Florida Department of Education
Curriculum Framework

Program Title:	Data Science and Machine Learning
Program Type:	Non-Career Preparatory
Career Cluster:	Information Technology

Secondary – Non-Career Preparatory	
Program Number	9007700	
CIP Number	0530700101	
Grade Level	9-12	
Program Length	4 credits	
Teacher Certification	Refer to the Program Structure section.	
CTSO	FL-TSA, SkillsUSA	
CTE Program Resources 	http://www.fldoe.org/academics/career-adult-edu/career-tech-edu/program-resources.stml	

Purpose 

This program offers a sequence of courses that provides coherent and rigorous content aligned with challenging academic standards and relevant technical knowledge and skills needed to prepare for further education and Data Analytics and Data Science-enabled careers in the Information Technology career cluster; provides technical skill proficiency, and includes competency-based applied learning that contributes to the academic knowledge, higher-order reasoning and problem-solving skills, work attitudes, general employability skills, technical skills, and occupation-specific skills, and knowledge of all aspects of the Artificial Intelligence (AI) and Machine Learning required for such data professionals working on business and academic environments in the Information Technology career cluster.  The intention of this course is to prepare students to be successful both personally and professionally in an increasingly data-focused society that demands more thorough data understanding and fluent analytic skills.

The content includes to fundamental understanding and application of data analytics, data visualization, relational database design, machine learning, societal impacts of AI/ML, AI/ML systems and their components, problems and tools AI-enabled workers use to build models and systems that leverage data to make decisions, and mastery of foundational skills required to become power ML/AI users. In addition, the course content includes but is not limited to practical experiences in AI/ML system design, deployment, and evaluation; problem identification; creation, selection, and curation of data sets; computer programming, use of machine learning algorithms, program design structure, evaluation of the societal impact of AI, employing ethical and responsible development methodologies and decision making, essential programming techniques, and implementation issues. Specialized programming skills involving advanced mathematical calculations and statistics are also integrated into the curriculum.  

Additional Information relevant to this Career and Technical Education (CTE) program is provided at the end of this document.

Program Structure

This program is a planned sequence of instruction consisting of 4 credits. 

To teach the course(s) listed below, instructors must hold at least one of the teacher certifications indicated for that course.

The following table illustrates the secondary program structure:

Course Number	Course Title	Teacher Certification	Length	Level	Graduation Requirement	
9007710

OR

	Foundations of Programming for Data Science and Artificial Intelligence 

OR

	BUS ED 1@2
COMPU SCI 6
INFO TECH 7G
WEB DEV 7G
COMP PROG 7G
ENG 7G
EG TEC 7G
TECH ED 1 @2
ENG & TEC ED 1@2
MATH 6-12
Agriculture (6-12)
Computer Science (K12)
Business Education (6-12)
Engineering and Technology Education (6-12)	1 credit

	3

	

CT	
9007220	Procedural Programming	BUS ED 1@2 
COMPU SCI 6 
COMP PROG 7G	1 credit	3	CT	
9007720	Data Analytics and Database Design	Refer to the first two courses for the Teacher Certifications	1 credit	3	CT	
9007730	Machine Learning and Applications		1 credit	3	CT	
9007740	Capstone Project with Industry Partners		1 credit	3	CT	
(Graduation Requirement Codes: CT=Career & Technical Education, EQ= Equally Rigorous Science, EC= Economics, MA=Mathematics, PL=Personal Financial Literacy)

Common Career Technical Core – Career Ready Practices

Career Ready Practices describe the career-ready skills that educators should seek to develop in their students. These practices are not exclusive to a Career Pathway, program of study, discipline or level of education. Career Ready Practices should be taught and reinforced in all career exploration and preparation programs with increasingly higher levels of complexity and expectation as a student advances through a program of study. 

1. Act as a responsible and contributing citizen and employee. 

2. Apply appropriate academic and technical skills. 

3. Attend to personal health and financial well-being. 

4. Communicate clearly, effectively and with reason. 

5. Consider the environmental, social and economic impacts of decisions. 

6. Demonstrate creativity and innovation. 

7. Employ valid and reliable research strategies. 

8. Utilize critical thinking to make sense of problems and persevere in solving them. 

9. Model integrity, ethical leadership and effective management. 

10. Plan education and career path aligned to personal goals. 

11. Use technology to enhance productivity. 

12. Work productively in teams while using cultural/global competence. 

Standards

After successfully completing this program, the student will be able to perform the following:

Foundations of Programming for Data Science and Artificial Intelligence
01.0	Explain and use design thinking to solve a problem.
02.0	Develop an awareness of microcomputers and how they work. 
03.0	Know the basic structure of a CPU (Central Processing Unit) and GPU (Graphics Processing Unit).
04.0	Explain what an ALU (Arithmetic Logic Unit) is and how it works.
05.0	Demonstrate understanding about various aspects of digital memory.
06.0	Develop awareness of computer languages, web-based and software applications, and emerging technologies related to AI and Data Science. 
07.0	Explore the characteristics, tasks, work attributes, options, and tools associated with a career in data science.
08.0	Demonstrate an understanding of the characteristics, use, and selection of numerical, non-numerical, and logical data types. 
09.0	Distinguish between iterative and non-iterative program control structures. 
10.0	Describe the processes, methods, and conventions for software development and maintenance for data science.
11.0	Explain the types, uses, and limitations of testing for ensuring quality control.
12.0	Create a program design document to support engineering design of a program or product.
13.0	Create programs that solve a problem using non-iterative and iterative algorithms.
14.0	Design a computer program to meet specific physical, operational, and interaction criteria for data science.
15.0	Create and document a computer program that uses a variety of internal and control structures for manipulating varied data types.
16.0	Differentiate among procedural, object-oriented, compiled, interpreted, and translated programming languages.
17.0	Create and document an interactive computer program that employs functions, subroutines, or methods to receive, validate, and process user input or data sets.
18.0	Be able to read and write data (file I/O) to and from a program.
19.0	Solve problems using critical thinking skills, engineering design, creativity and innovation.
20.0	Describe the importance of ethical and fair use, security and privacy information sharing, ownership, licensure and copyright of created programs and data.

OR

Procedural Programming
21.0	Design a computer program to meet specific physical, operational and interaction criteria.
22.0	Create and document a computer program that uses a variety of internal and control structures for manipulating varied data types.
23.0	Create and document an interactive computer program that employs functions, subroutines, or methods to receive, validate, and process user input.
24.0	Effectively communicate and collaborate.
25.0	Demonstrate responsible use of technology and information.
26.0	Differentiate among procedural, object-oriented, compiled, interpreted, and translated programming languages.

Data Analytics and Database Design
27.0	Generate and tell stories with data.
28.0	Think critically about data.
29.0	Collect, analyze, and visualize a dataset to gain insight.
30.0	Know and apply best practices in data visualization.
31.0	Construct interactive dashboards following best practices.
32.0	Understand and apply concepts in probability.
33.0	Understand and apply concepts in basic statistics.
34.0	Understand and apply concepts in statistical sampling.
35.0	Understand and apply concepts in hypothesis testing. 
36.0	Be aware of the limitations of statistics and cognitive biases in data analysis.
37.0	Understand how data is accessed, sorted, and stored.
38.0	Become more SQL literate.
39.0	Understand design considerations and apply best practices in designing SQL databases.
40.0	Identify data privacy/ data governance issues and methods to mitigate them.
41.0	Understand common cybersecurity issues and mitigation methods.

Machine Learning and Applications
42.0	Identify and define intelligent behavior. 
43.0	Articulate the relationship between AI, Machine Learning, and Computer Science. 
44.0	Identify and describe the types of representations and algorithms designed into AI-enabled technologies. 
45.0	Define and investigate examples of AI applications. 
46.0	Explain how domain knowledge is used in the design of AI systems. 
47.0	Describe machine learning algorithms in AI-enabled technologies. 
48.0	Explain the key technical challenges in design and responsible use of AI technologies. 
49.0	Describe different types of data and how they are used in AI. 
50.0	Explain and use design thinking to solve a problem. 
51.0	Apply the machine learning life cycle in the development and use of a machine learning model. 
52.0	Recognize and identify mathematical principles upon which machine learning and AI are built such as calculus, linear algebra, probability, statistics, and optimization partial derivatives. 
53.0	Train and evaluate a range of ML models based on specific accuracy, inclusivity, and ethical design criteria. 
54.0	Use and evaluate supervised learning techniques to classify or predict outputs. 
55.0	Use and evaluate unsupervised learning techniques to solve problems. 
56.0	Understand neural networks and their components. 
57.0	Use and evaluate different types of neural network architectures and their applications. 
58.0	Use and evaluate reinforcement learning techniques to solve problems. 
59.0	Research and explain the advancements in computing hardware that make AI possible. 
60.0	Understand and articulate how AI can impact society in both positive and negative ways. 
61.0	Understand the best practices and key characteristics of bias, fairness, transparency, explainability, accountability of ethically designed AI systems and decision-making practices. 
62.0	Identify different kinds of data, their sources, and how they might be used in decision making. 
63.0	Critique data and data-based claims to avoid being misled by data through identifying bias, confounding, and random error. 
64.0	Describe the limitations of machine learning and the decisions that can be made with data. 
65.0	Explore the characteristics, tasks, work attributes, options, and tools associated with AI-enabled careers. 
66.0	Identify how leadership development, school and community service projects and competitive events are integral parts of career and technology education.

