify model parameters for effective predictions in both supervised and unsupervised learning tasks. |
| 3.G.3 | Explain tokens, tokenization, and embeddings, and describe how decoder‑only transformers use self‑attention to predict the next token. |
