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Standards Mapping

for Maryland Machine Learning and Data Science I

36

Standards in this Framework

12

Standards Mapped

33%

Mapped to Course

Standard Lessons
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
  1. 14.2 Exploring AI-Specific Career Paths
  2. 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.
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.
  1. 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.
  1. 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.
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.
  1. 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.
3.E.5
Discuss the impact of data science applications, such as predictive analytics, natural language processing, and computer vision, on industries and society.
  1. 9.4 Large Language Models
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.
  1. 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.
3.F.5
Use data visualization tools to represent data in meaningful ways, facilitating datadriven insights.
  1. 10.1 Intro to Machine Learning
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.
  1. 10.5 Data's Role in Machine Learning
3.F.8
Application of Machine Learning Algorithms
  1. 10.6 Project: Build a Sorting Machine
3.F.8.a
Application of Introductory Machine-Learning Algorithms (scaled with beginner datasets).
  1. 10.1 Intro to Machine Learning
3.G.1
Explain the purpose and structure of machine learning models, including supervised, unsupervised, and reinforcement learning.
  1. 10.2 Supervised Learning
  2. 10.3 Unsupervised Learning
  3. 10.4 Reinforcement Learning
3.G.2
Configure and verify model parameters for effective predictions in both supervised and unsupervised learning tasks.
  1. 10.2 Supervised Learning
  2. 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.