Capstone Project with Industry Partners
67.0	Explain and use design thinking to solve a problem. 
68.0	Design AI solutions using embedded computing (as applicable to specific projects). 
69.0	Explore the characteristics, tasks, work attributes, options, and tools associated with AI-enabled careers and educational pathways to achieve these career goals.  
70.0	Use appropriate tools to design an AI System to solve problems. 
71.0	Set up and use a machine learning (ML) pipeline to solve a problem. 
72.0	Appropriately use automated services to accomplish common tasks. 
73.0	Use data analysis and visualization tools to work with datasets and gain insights. 
74.0	Apply the machine learning life cycle in the development and use of a machine learning model. 
75.0	Design and develop AI systems to solve a problem or design solutions for social and ethical issues. 
76.0	Create a portfolio of AI projects that demonstrate ability to program machine learning models using a wide range of AI algorithms. 
77.0	Research and evaluate various AI careers involved in AI system usage, design, development, deployment, and maintenance.

Florida Department of Education
Student Performance Standards

Course Title: 	Foundations of Programming for Data Science and Artificial Intelligence 
Course Number: 	9007710
Course Credit:	1

Course Description:

The purpose of this course is to provide students with the computer programming skills necessary to perform AI applications. Topics will include the ethical and societal importance of AI, awareness of microcomputers, arithmetic log unit, various aspects of digital memory, computer programming languages and emerging technology in Artificial Intelligence and Data Science. Instruction and learning activities are provided in a laboratory setting using hands-on experiences with the equipment, materials and technology appropriate to the course content and in accordance with current practices.

CTE Standards and Benchmarks	
01.0	Explain and use design thinking to solve a problem. The student will be able to:	
01.01	Distinguish between social and personal problems to be solved through a design.	
01.02	Identify a challenge of social importance and personal interest.	
01.03	Students develop problem statements embedded within a complex challenge.	
01.04	Understanding challenges and user needs more deeply.	
01.05	Pattern and sort user data to define design challenges.	
01.06	Use a formal brainstorming process to generate ideas.	
01.07	Test and iterate prototypes.	
01.08	Refine prototypes based on user feedback.	
01.09	Create stakeholder maps.	
02.0	Develop an awareness of microcomputers and how they work. The student will be able to:	
02.01	Explain the general architecture of a microcomputer system.	
02.02	Explain the need for and use of peripherals (e.g., keyboards, sensory input, geospatial input).	
02.03	Demonstrate proficiency using peripherals.	
02.04	Differentiate between diagnosing and troubleshooting peripherals.	
02.05	Describe the necessary components for data storage and memory, and how it affects programming (e.g., RAM, ROM).	
02.06	Differentiate between multiple levels of hardware and software (e.g., CPU hardware, operating system, translation, interpretation) that support program execution.	
02.07	Evaluate various forms of input and output (e.g., IO, storage devices, digital media).	
03.0	Know the basic structure of a CPU (Central Processing Unit) and GPU (Graphics Processing Unit). The students will be able to:	
03.01	Know the basic functions a CPU performs.	
03.02	Identify the main components within a CPU and describe their functionality - Memory or Storage Unit, Control Unit, and ALU (Arithmetic Logic Unit).	
03.03	Know the basic functions a GPU performs.	
03.04	Compare and contrast how CPU and GPU are structured differently and have different applications among Data Science use cases.	
04.0	Explain what an ALU (arithmetic logic unit) is and how it works. The students will be able to:	
04.01	Understand that an ALU is responsible for performing arithmetic and logical functions or operations.	
04.02	Provide examples of the logical operations that an ALU performs (e.g., AND, OR, XOR, etc.).	
04.03	Provide examples of the arithmetic functions that an ALU performs (e.g., add, subtract, negation, etc.).  	
04.04	Provide examples of the bit-wise operations that an ALU performs (e.g., left, right, rotation, and their purposes).	
05.0	Demonstrate understanding about various aspects of digital memory. The students will be able to	
05.01	Know that computer registers are memory units inside a CPU designed to be small and fast for frequently accessed data.	
05.02	Give examples of different types of registers and describe their specific functions (e.g., instruction, program counter, memory address, memory buffer, etc.).	
05.03	Know that an address is a reference to a specific memory location that software or hardware can understand.	
05.04	Describe different address types, units of address resolution, and address schemes.	
05.05	Compare and contrast stack vs. heap memory allocation schemes - describe them and list their respective benefits and drawbacks.	
06.0	Develop awareness of common computer programming languages, software applications, career tracks, and emerging technology in Data Science and AI. The student will be able to:	
06.01	Identify the main programming languages used for Data Science – compare and contrast their benefits and barriers to use.	
06.02	Understand the difference between software applications for Data Science vs. traditional program automation. Distinguish between local machine, web-based, network-based, or supercomputer-based applications.	
06.03	Understand the applications of Data Science to provide information and solve problems across multiple industries, including locally based ones.	
06.04	Explore careers in Data Science and AI, including those that require postsecondary education.	
06.05	Identify emerging technologies that can implement and apply Data Science, as well as display and inform potential stakeholders about use cases.	
07.0	Explore the characteristics, tasks, work attributes, options, and tools associated with a career in Data Science. The student will be able to:	
07.01	Explore a variety of careers to which computing is central.	
07.02	Discuss the impact of computing on business and commerce (e.g., automated inventory processing, financial transactions, e-commerce, virtualization, and cloud computing).	
07.03	Discuss the applications of Data Science within various industry sectors.	
07.04	Identify design tasks performed by programmers (e.g. problem definition, requirements gathering, stakeholder alignment).	
07.05	Describe how businesses use computer programming to solve business problems.	
07.06	Investigate job opportunities in the programming field.	
07.07	Explain different specializations and relevant training in the computer programming field.	
07.08	Explain the need for continuing education and training of computer programmers.	
07.09	Explain software as a service (SaaS) and how it impacts business.	
07.10	Describe ethical responsibilities of computer programmers (e.g., impacts of irresponsible use of information) (e.g., plagiarism and falsification of data) on collaborative projects.	
07.11	Identify credentials and certifications that may improve employability for a computer programmer.	
07.12	Identify devices, tools, and other environments for which programmers may develop software.	
08.0	Demonstrate an understanding of the characteristics, use, and selection of numerical, non-numerical, and logical data types. The student will be able to	
08.01	Identify the characteristics (e.g., size, limits) and uses of different numerical and non-numerical data types.	
08.02	Explain the types and uses of variables in programs.	
08.03	Determine the best data type to use for given programming problems.	
08.04	Compare and contrast simple data structures and their uses.	
08.05	Identify the types of operations that can be performed on different data types (e.g., math operations on numerical data types, concatenation, and other string operations).	
08.06	Evaluate arithmetic and logical expressions using appropriate operator precedence.	
08.07	Explain how computers store different data types in memory.	
08.08	Demonstrate the difference between "data" and "information".	
08.09	Use different number systems to represent data.	
08.10	Explain how national and international standards (i.e., ASCII, UNICODE) are used to represent non-numerical data.	
08.11	Use Boolean logic to perform logical operations using Boolean algebra and truth tables.	
09.0	Distinguish between iterative and non-iterative program control structures. The student will be able to	
09.01	Identify the uses of non-iterative and iterative programming structures using pseudocode and flowcharts.	
09.02	Create iterative programming structures and their uses.	
09.03	Explain how sequence, selection, and iteration are building blocks of algorithms.	
10.0	Describe the processes, methods, and conventions for software development and maintenance for data science. The student will be able to:	
10.01	Describe a software development process that is used to solve problems at different software development stages.	
10.02	Define alternative methods of program development (e.g., rapid prototyping, waterfall, spiral model, peer coding).  	
10.03	List and explain the steps in the program development cycle.	
10.04	Describe different types of documentation used in the program development cycle (e.g., requirements document, program design documents, test plans).	
10.05	Describe different methods used to facilitate version control.	
11.0	Explain the types, uses, and limitations of testing for ensuring quality control. The student will be able to:	
11.01	Explain the uses and limits of testing in ensuring program quality.	
11.02	Explain testing performed at different stages of the program development cycle (e.g., unit testing, system testing, user acceptance testing).	
11.03	Describe and identify types of programming errors.	
12.0	Create a program design document to support engineering design of a program or product The student will be able to:	
12.01	Describe different design methodologies and their uses (e.g., object-oriented design, structured design, rapid application development).	
12.02	Describe and use tools for developing a program design (e.g., flowcharts, design documents, pseudocode).	
12.03	Explain the role of existing libraries and packages in facilitating programmer productivity.	
12.04	Participate and contribute to a design review of a program design developed using a common program design tool (e.g., UML, flowcharts, design documents, pseudocode).	
12.05	Develop a software artifact (independently and collaboratively) in phases (or stages) according to a common software development methodology (e.g., Waterfall or Spiral model).	
12.06	Define input and output for a program module using standard design methodology.	
13.0	Create programs that solve a problem using non-iterative and iterative algorithms. The student will be able to:	
13.01	Apply the developmental cycle methodologies to create a program.	
13.02	Develop a program using string and/or numeric data types.	
13.03	Develop a program using sequential algorithms.	
13.04	Develop a program using selection structures.	
13.05	Develop a program using looping structures.	
14.0	Design a computer program to meet specific physical, operational, and interaction criteria. The student will be able to:	
14.01	Choose appropriate data types depending on the needs of the program.	
14.02	Define appropriate user prompts for clarity and usability (e.g., user guidance for data ranges, data types).	
14.03	Design and develop program for efficiency (e.g., less memory usage, fewer I/O parameters, faster processing).	
14.04	Compare techniques for analyzing massive data collections.	
14.05	Identify the software environment required for a program to run (e.g., operating system required, delivery method such as mobile/ web-based/ desktop).	
14.06	Create mobile computing applications and/or dynamic webpages by using a variety of design and development tools, programming languages and mobile devices/ emulators.	
14.07	Explain the role of an application programming interface (API), and distinguish between a programming language’s syntax and the API.	
14.08	Identify the tools required to develop a program (e.g., editors, compilers, linkers, integrated development environments, APIs, libraries).	
15.0	Create and document a computer program that uses a variety of internal and control structures for manipulating varied data types. The student will be able to:	
15.01	Use appropriate naming conventions to define program variables and methods.	
15.02	Use a program editor to write the source code for a program.	
15.03	Write programs that use selection structures.	
15.04	Write programs that use repetition structures.	
15.05	Write programs that use nested structures.	
15.06	Use internal documentation (e.g., single-line and multi-line comments, program headers, module descriptions, meaningful variable, and function/module names) to document a program according to accepted standards.	
15.07	Compile, run, test and debug programs.	
15.08	Write programs that use standard arithmetic operators with different numerical data types.	
15.09	Write programs that use standard logic operators.	
15.10	Write programs that use a variety of common data types.	
15.11	Write programs that perform data conversion between numeric and string data types.	
15.12	Write programs that define, use, search, and sort arrays.	
16.0	Differentiate among procedural, object-oriented, compiled, interpreted, and translated programming languages. The student will be able to:	
16.01	Differentiate between multiple levels of operating system, translation, and interpretation that support program execution.	
16.02	Explain the program execution process (by an interpreter and in CPU hardware).	
16.03	Describe object-oriented concepts.	
16.04	Explain the characteristics of procedural and object-oriented programming languages.	
16.05	Compare and contrast programming languages that are compiled, interpreted, and translated.	
17.0	Create and document an interactive computer program that employs functions, subroutines, or methods to receive, validate, and process user input. The student will be able to:	
17.01	Determine the results of code segments.	
17.02	Write programs that perform user input and output.	
17.03	Write programs that validate user input (e.g., range checking, data formats, valid/invalid characters).	
17.04	Write program modules such as functions, subroutines, or methods.	
17.05	Write program modules that accept arguments.	
17.06	Write program modules that return values.	
17.07	Write program modules that validate arguments and return error codes.	
17.08	Design and implement a simple simulation algorithm to analyze, represent and understand natural phenomena.	
17.09	Use APIs and libraries to facilitate programming solutions.	
17.10	Participate in a peer code review to verify program functionality, programming styles, program usability, and adherence to common programming standards.	
17.11	Explain how abstraction manages complexity.	
18.0	Be able to read and write data (file I/O) to and from a program. The student will be able to	
18.01	Be able to read in data from a file as text or binary.	
18.02	Be able to write data to a file as text or binary.	
18.03	Stream data to a text or binary file.	
18.04	Recognize and mitigate potential file handles and text encoding issues.	
19.0	Solve problems using critical thinking skills, creativity and innovation. The student will be able to	
19.01	Employ critical thinking skills independently and in teams to solve problems and make decisions.	
19.02	Employ critical thinking and collaborative skills to resolve conflicts.	
19.03	Identify and document workplace performance goals and monitor progress toward those goals.	
19.04	Conduct technical research to gather information necessary for decision-making.	
19.05	Discuss digital tools or resources to use for a real-world task based on their efficiency and effectiveness, individually and collaboratively.	
20.0	Describe the importance of security and privacy information sharing, ownership, licensure and copyright. The student will be able to:	
20.01	Describe security and privacy issues that relate to computer networks including the permanency of data on the Internet, online identity, and privacy.	
20.02	Discuss the impact of government regulation on privacy and security.	
20.03	Describe how different types of software licenses (e.g., open source and proprietary licenses) can be used to share and protect intellectual property.	
20.04	Explain how access to information may not include the right to distribute the information.	
20.05	Describe differences between open source, freeware, and proprietary software licenses, and how they apply to different types of software.	
20.06	Discuss security and privacy issues that relate to computer networks.	
20.07	Identify computer-related laws and analyze their impact on digital privacy, security, intellectual property, network access, contracts, and harassment.	
20.08	Understand, describe and implement FAIR data practices.	

