for Maryland Machine Learning and Data Science I — Digital Literacy and Artificial Intelligence
Total Standards: 36Mapped: 21Completion: 58%
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.
11.4 Ethics and AI
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
14.3 Looking Ahead at Careers & Applications
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.
9.5 Prompt Engineering
3.C.2
Use digital tools, including computers, cloud platforms, and data science software, to
access, manage, and analyze information.
13.2 Project: AI-Assisted Coding
3.C.3
Demonstrate proficiency in using emerging and industry-standard technologies,
including data visualization tools, programming languages like Python, and data
management applications.
13.2 Project: AI-Assisted Coding
3.C.4
Understand ethical and legal considerations for technology use, including principles of
data protection, intellectual property, and responsible data handling.
1.5 Personal Data Security
3.C.5
Describe limitations and risks (hallucinations, bias, privacy) and apply basic responsible
use practices for assignments with Artificial Intelligence.
12.2 Hallucinations and Security Risks
3.D.1
Demonstrate the use of clear communication techniques, both written and verbal, that
are consistent with industry standards.
9.6 Prompt Practice & Refinement
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.
10.6 Project: Build a Sorting Machine
3.E.4
Explain the importance of data cleaning, preparation, and validation in building
accurate and reliable machine learning models.
10.5 Data's Role in Machine Learning
3.E.5
Discuss the impact of data science applications, such as predictive analytics, natural
language processing, and computer vision, on industries and society.
9.4 Large Language Models
3.E.6
Interpret data visualizations and documentation to understand data trends and
patterns.
13.2 Project: AI-Assisted Coding
3.F.1
Select appropriate data sources and collection methods for different types of data
science projects, including structured and unstructured data.
10.6 Project: Build a Sorting Machine
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.
13.2 Project: AI-Assisted Coding
3.F.5
Use data visualization tools to represent data in meaningful ways, facilitating datadriven insights.
10.1 Intro to Machine Learning
3.F.6
Implement security measures for data protection, including access controls and data
encryption.
1.6 Cybersecurity Essentials
3.F.7
Diagnose and resolve data-related issues such as incomplete data sets, data drift, and
inconsistencies.
10.5 Data's Role in Machine Learning
3.F.8
Application of Machine Learning Algorithms
10.6 Project: Build a Sorting Machine
3.F.8.a
Application of Introductory Machine-Learning Algorithms (scaled with
beginner datasets).
10.1 Intro to Machine Learning
3.G.1
Explain the purpose and structure of machine learning models, including supervised,
unsupervised, and reinforcement learning.
10.2 Supervised Learning
10.3 Unsupervised Learning
10.4 Reinforcement Learning
3.G.2
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.3
Explain tokens, tokenization, and embeddings, and describe how decoder‑only
transformers use self‑attention to predict the next token.
