Standards Mapping

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.

• 13.1 Introduction to AI-Assisted Coding
• 13.2 Project: AI-Assisted Coding

3.A.4

Solve problems using critical thinking techniques and structured methodologies in data processing, model tuning, and troubleshooting data science pipelines.

• 10.5 Data's Role in Machine Learning
• 11.3 Bias in Training

3.A.5

Demonstrate leadership skills and collaborate effectively as a team member in data science projects, sharing insights and problem-solving strategies.

• 11.4 Ethics and AI

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.

• 1.5 Personal Data Security
• 1.6 Cybersecurity Essentials
• 1.7 Common Cyber Attacks and Prevention

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.

• 14.2 Exploring AI-Specific Career Paths

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.

• 9.5 Prompt Engineering

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.

• 3.8 Basic Python and Console Interaction Quiz
• 4.6 Conditionals Quiz
• 6.5 Looping Quiz

3.C.4

Understand ethical and legal considerations for technology use, including data privacy principles, intellectual property, and responsible AI practices.

• 1.4 Cyber Ethics and Laws
• 12.3 Deepfakes and Misinformation
• 12.7 AI Governance and the Future of AI

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.

• 10.5 Data's Role in Machine Learning

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.

• 10.1 Intro to Machine Learning

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.

• 11.1 How Are AI Models Trained?

3.G.3

Configure and verify model parameters for effective predictions in both supervised and unsupervised learning tasks.

• 10.2 Supervised Learning
• 10.3 Unsupervised Learning

3.G.4

Troubleshoot model performance issues and interpret model results using tools.

• 11.3 Bias in Training