Florida Department of Education
Student Performance Standards

Course Title: 	Procedural Programming
Course Number: 	9007220
Course Credit:	1

Course Description:

This course continues the study of computer programming concepts with a focus on the creation of software applications employing procedural programming techniques.  

CTE Standards and Benchmarks	
21.0	Design a computer program to meet specific physical, operational and interaction criteria. The student will be able to: 	
21.01	Choose appropriate data types depending on the needs of the program.	
21.02	Define appropriate user prompts for clarity and usability (e.g., user guidance for data ranges, data types).	
21.03	Design and develop program for efficiency (e.g., less memory usage, less inputs/outputs, faster processing).	
21.04	Compare techniques for analyzing massive data collections.	
21.05	Identify the software environment required for a program to run (e.g., operating system required, mobile, web-based, desktop, delivery method).	
21.06	Create mobile computing applications and/or dynamic webpages through the use of a variety of design and development tools, programming languages and mobile devices/emulators.	
21.07	Explain the role of an application programming interface (API) in the development of applications and the distinction between a programming language’s syntax and the API.	
21.08	Identify the tools required to develop a program (e.g., editors, compilers, linkers, integrated development environments, APIs, libraries).	
22.0	Create and document a computer program that uses a variety of internal and control structures for manipulating varied data types. The student will be able to:	
22.01	Use appropriate naming conventions to define program variables and methods.	
22.02	Use a program editor to write the source code for a program.	
22.03	Write programs that use selection structures.	
22.04	Write programs that use repetition structures.	
22.05	Write programs that use nested structures.	
22.06	Use internal documentation (e.g., single-line and multi-line comments, program headers, module descriptions, meaningful variable and function/module names) to document a program according to accepted standards.	
22.07	Compile, run, test and debug programs.	
22.08	Write programs that use standard arithmetic operators with different numerical data types.	
22.09	Write programs that use standard logic operators.	
22.10	Write programs that use a variety of common data types.	
22.11	Write programs that perform data conversion between standard data types.	
22.12	Write programs that define, use, search, and sort arrays.	
22.13	Write programs that use user-defined data types.	
22.14	Demonstrate understanding and use of appropriate variable scope.	
22.15	Use global and local scope appropriately in program implementation.	
22.16	Distinguish between binary and sequential searches.	
23.0	Create and document an interactive computer program that employs functions, subroutines, or methods to receive, validate, and process user input. The student will be able to:	
23.01	Determine the results of code segments.	
23.02	Write programs that perform user input and output.	
23.03	Write programs that validate user input (e.g., range checking, data formats, valid/invalid characters).	
23.04	Write program modules such as functions, subroutines, or methods.	
23.05	Write program modules that accept arguments.	
23.06	Write program modules that return values.	
23.07	Write program modules that validate arguments and return error codes.	
23.08	Design and implement a simple simulation algorithm to analyze, represent and understand natural phenomena.	
23.09	Use APIs and libraries to facilitate programming solutions.	
23.10	Participate in a peer code review to verify program functionality, programming styles, program usability, and adherence to common programming standards.	
23.11	Explain how abstraction manages complexity.	
24.0	Effectively communicate and collaborate. The student will be able to:	
24.01	Evaluate modes of communication and collaboration.	
24.02	Select appropriate tools within a project environment to communicate with project team members.	
24.03	Utilize project collaboration tools (such as version control systems and integrated development environments) while working on a collaborative software project.	
24.04	Generate, evaluate, and prioritize questions that can be researched through digital resources and online tool.	
24.05	Perform advanced searches to locate information and/or design a data-collection approach to gather original data.	
24.06	Communicate and publish key ideas and details to a variety of audiences using digital tools and media-rich resources.	
25.0	Demonstrate responsible use of technology and information. The student will be able to:	
25.01	Implement an encryption, digital signature, or authentication method.	
25.02	Describe computer security vulnerabilities and methods of attack and evaluate their social and economic impact on computer systems and people (e.g., phishing, keylogging, virus, malware, intercepting data over public networks).	
25.03	Identify and explain the existence of biases in computer programming.	
25.04	Explain how computing can play a role in social and political issues.	
26.0	Differentiate among procedural, object-oriented, compiled, interpreted, and translated programming languages. The student will be able to:	
26.01	Differentiate between multiple levels of operating system, translation, and interpretation that support program execution.	
26.02	Explain the program execution process (by an interpreter and in CPU hardware).	
26.03	Describe object-oriented concepts.	
26.04	Explain the characteristics of procedural and object-oriented programming languages	
26.05	Compare and contrast programming languages that are compiled, interpreted, and translated.	

Florida Department of Education
Student Performance Standards

Course Title: 	Data Analytics and Database Design
Course Number: 	9007720
Course Credit:	1

Course Description:

The purpose of this course is to instruct students in the best practices for data visualization, dashboard design, and any relevant considerations thereof, such as data pre-processing, visual aesthetics, accurate and effective data representation. The content includes but is not limited to foundational knowledge and skills related to software methods and interactive plotting libraries. Moreover, the course covers foundational concepts in statistics, SQL querying, relational database design, as well as how to address concerns in data privacy and security. Instruction and learning activities are provided in a laboratory setting using hands-on experiences with the equipment, materials and technology appropriate to the course content and in accordance with current practices.

CTE Standards and Benchmarks	
27.0	Generate and tell stories with data. The student will be able to:	
27.01	Collect, organize, proofread, and arrange data into charts and graphs.	
27.02	Identify structural components of data representations (e.g., axes on a graph, table rows and columns, scale on a geographic map, key on a color map, etc.).	
27.03	Organize data and tell the data story presented in simple charts and graphs.	
27.04	Interpret and explain more complex charts that record more variables.	
27.05	Decode data: Identify and extract measurements, values or data points and what they represent from graphs, tables, or other data representations.	
28.0	Think critically about data. The student will be able to:	
28.01	Understand and explain how data changes over time (e.g., weather through seasons).	
28.02	Analyze information and derive conclusions based on the data presented.	
28.03	Show ability to interpret and make predictions based on data that changes over time.	
28.04	Interpret data representations accurately and describe meaningful patterns within the data.	
28.05	Identify potential bias and missing data; accurately connect position statements with datasets that support them.	
28.06	Separate factual information from inferences.	
28.07	Make quantitative and qualitative comparisons of data.	
28.08	Use appropriate mathematical/ statistical methods to develop and/or support claims that address specific scientific questions.	
29.0	Collect, analyze, and visualize a dataset to gain insight. The student will be able to:	
29.01	Identify different kinds of data and how they might be used in decision making. 	
29.02	Describe and construct a simple model of the data processing cycle (input-processing-output).	
29.03	Collect, organize, and manipulate different types of data with a data analysis and manipulation tool (e.g., Pandas).  	
29.04	Describe the information various online datasets provide and identify the types of questions they can and cannot answer.	
29.05	Use good practices for statistical sampling and testing, such as incorporation of uncertainties in estimation using margins of error or interval estimates to accurately analyze data.	
29.06	Apply statistics to find patterns in data.	
29.07	Use spreadsheet and Python functions to create tables and graphs to visually represent and communicate findings.	
29.08	Construct informed summaries, decisions, and predictions related to the data.	
29.09	Tell a data story using appropriate visualizations that are clear, compelling and accessible for the audience (e.g., infographics, charts, graphs, interactive dashboards, etc.).	
30.0	Know and apply best practices in data visualization. The student will be able to:	
30.01	Realize that the human brain combines visual cues and form immediate conclusions.	
30.02	Give examples of "simple" vs. "complex" charts and identify situations where they work best.	
30.03	Consider the audience and goals of the data visuals and make appropriate design decisions.	
30.04	Decide whether or how to shape the data before constructing the graphs.	
30.05	Determine which chart types to use and make appropriate design decisions.	
30.06	Apply and shape visual elements to convey desired messages (e.g., color, size, placement, etc.).	
30.07	Make sure that chart elements e.g. axes, labels, titles, are accurate and don't mislead the readers.  	
30.08	Define "chart junk" and know how to minimize it.  	
30.09	Realize that stories and narratives often make data conclusions more compelling.  	
30.10	Construct a logical presentation flow in presenting findings from data analysis. 	
30.11	Apply appropriate design choices for presentation slides and other elements. 	
30.12	Present data in a manner that is appropriate and compelling for the audience. 	
30.13	Experiment with visualizing complex concepts (e.g., confidence intervals.) 	
30.14	Experiment with visualizing complex multidimensional data. 	
31.0	Construct interactive dashboards following best practices. The student will be able to:	
31.01	Understand the strengths and inherent limitations of dashboards.	
31.02	Write Python code and import libraries for user interactivity.  	
31.03	Perform any data cleaning and processing before plotting data.  	
31.04	Conceptualize dashboard layouts and go through design iterations.  	
31.05	Decide on appropriate chart types to include.	
31.06	Apply appropriate "slice and dice" data filters for commonly-asked data questions.	
31.07	Observe and/or anticipate how the user might interact with dashboard elements.	
31.08	Place visual elements that properly convey relationships and hierarchies.  	
31.09	Effectively control visual noise in layout and interactivity.	
32.0	Understand and apply concepts in probability. The student will be able to:	
32.01	Define a sample space and enumerate the probabilities of possible outcomes. 	
32.02	Know what permutations are, give examples, and calculate them. 	
32.03	Know what combinations are, give examples, and calculate them.  	
32.04	Know what percentiles are, give examples, and calculate them. 	
32.05	Understand conditional probabilities as contingent multiple events. 	
32.06	Calculate probabilities for multiple events by constructing probability trees. 	
32.07	Understand dependent vs. independent events and give examples.  	
32.08	Know what Bayes Theorem is and apply to scenarios with conditional probabilities. 	
32.09	Understand discrete vs. continuous probabilities and give examples.  	
32.10	Calculate binomial probabilities and plot distributions thereof.	
33.0	Understand and apply concepts in basic statistics. The student will be able to:	
33.01	Calculate means, medians, modes, and explain scenarios where each is best used. 	
33.02	Calculate ranges, variances, and explain what they imply about the data.  	
33.03	Describe normal distributions and give examples. 	
33.04	Calculate Z-scores and explain their meaning.  	
33.05	Understand what the empirical rule is and apply to exploratory data analysis. 	
33.06	Identify examples of skewed distributions and understand their ramifications. 	
33.07	Locate any outliers in the data and understand their effect on summary statistics. 	
34.0	Understand and apply concepts in statistical sampling. The student will be able to:	
34.01	Define what sampling means and give examples. 	
34.02	Understand the importance of sample size. 	
34.03	Know what the Central Limit Theorem is and its implications. 	
34.04	Calculate the standard error for sample proportions and explain its implications. 	
34.05	Calculate the standard error for sample means and explain its implications. 	
34.06	Define what a confidence interval is and give examples. 	
34.07	Understand the limitation of confidence intervals in making conclusions about data.	
35.0	Understand and apply concepts in hypothesis testing. The student will be able to:	
35.01	Define what hypothesis testing is. 	
35.02	Identify situations where hypothesis testing is appropriate.  	
35.03	Define the null hypothesis and give examples. 	
35.04	Define the alternative hypothesis and give examples. 	
35.05	Know the difference between one-tailed vs. two-tailed tests and give examples. 	
35.06	Set up a significance test for proportions and draw appropriate conclusions.  	
35.07	Set up a significance test for means and draw appropriate conclusions.  	
35.08	Understand the difference between Type I and II errors and their implications. 	
36.0	Be aware of cognitive biases and the limitations of statistics in data analysis. The student will be able to:	
36.01	Identify and understand what the Anscomb's Quartet says about summary stats. 	
36.02	Recognize and understand how sampling bias can occur.  	
36.03	Define and give examples of cognitive biases (e.g., survivorship bias, confirmation bias, anchoring, availability, endowment effect, etc.). 	
37.0	Understand how data is accessed, sorted, and stored. The student will be able to:	
37.01	Explain the role that computers play in storing and accessing data. 	
37.02	Explain how computing devices represent and manipulate information.  	
37.03	Identify and understand the roles of databases in everyday life. 	
37.04	Describe databases and how they organize and transform data.  	
37.05	Understand datasets with multiple categories/ arranged in scaled graphs. 	
37.06	Collect, organize, manipulate, and transform data. 	
37.07	Understand and show ability to represent and manipulate data/ databases. 	
37.08	Create, modify, and manipulate databases.	
38.0	Become more SQL literate. The student will be able to	
38.01	Understand what a relational database is and when they are used.  	
38.02	Know what SQL is and when it is used. 	
38.03	Recognize there are different versions of SQL, their commonalities and differences. 	
38.04	Retrieve data with SELECT statements. 	
38.05	Filter query results with WHERE clauses.  	
38.06	Apply comparators (e.g., LIKE, IN, and wildcards with WHERE). 	
38.07	Sort query results with ORDER BY. 	
38.08	Use string functions on the data. 	
38.09	Change report headings with aliases. 	
38.10	Use date functions with WHERE. 	
38.11	Use GROUP BY with count. 	
38.12	Filter grouped results with HAVING. 	
38.13	De-duplicate with SELECT DISTINCT. 	
38.14	Merge rows with GROUP BY. 	
38.15	Understand how SQL indices and keys are used. 	
38.16	Understand different types of SQL JOIN. 	
38.17	Merge data across tables with JOIN. 	
38.18	Combine rows with SQL UNION. 	
38.19	Merge data across tables with IN. 	
38.20	Select from a selection with subqueries. 	
38.21	Write more efficient queries. 	
39.0	Understand design considerations and apply best practices in designing SQL databases. The student will be able to:	
39.01	Know what the ACID principles for transactions are. 	
39.02	Model and plan the structure of a database. 	
39.03	List and name data tables that go into a database. 	
39.04	Determine what data columns and data types each table contains. 	
39.05	Designate a primary key for each data table. 	
39.06	Identify any useful foreign keys among tables.   	
39.07	Characterize the relationships among tables (e.g., one-to-one, one-to-many, etc.). 	
39.08	Maintain referential integrity among tables. 	
39.09	Describe normalization and when/why it is helpful. 	
39.10	Define the first normal form and when it is applied.  	
39.11	Define the second normal form and when it is applied.  	
39.12	Define the third normal form and when it is applied.  	
39.13	Describe denormalization and when/when it is helpful. 	
39.14	Recognize issues with access control, compliance, and SQL injection.	
40.0	Identify data privacy/ data governance issues and methods to mitigate them. The student will be able to:	
40.01	Understand that identity protection is critical. 	
40.02	Name examples of personally identifiable information (PII).  	
40.03	Name examples of data that is not PII.  	
40.04	Cite examples of existing privacy laws and understand their scope. 	
40.05	Realize the combination of data points that alone would not uniquely identify someone can be used to precisely identify a person when used together. 	
40.06	Understand the various parts of a data processing ecosystem (e.g., databases, controllers, IT, governing bodies, etc.). 	
40.07	Recognize issues in data processing (e.g., database misconfigurations, lax security settings, inaccurate data, improper or excessive collection of information, demographical biases, etc.).  	
40.08	Perform a risk assessment by identifying risks in data processing and ways to mitigate them. 	
40.09	Understand the implications of a set of data privacy policies.  	
40.10	Define and understand the phases of the data life cycle (e.g., obtaining consent, collecting/ retaining/ processing data, disclosing/ transferring data, and archiving or destroying data).  	
40.11	Describe what data inventory is and why it is important.  	
40.12	Know the main steps in a data inventory process.	
41.0	Understand common cybersecurity issues and mitigation methods. The student will be able to:	
41.01	Identify and describe common types of cyber-attacks (e.g., malwares, phishing, denial of service, SQL injection, etc.). 	
41.02	Know ways to mitigate common types of cyber-attacks. 	
41.03	Know how common data breaches are and describe ways to safeguard PII data from such breaches.	

Florida Department of Education
Student Performance Standards

Course Title: 	Machine Learning and Applications
Course Number: 	9007730
Course Credit:	1

Course Description:

The purpose of this course is to provide students with core foundational knowledge to deepen understanding of machine learning (ML) practices and applications. This course builds understanding of the mathematical foundation needed to create algorithms for use in artificial intelligence and machine learning. The content includes but is not limited to foundational knowledge and skills related to computer coding and software development. Instruction and learning activities are provided in a laboratory setting using hands-on experiences with the equipment, materials and technology appropriate to the course content and in accordance with current practices.   

CTE Standards and Benchmarks	
42.0	Identify and define intelligent behavior. The student will be able to:	
42.01	Explain that "knowing" something means the ability to both represent and reason with it. 	
42.02	Explain the difference between knowledge and “knowing” (e.g., books can represent knowledge, but they do not "know" things because they cannot make use of that knowledge.) 	
42.03	Know and state conditions where behavior is intelligent using non-trivial sense-deliberate-act cycle.  	
42.04	Identify examples of intelligent and non-intelligent machine/agent behavior.  	
42.05	Explain common sense reasoning, emotional intelligence, deductive, and inductive reasoning and characteristics of quintessential human intelligence. 	
42.06	Describe the limitations of AI for natural interaction. 	
42.07	Identify the types of problems that are difficult for AI to solve (e.g., AI General Intelligence/ Strong AI, natural language understanding, computer vision) 	
42.08	Identify, explain, and debate the issues of AI and consciousness. 	
43.0	Articulate the relationship between AI, Machine Learning, and Computer Science. The student will be able to:	
43.01	Describe computer science as a discipline focused on the study of computers and algorithmic processes, including their principles, their hardware and software designs, their implementation, and their impact on society. 	
43.02	Describe AI as a branch of computer science that studies the science and engineering of making intelligent machines, especially intelligent computer programs that enable them to make predictions, decisions, plans, and solve problems.  	
43.03	Describe machine learning as a set of techniques that that enables a computer system to learn from data rather than through explicit programming. Machine learning techniques can be used to build models that make solve problems, make predictions, and make decisions. 	
43.04	Describe the differences between the field of AI and ML. 	
44.0	Identify and describe the types of representations and algorithms designed into AI-enabled technologies. The student will be able to:	
44.01	Identify examples of abstractions used in everyday life, describe the characteristics of abstractions, and explain how they are different than their real-world counterparts. 	
44.02	Describe the two major types of knowledge representations (i.e., symbolic and numerical representations), identify examples of each type, and explain when each is used in AI solutions. 	
44.03	Identify and describe classification, inference, approximation, optimization, recognition, and search families of reasoning algorithms and how they can solve problems. 	
44.04	Explain how and when data structures (e.g., trees, graphs, arrays, schemas) are used to represent data in AI (e.g., search trees, decision trees, maps, 1D Arrays -feature vectors. 2D-Arrays – tables, matrices). 	
44.05	Construct a representation of data, game, map, or real-world object/scenario/problem using a data structure (e.g., graph – map or game board, tree – classification and moves in a game, array – feature vector, schema – description of a concept), identify the parts of the representation and explain how to reason with them. 	
44.06	Evaluate and use the appropriate data structure for programming a specific machine learning algorithm. 	
44.07	Research the different kinds of symbolic representations used to encode information about the world (e.g., symbols, relationships, properties, etc.) in ways a computer can reason with to solve complex tasks (i.e. diagnosing a medical condition or having a dialog in a natural language). 	
45.0	Define and investigate examples of AI applications. The student will be able to:	
45.01	Identify and describe AI technologies students interact with frequently and determine what issues the AI is intended to address. 	
45.02	Discuss how AI is and could be used to enhance areas of student interest, real-world problems, business needs, and the future of work. 	
45.03	Understand the ways natural language processing can be used to create software to solve a wide range of problems related to written and spoken language. 	
45.04	Construct a chatbot and describe the factors that constrain the range of responses. 	
45.05	Name common computer vision tasks (e.g., image classification, object detection, semantic segmentation, image analysis, face detection, analysis, and recognition; optical character recognition (OCR)) and how they might be used to solve problems. 	
45.06	Experiment with software that recognizes emotions in facial expressions.	
46.0	Explain how domain knowledge is used in the design of AI systems. The student will be able to:	
46.01	Understand and describe how AI systems rely on domain knowledge from many fields including visual perception, linguistics, human behavior, psychology, anatomy and physiology, philosophy, and mathematics.  	
46.02	Explain how domain knowledge in AI systems can be provided by human experts or derived from statistics collected from millions of sentences, images, expert-labeled data, etc. 	
46.03	Explain and demonstrate how natural language understanding including speech recognition, speech generation, and speech translation is applied in speech recognition systems. 	
46.04	Explain and demonstrate how human language can be ambiguous and strategies AI uses to resolve ambiguity. 	
46.05	Explain and demonstrate how domain knowledge is used to classify, compare, or generate music. 	
46.06	Explain and demonstrate how domain knowledge is used to play board games and solve puzzles.  	
46.07	Explain and demonstrate how domain knowledge is used to recognize faces, gestures, and scenes. 	
46.08	Explain and demonstrate how domain knowledge is used to recognize affect from images (e.g., gestures, facial expressions). 	
46.09	Explain and demonstrate how domain knowledge is used to recognize sentiment from text or speech. 	
46.10	Identify and demonstrate the use of domain knowledge in the creation and use of medical diagnosis, finance, or manufacturing systems, building and construction, education, criminal justice, etc. 	
46.11	Understand that domain knowledge and best practices can be one-way – human bias can affect the decisions AI systems make, either in the collection and selection of examples to train systems or the interpretation of results of an AI system. 	
46.12	Describe the types of tasks where AI outperforms humans and those where humans outperform AI. 	
47.0	Describe machine learning algorithms in AI-enabled technologies. The student will be able to	
47.01	Define machine learning as a set of techniques (algorithms) that allow a computer to learn behaviors without explicit programming. 	
47.02	Explain that machine learning algorithms are learning "patterns" in data to construct internal representations that encode the relationship between inputs and outputs, which results in a model for reasoning.  	
47.03	Explain the difference between machine learning and human learning. 	
47.04	Identify supervised, unsupervised, reinforcement, and transfer learning types of machine learning, and provide examples of the types of problems they solve. 	
47.05	Illustrate and compare how supervised, unsupervised, and reinforcement learning algorithms adjust internal representations to learn for classification or prediction.  	
47.06	Identify CNN, RNN, GAN types of neural networks in image classification, speech recognition, and other applications.   	
47.07	Select the appropriate type of machine learning algorithm (supervised, unsupervised, or reinforcement learning) to solve a reasoning problem and explain why this algorithm is most appropriate for this type of problem.	
48.0	Explain the key technical challenges in design and responsible use of AI technologies. The student will be able to:	
48.01	Explain and demonstrate how training data influences learning. 	
48.02	Explain how developing an AI application involves humans making numerous technical and ethical decisions. 	
48.03	Explain the current issues with explainability of decisions made with neural networks and other deep learning architectures 	
48.04	Explore the challenges of using explainable AI (e.g., cost, ability to develop solutions when issues are discovered). 	
49.0	Describe different types of data and how they are used in AI. The student will be able to:	
49.01	Identify the different kinds of data we collect and share as Internet users. 	
49.02	Define numeric, text, date, graphics and sound types of data that computers use. 	
49.03	Distinguish that data requires context to be information. 	
49.04	Describe how computers store data using bits (binary digits). 	
49.05	Describe and construct a simple model of the data processing cycle (input-processing-output). 	
49.06	Describe how AI uses data to make predictions.	
50.0	Explain and use design thinking to solve a problem. The student will be able to:	
50.01	Distinguish between social and personal problems to be solved through a design. 	
50.02	Identify a challenge of social importance and personal interest. 	
50.03	Students develop problem statements embedded within a complex challenge.  	
50.04	Understanding challenges and user needs more deeply. 	
50.05	Pattern and sort user data to define design challenges. 	
50.06	Use a formal brainstorming process to generate ideas.  	
50.07	Test and iterate prototypes. 	
50.08	Refine prototypes based on user feedback. 	
50.09	Create stakeholder maps. 	
50.10	Develop point-of-view statements for users.  	
50.11	Develop a blueprint for a prototype. 	
50.12	Gather feedback from users. 	
50.13	Revise prototypes and present rationale for final design. 	
51.0	Apply the machine learning life cycle in the development and use of a machine learning model. The student will be able to:	
51.01	Identify and evaluate a problem appropriate for solving by machine learning.  	
51.02	Describe the data pipeline steps, including data collection, manipulation, cleansing, and transformation.  	
51.03	Understand how to make sure each step in the data pipeline is carried out ethically and responsibly. 	
51.04	Identify biases and ethical factors to consider when evaluating sources of data. 	
51.05	Locate data from reliable sources. 	
51.06	Find, convert, clean, and label the data features.  	
51.07	Evaluate, select, and test an appropriate model. 	
51.08	Select and justify the appropriateness of the learning algorithm for the particular problem and data.  	
51.09	For classification problems, define and distinguish between balanced and imbalanced datasets, and identify potential issues with imbalanced datasets. 	
51.10	Define and explain the differences among training, validation, and test datasets. 	
51.11	Explain and demonstrate the ways training data influences learning and decisions made by the model. 	
51.12	Experiment by adjusting hyper-parameters to tune the model. 	
51.13	Use a cross-validation set to determine when training should stop to avoid overfitting. 	
51.14	Use a test set to measure performance of the machine learning model. 	
51.15	Select appropriate metrics to evaluate success of the model: setting criteria, what to do when it fails in lab; fails in field. This can be done by an independent group for high-stakes applications.  	
51.16	Use appropriate tools to deploy and use the machine learning model. 	
51.17	Monitor model use over a span to time to evaluate its effectiveness on unseen data and make improvements.	
52.0	Recognize and identify mathematical principles upon which machine learning and AI are built such as calculus, linear algebra, probability, statistics, and optimization partial derivatives. The student will be able to:	
52.01	Explain how machine learning uses statistics to find patterns in data to make predictions about future values, identify the relationship between features, and make improvements.  	
52.02	Identify the type of mathematical functions machine learning models use (e.g., a simple linear equation, a high-degree polynomial, or a complex nonlinear equation found in deep neural networks). 	
52.03	Use a linear regression model and then adjust its parameters to fit a set of data points and use the model to predict a y value for any x value. 	
52.04	Model polynomial or logistic regression by using tools to manually adjust the parameters to reach what they perceive as a best fit to the data.  	
52.05	Recognize that neural networks are non-convex functions composed of many multi-variable functions.  	
52.06	Illustrate and explain the role of gradient-based algorithms for training neural networks and related issues.  	
52.07	Evaluate the quality of fit of a machine learning model using the mean squared error to determine if they have been over-fitted or under-fitted and explain the implications for future predictions or classifications made by the model. 	
52.08	Utilize visual reporting and statistical tools to perform, understand, and interpret statistics such as regression analysis, hypothesis testing, and sampling distributions. 	
53.0	Train and evaluate a range of ML models based on specific accuracy, inclusivity, and ethical design criteria. The student will be able to:	
53.01	Apply a supervised or unsupervised learning algorithm on real world data and evaluate the results.  	
53.02	Train a classification model and examine model performance on new inputs.  	
53.03	Train a regression model and examine model performance on new inputs.  	
53.04	Explain how a goodness of fit measure can be used to quantify the success of model prediction. 	
53.05	Evaluate the inclusivity and ethical design of the model. 	
53.06	Explore and utilize packages from a data analysis and manipulation tool when training a machine learning model. 	
53.07	Utilize visual reporting and statistical tools to perform, understand, and interpret statistics such as regression analysis, hypothesis testing, and sampling distributions.	
54.0	Use and evaluate supervised learning techniques to classify or predict outputs. The student will be able to:	
54.01	Research and present real-world problems and applications of supervised learning. 	
54.02	Describe how supervised learning algorithms find relationships between feature values and class labels in labeled data to create classification or prediction models. 	
54.03	Explain how supervised learning models use features to predict or label new data.  	
54.04	Describe how supervised learning algorithms adjust the parameters of a mathematical model (selected in advance by a human) to create models that make correct classifications or predictions. 	
54.05	Evaluate the results of a supervised learning model by measuring the percent of items in a test set that are labeled correctly. 	
54.06	Describe the types of algorithms that are used for classification (e.g., decision trees, logistic regression, and random forests). 	
54.07	Describe the types of algorithms that are used for regression (e.g., linear regression, XGBoost). 	
54.08	Evaluate the accuracy/ precision/ recall of a classification model. 	
54.09	Describe the benefits and limitations of supervised learning algorithms for solving problems.	
55.0	Use and evaluate unsupervised learning techniques to solve problems. The student will be able to:	
55.01	Research and present real-world problems and applications of unsupervised learning (e.g., anomaly detection in fraud and medical images, market basket analysis, recommender systems). 	
55.02	Describe how unsupervised learning algorithm finds patterns in unlabeled data by looking for data grouped into clusters.  	
55.03	Explain how unsupervised learning differs from supervised learning and how this difference allows unsupervised learning algorithms to solve more complex problems than supervised learning algorithms. 	
55.04	Distinguish between data that are appropriate for supervised versus unsupervised learning based on its structure, particularly the presence and roles of inputs and outputs. 	
55.05	Use clustering algorithms (e.g., K-means, hierarchical clustering, principal component analysis) to solve a problem.  	
55.06	Evaluate the results of an unsupervised learning model by examining the clusters to see if they capture useful distinctions in the dataset (e.g., If the clustering algorithm separates dissimilar observations apart and similar observations together, then it has performed well). 	
55.07	Describe the limitations of clustering algorithms (e.g., identification of clusters even if the data does not contain any clusters, unable to understand why elements are clustered together).	
56.0	Understand neural networks and their components. The student will be able to:	
56.01	Describe and illustrate the fundamental components of a neural network (e.g., input, hidden layers, output) and their purposes. 	
56.02	Explain the roles hyper-parameters, activation functions, learning rules, and transfer functions play in the development of a machine learning model. 	
56.03	Illustrate and explain how backpropagation works and how it improves prediction. 	
56.04	Illustrate and explain how stochastic gradient descent works. 	
56.05	Research and explain how deep neural networks work. 	
56.06	Illustrate and explain how feedforward multi-layer work. 	
56.07	Illustrate and explain how a perceptron work. 	
57.0	Use and evaluate different types of neural network architectures and their applications. The student will be able to:	
57.01	Illustrate the two main components of a Generative Adversarial Network’s (GAN) architecture (i.e., generator and discriminator models) and explain how each component works to create realistic images and audio. 	
57.02	Illustrate the architecture of a convolutional neural network (CNN) and explain the motivation for the model and how it is optimized for computer vision tasks (e.g., image processing, natural language processing, and recommendation systems). 	
57.03	Illustrate the architecture of a recurrent neural network (RNN) and explain how it is optimized for time-series forecasting (e.g., weather prediction, stock predictions, etc.). 	
57.04	Describe the purpose of ensemble learning methods and how they are used to improve the performance of machine learning models and identify examples in the real-world. 	
57.05	Describe the purpose of transfer learning methods and how it is used to decrease the amount of training time for new tasks and identify examples in the real-world. 	
58.0	Use and evaluate reinforcement learning techniques to solve problems. The student will be able to:	
58.01	Research and present real-world problems and applications of reinforcement learning (e.g. self-driving cars, walking, learning a new skill, video games, question answering, machine translation, medical diagnosis, sequential decision making problems). 	
58.02	Explain that reinforcement learning models focus on how machines can learn to act in a particular way (e.g., robots learning to walk, or chatbots learning to better answer customer problems). 	
58.03	Describe how reinforcement learning generally works: the learning algorithm uses trial and error to find a policy for choosing actions that maximizes the reinforcement signal. 	
58.04	Model the ways that reinforcement learners update value predictions or policies (e.g., internal representations). 	
58.05	Use reinforcement algorithms to solve a problem (e.g., Deep Adversarial Networks, Q-learning).  	
58.06	Describe the benefits and limitations of reinforcement learning.	
59.0	Research and explain the advancements in computing hardware that make AI possible. The student will be able to:	
59.01	Explain the original purpose of Graphical Processing Units (GPUs) and their role in advancing the field of deep learning. 	
59.02	Explain the purpose and function of GPUs in decreasing the training time of machine learning and identify applications that use this approach in the real world. 	
59.03	Explain the purpose and function of Tensor Processing Unit (TPU) as an AI accelerator application-specific integrated circuit (ASIC) for neural network machine learning and identify applications that use this approach in the real world.  	
59.04	Explain why CPUs are not sufficient for training many machine learning models.  	
59.05	Compare and contrast the pros and cons of using GPUs, TPUs, and FPGAs in data processing and performance improvements of AI-based applications. 	
60.0	Understand and articulate how AI can impact society in both positive and negative ways. The student will be able to:	
60.01	Explain the ways that AI impacts different communities and people in different ways. 	
60.02	Research proxies in data for race, ethnicity, socio-economic status, gender, or context that can lead to disparate accuracy and impacts on different people groups.  	
60.03	Demonstrate the use of AI is an economic driver that makes new services possible and businesses more efficient. 	
60.04	Describe the ways AI technologies are changing business, healthcare, education, government, etc. 	
60.05	Explain and provide examples of the ways AI and automation will change the way people work, create new jobs, and eliminate some jobs. 	
60.06	Identify, research, and analyze current events in the field of AI, considering new technology developments, social and ethical impact. 	
60.07	Identify and describe current challenges and opportunities in AI technologies using non-machine learning aspects of AI such as genetic algorithms, robotics, and computer vision. 	
60.08	Make predictions about the future trends or developments in the field of AI based on current AI applications. 	
60.09	Research the purpose of organizations that consider how AI can be used for social and ethical good and describe their role in AI development. 	
60.10	Identify and describe ethical and societal issues for AI applications in a variety of settings such as public safety, finance, social media marketing, and government, in different cultures and countries. 	
60.11	Analyze participation in collective online activities considering the possibilities over problems, opportunities over risks, and community successes over personal gain.   	
60.12	Explain the ethical use of technology and digital content with a specific understanding of ownership, licensing, and fair use.  	
60.13	Understand consequences of inappropriate technology use.	
61.0	Understand the best practices and key characteristics of bias, fairness, transparency, explainability, accountability of ethically designed AI systems and decision-making practices. The student will be able to:	
61.01	Define bias, perception, privacy, and accuracy in the context of AI. 	
61.02	Describe and critique how ethics and philosophy explicitly and implicitly play a role in AI applications. 	
61.03	Explain the key principles of responsible and ethical AI design that result in AI systems that keep fairness, transparency, explainability, human-centeredness, privacy and security interests of users in mind. 	
61.04	Identify the sources and types of bias in AI systems and models. 	
61.05	Explain the key approaches to minimizing various types of bias. 	
61.06	Describe group and individual types of fairness and various views of fairness that affect the metrics for evaluating fairness in systems. 	
61.07	Describe the relevant algorithms, data, goals, compliance, influence, and usage principles of transparency in AI and explain the regulatory, privacy, and security considerations that are important when making decisions with AI systems. 	
61.08	Explain the challenges of implementing transparency in AI for companies such as vulnerabilities and security, privacy, intellectual property, legal and risk management. 	
61.09	Examine the role and importance of taking a human-in-the-loop approach to the training of machine learning models and deployment and use of AI-driven automation of systems.  	
61.10	Identify the critical points of machine learning model and AI system design (e.g., goals, purposes, and risks) that are not easily identified by automation itself and require human-in-the-loop decision making. 	
61.11	Provide examples of how explainability vs. interpretability is the most effective means of ensuring AI solutions are transparent, accountable, responsible, fair, and ethical across use cases and industries.  	
61.12	Provide examples or explain how a company, developer, or decision maker can establish accountability in the development and use of AI to make decisions (e.g., codes of ethics/ responsible design, ethical design considerations, and decision tracking).  	
61.13	Understand the subjective nature of values across cultures; describe the key principles and challenges of designing AI to align with the norms and values of the user group in mind. 	
61.14	Describe the expertise and processes needed to ethically design AI to support users (e.g., multi-disciplinary team including ethicists, sociologists, developers, legal/compliance, tracking, etc.). 	
61.15	Determine whether characteristics of dataset (e.g., incomplete, imbalanced, and/or inappropriately selected) are contributing bias to a machine learning model. 	
61.16	Evaluate machine learning models for characteristics of measurement bias as a result from faulty measurements (e.g., sensors such as camera color filters, other data collection instruments, and data labeling). 	
61.17	Explain the need for models to be regularly evaluated and updated to best represent the current world. 	
62.0	Identify different kinds of data, their sources, and how they might be used in decision making. The student will be able to:	
62.01	Explain why data is the fuel of machine learning and the importance of quality data for gaining insights.  	
62.02	Identify and describe sources of company data such as basic information, economic information, technology information (e.g., website traffic and search trends), and reviews-based information (e.g., business ratings and reviews). 	
62.03	Identify and describe sources of people data such as Internet behavior: web presence score, social networks and social mentions, search engine results, proxy usage, interests, and purchasing behavior. 	
62.04	Identify and describe sources of geospatial data such as location-specific alternative data such as demographic information, property information, purchasing behavior, and other business information. 	
62.05	Identify and describe sources of time-based data such as real-time information surrounding events, politics, internet trends behavior (e.g., trending keywords and subjects, foot traffic, web traffic), and financial trends (e.g., company share price, GDP, debt history, and unemployment trends). 	
62.06	Explain how different kinds of data can be used in decision-making and the form of data (e.g., text, numerical, time series, or image) that may be represented in.	
63.0	Critique data and data-based claims to avoid being misled by data through identifying bias, confounding, and random error. The student will be able to	
63.01	Provide examples of the social and personal consequences of predictions derived from models built on data.  	
63.02	Distinguish between credible and unreliable information sources.   	
63.03	Recognize and describe signs of compromised information or data.  	
63.04	Identify and describe errors in decisions and predictions due to faulty use of data. 	
63.05	Describe issues of privacy and security with respect to data collection, storage, analysis, and insights. 	
63.06	Discuss how and when data can support making decisions. 	
64.0	Describe the limitations of machine learning and the decisions that can be made with data. The student will be able to: 	
64.01	Describe the principle of garbage in/garbage out in relationship to data for training and evaluating the machine learning model, as well as its accuracy and impact.  	
64.02	Demonstrate understanding of the inverse relationship between model complexity and transparency and the impact this has on decision-making. 	
64.03	Research and evaluate the limitations of machine learning and AI and the impact this has on decision-making. 	
64.04	Research the security vulnerabilities of a machine learning model and ways to mitigate them.  	
64.05	Research the potential failure modes of AI and evaluate how, when, and why things can go wrong. 	
64.06	Research and evaluate the sensitivity of machine learning models to understand how and why it is likely to break.	
65.0	Explore the characteristics, tasks, work attributes, options, and tools associated with AI-enabled careers. The student will be able to:	
65.01	Explore a variety of careers that leverage AI tools and systems.  	
65.02	Discuss the impact of AI on business and commerce.  	
65.03	Evaluate the impacts of irresponsible use of AI technologies.  	
65.04	Identify tasks performed by AI-enable professionals. 	
65.05	Identify and explain the ways businesses use AI to solve business problems. 	
65.06	Investigate AI-enabled career opportunities in various businesses, medicine, engineering, construction, science, social services, government, military, computing, education, manufacturing, finance/banking, etc. 	
65.07	Explain different specializations and the related training in AI-enabled careers. 	
65.08	Explain the need for continuing education and training of AI-enabled professionals. 	
65.09	Explain how AI is used in enterprise software systems and how insights from AI impact decision-making. 	
65.10	Describe ethical responsibilities of AI-enabled professionals. 	
65.11	Identify credentials and certifications that may improve employability for an AI-enabled professional. 	
65.12	Identify devices, datasets, tools, and other environments for which AI-enabled professionals may use, modify, and develop AI systems.  	
65.13	Learn that people in various work roles engage in continuous learning to upgrade skills and adapt to change. 	
66.0	Identify how leadership development, school and community service projects and competitive events are integral parts of career and technology education. The student will be able to:	
66.01	Explain the goals, mission, and objectives of the career-technical student organization (CTSO). 	
66.02	Explore opportunities a student organization can leverage to bring business and education together in a positive working relationship through innovative leadership and career development programs. 	
66.03	Explore the local, state, and national opportunities available to students through participation in conferences, competitions, community service, philanthropy, and other CTSO activities. 	
66.04	Explain how participation in career and technology education student organizations can promote lifelong responsibility for community service and professional development. 	
66.05	Explore competitive events related to the content of this course and the required competencies, skills, and knowledge for each related event for individual, team, and chapter competitions. 	
66.06	Understand that learning can occur in both formal and informal environments outside of school. 	
66.07	Express a positive attitude towards lifelong learning and how it relates to various careers. 	

Florida Department of Education
Student Performance Standards

Course Title: 	Capstone Project with Industry Partners
Course Number: 	9007740
Course Credit:	1

Course Description:

The purpose of this course is to provide students with opportunities to apply their knowledge in data analysis, database design, and machine learning to internship projects with collaborating industry partners. Instruction and learning activities are provided in a laboratory setting using hands-on experiences with the equipment, materials and technology appropriate to the course content and in accordance with current practices.  

CTE Standards and Benchmarks	
67.0	Explain and use design thinking to solve a problem. The student will be able to:	
67.01	Distinguish between social and personal problems to be solved through a design. 	
67.02	Develop problem statements embedded within a complex challenge.  	
67.03	Create stakeholder maps. 	
67.04	Understand challenges and user needs more deeply. 	
67.05	Develop point-of-view statements for users.  	
67.06	Pattern and sort user data to better address design challenges. 	
67.07	Use a formal brainstorming process to generate ideas.  	
67.08	Develop a blueprint for a prototype. 	
67.09	Test and iterate prototypes. 	
67.10	Gather feedback from users. 	
67.11	Refine prototypes based on user feedback. 	
67.12	Revise prototypes and present rationale for final design. 	
68.0	Design AI solutions using embedded computing (as applicable to specific projects). The student will be able to:	
68.01	Identify and define the function of circuits, sensors, microcontrollers, motors, and other components used in embedded systems. 	
68.02	Assemble an embedded or robotic system that use circuits, sensor(s), microcontroller, microcomputers, motor(s) to complete a specific task. 	
68.03	Write a program for an embedded or robotic system that makes a decision based on sensor/user input, controls mechanics of the robot, and completes a “human” task (e.g., delivers items, opens a door for someone, solves a puzzle, etc.). 	
68.04	Use a problem-solving method to debug hardware issues	
69.0	Explore the characteristics, tasks, work attributes, options, and tools associated with AI-enabled careers and educational pathways to achieve these career goals. The student will be able to:	
69.01	Explore a variety of careers that leverage AI tools and systems.  	
69.02	Discuss the impact of AI on business and commerce.  	
69.03	Evaluate the impacts of irresponsible use of AI technologies.  	
69.04	Identify tasks performed by AI-enable professionals. 	
69.05	Identify and explain the ways businesses use AI to solve business problems. 	
69.06	Investigate AI-enabled career opportunities in various businesses, medicine, engineering, construction, science, social services, government, military, computing, education, manufacturing, finance/banking, etc. 	
69.07	Explain different specializations and the related training in AI-enabled careers. 	
69.08	Explain the need for continuing education and training of AI-enabled professionals. 	
69.09	Explain how AI is used in enterprise software systems and how insights from AI impact decision-making. 	
69.10	Describe ethical responsibilities of AI-enabled professionals. 	
69.11	Identify credentials and certifications that may improve employability for an AI-enabled professional. 	
69.12	Identify devices, datasets, tools, and other environments for which AI-enabled professionals may use, modify, and develop AI systems.  	
69.13	Learn that people in various work roles engage in continuous learning to upgrade skills and adapt to change.	
69.14	Explore a variety of careers that leverage AI tools and systems.  	
69.15	Discuss the impact of AI on business and commerce.  	
69.16	Evaluate the impacts of irresponsible use of AI technologies.  	
69.17	Identify tasks performed by AI-enable professionals. 	
70.0	Use appropriate tools to design an AI System to solve problems. The student will be able to	
70.01	Select a dataset that is appropriate for a given AI application.  	
70.02	Curate and clean a dataset for use. 	
70.03	Employ one or more technological tools in machine learning such as natural language processing, ensemble learning, signal processing, computer vision, optical character recognition, etc. to expedite workflow.  	
70.04	Apply an ethical development process to create, analyze and iterate an AI-enabled solution, individually and collaboratively. 	
70.05	Be cognizant of the range of tools and services available for AI-enabled professionals and AI experts/developers/engineers. 	
70.06	Determine the computing resources (e.g., desktop vs GPU) and time needed (e.g., minutes, hours, days, weeks) to complete a ML/AI task.  	
70.07	Identify and research networks and cloud services that use AI solutions (e.g., machine learning, data management, edge AI, and various industry-specific solutions and services). 	
70.08	Identify AI in a variety of industry solutions and services and make appropriate recommendations of AI applications based on an industry need.	
71.0	Set up and use a machine learning (ML) pipeline to solve a problem. The student will be able to:	
71.01	Identify a problem that can be solved with machine learning. 	
71.02	Use appropriate techniques and best practices for selection, collection, and/or curation of data to gain insight on a problem. 	
71.03	Use appropriate techniques and best practices for selection of algorithms and architectures to develop and train a model to solve a problem. 	
71.04	Use appropriate tools to evaluate model performance. 	
71.05	Use appropriate tools and techniques to deploy the model. 	
71.06	Evaluate the important issues related to privacy and security in the development, deployment, and usage of ML models. 	
71.07	Demonstrate awareness of the issues that arise in model use (e.g., model drift) and best practices for maintaining the accuracy of deployed models. 	
71.08	Demonstrate awareness that training a machine learning model is an iterative process that requires time and compute resources.	
72.0	Appropriately use automated services to accomplish common tasks. The student will be able to:	
72.01	Train an ML model and deploy the model as a service. 	
72.02	Select and use appropriate techniques for setting up a workspace, create computing resource, and exploring data. 	
72.03	Create and run a training pipeline, evaluate a regression model, create an inference pipeline, and deploy a predictive service.  	
72.04	Select and use appropriate services to  
•	Create a regression model that predicts numeric values. 
•	Create a clustering model that groups similar entities based on their features. 
•	Create a classification model that predict categories or classes. 
•	Detect and analyze faces. 
•	Detect, analyze, and classify images. 
•	Read and analyze text. 
•	Recognize, synthesize, and/or translate speech. 
•	Generate a language model. 
•	Build a Q&A chatbot.	
73.0	Use data analysis and visualization tools to work with datasets and gain insights. The student will be able to:	
73.01	Use good practices for statistical sampling, testing, and incorporation of uncertainties in estimation using margins of error or interval estimates to accurately analyze data. 	
73.02	Apply statistics to find patterns in data. 	
73.03	Apply data analysis techniques to discover new information and guide decision-making. 	
73.04	Explore and utilize packages for data analysis and manipulation before training a machine learning model (e.g., Pandas). 	
73.05	Apply techniques for exploration and analysis of single, pair, and multi-feature models. 	
73.06	Construct an ML model based on a dataset that trains the algorithm on how to make decisions. 	
73.07	Tell a data story using appropriate visualizations that are clear, compelling and accessible for the audience (e.g., infographics, charts, graphs, interactive dashboards, etc.). 	
74.0	Apply the machine learning life cycle in the development and use of a machine learning model. The student will be able to:	
74.01	Identify and evaluate a problem appropriate for solving by machine learning.  	
74.02	Describe the data pipeline steps, including data collection, manipulation, cleansing, and transformation.  	
74.03	Understand how to make sure each step in the data pipeline is carried out ethically and responsibly. 	
74.04	Identify biases and ethical factors to consider when evaluating sources of data. 	
74.05	Locate data from reliable sources. 	
74.06	Find, convert, clean, and label the data features.  	
74.07	Evaluate, select, and test an appropriate model. 	
74.08	Select and justify the appropriateness of the learning algorithm for the particular problem and data.  	
74.09	For classification problems, define and distinguish between balanced and imbalanced datasets, and identify potential issues with imbalanced datasets. 	
74.10	Define and explain the differences among training, validation, and test datasets. 	
74.11	Explain and demonstrate the ways training data influences learning and decisions made by the model. 	
74.12	Experiment by adjusting hyper-parameters to tune the model. 	
74.13	Use a cross-validation set to determine when training should stop to avoid overfitting. 	
74.14	Use a test set to measure performance of the machine learning model. 	
74.15	Select appropriate metrics to evaluate success of the model: setting criteria, what to do when it fails in lab; fails in field. This can be done by an independent group for high-stakes applications.  	
74.16	Use appropriate tools to deploy and use the machine learning model. 	
74.17	Monitor model use over a span to time to evaluate its effectiveness on unseen data and make improvements.	
75.0	Design and develop AI systems to solve a problem or design solutions for social and ethical issues. The student will be able to:	
75.01	Define and research a real social or ethical problem in the community that could be enhanced or solved with AI.  	
75.02	Use a problem-solving process (e.g., design thinking) to collaboratively investigate the identified problem and define requirements. 	
75.03	Analyze the requirements and translate the design vision to build complete end-to-end AI solutions. 	
75.04	Define and apply a team-based software development process to collaboratively design a solution that uses AI for the problem. 	
75.05	Identify and use IDEs, APIs, and packages in program development to build and train machine learning models. 	
76.0	Create a portfolio of AI projects that demonstrate ability to program machine learning models using a wide range of AI algorithms. The student will be able to:	
76.01	Identify common vendors of cognitive services, machine learning, and knowledge mining available to architect and implement AI systems involving natural language processing, speech, computer vision, and conversational AI.  	
76.02	Analyze requirements for AI solutions, recommending the appropriate tools and technologies to solve a problem. 	
76.03	As applicable, explain the architecture, development process, and build: 
•	A chatbot. 
•	A recommender system. 
•	An application of natural language processing. 
•	An application of computer vision. 	
76.04	As applicable, apply or construct: 
•	Regression modeling to make a time-series prediction model. 
•	Naïve Bayes, K-nearest neighbor, or support vector machines, etc. to develop a classification model. 
•	Random forest and/or the accompanying boosting algorithms, e.g., XGBoost and AdaBoost in ensemble learning. 
•	Isolation forests, PCA, or K-means clustering to develop an anomaly detection model. 	
76.05	Regression modeling to make a time-series prediction model. 	
76.06	Naïve Bayes, K-nearest neighbor, or support vector machines, etc. to develop a classification model. 	
76.07	Random forest and/or the accompanying boosting algorithms, e.g., XGBoost and AdaBoost in ensemble learning. 	
76.08	Isolation forests, PCA, or K-means clustering to develop an anomaly detection model. 	
77.0	Research and evaluate various AI careers involved in AI system usage, design, development, deployment, and maintenance. The student will be able to:	
77.01	Explain the roles, responsibilities, tools, skills, expertise, and education needed for AI researchers to develop new machine learning algorithms, architectures, applications, and approaches to solve challenging problems.  	
77.02	Explain the roles, responsibilities, tools, skills, expertise, and education needed for AI or machine learning engineers to architect and implement AI algorithms, models, and systems.  	
77.03	Explain the roles, responsibilities, tools, skills, expertise, and education needed for AI solutions architect to design AI systems that meet the business needs for customers. 	
77.04	Explain the tools, skills, expertise, and education needed for data scientists to train machine learning models that can make predictions and inferences based on the relationships they find in the data.  	
77.05	Explain the roles, responsibilities, tools, skills, expertise, and education needed for data engineers to design and implement the management, monitoring, security, and privacy of data solutions. 	
77.06	Explain the roles, responsibilities, tools, skills, expertise, and education needed for database administrators to implement and manage cloud and on premises databases that work with AI-base data services. 	

Additional Information

Laboratory Activities

Laboratory investigations that include scientific inquiry, research, measurement, problem solving, emerging technologies, tools and equipment, as well as, experimental, quality, and safety procedures are an integral part of this career and technical program/course. Laboratory investigations benefit all students by developing an understanding of the complexity and ambiguity of empirical work, as well as the skills required to manage, operate, calibrate and troubleshoot equipment/tools used to make observations. Students understand measurement error; and have the skills to aggregate, interpret, and present the resulting data. Equipment and supplies should be provided to enhance hands-on experiences for students.

Florida Standards for English Language Development (ELD)
English language learners communicate for social and instructional purposes within the school setting. ELD.K12.ELL.SI.1

English Language Development (ELD) Standards Special Notes:
Teachers are required to provide listening, speaking, reading and writing instruction that allows English language learners (ELL) to communicate for social and instructional purposes within the school setting. For the given level of English language proficiency and with visual, graphic, or interactive support, students will interact with grade level words, expressions, sentences and discourse to process or produce language necessary for academic success. The ELD standard should specify a relevant content area concept or topic of study chosen by curriculum developers and teachers which maximizes an ELL’s need for communication and social skills. For additional information on the development and implementation of the ELD standards, please contact the Bureau of Student Achievement through Language Acquisition at sala@fldoe.org. 

Career and Technical Student Organization (CTSO)

Florida Technology Student Association (FL-TSA) and SkillsUSA are the co-curricular career and technical student organization(s) providing leadership training and reinforcing specific career and technical skills. Career and Technical Student Organizations provide activities for students as an integral part of the instruction offered. 

Cooperative Training – OJT

On-the-job training is appropriate but not required for this program. Whenever offered, the rules, guidelines, and requirements specified in the OJT framework apply. 

Accommodations

Federal and state legislation requires the provision of accommodations for students with disabilities as identified on the secondary student’s Individual Educational Plan (IEP) or 504 plan or postsecondary student’s accommodations’ plan to meet individual needs and ensure equal access. Accommodations change the way the student is instructed. Students with disabilities may need accommodations in such areas as instructional methods and materials, assignments and assessments, time demands and schedules, learning environment, assistive technology and special communication systems. Documentation of the accommodations requested and provided should be maintained in a confidential file.

In addition to accommodations, some secondary students with disabilities (students with an IEP served in Exceptional Student Education (ESE)) will need modifications to meet their needs. Modifications change the outcomes or what the student is expected to learn, e.g., modifying the curriculum of a secondary career and technical education course. Note: postsecondary curriculum and regulated secondary programs cannot be modified.

Some secondary students with disabilities (ESE) may need additional time (i.e., longer than the regular school year), to master the student performance standards associated with a regular course or a modified course.  If needed, a student may enroll in the same career and technical course more than once.  Documentation should be included in the IEP that clearly indicates that it is anticipated that the student may need an additional year to complete a Career and Technical Education (CTE) course.  The student should work on different competencies and new applications of competencies each year toward completion of the CTE course.  After achieving the competencies identified for the year, the student earns credit for the course.  It is important to ensure that credits earned by students are reported accurately.  The district’s information system must be designed to accept multiple credits for the same course number for eligible students with disabilities.