Florida Department of Education
Curriculum Framework 

Program Title:	Fundamentals of Data Science 
Program Type:	Orientation/Exploratory 
Career Cluster:	Information Technology

Secondary – Middle School	
Program Number	9003100	
CIP Number	0530700102	
Grade Level	6-8	
Standard Length	Year	
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 course that provides coherent and rigorous content aligned with challenging academic standards and relevant technical knowledge and skills needed to prepare for further education and careers in Data Analytics and Data Science-enabled careers; 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 required for such data professionals working in business and academic environments. 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 fundamental understanding and application of data science and analytics, including data gathering from the web, thinking critically about data, data visualization, working with spreadsheets and databases, societal impacts and ethical concerns in data science, as well as building models and systems that leverage data to make decisions. In addition, the course content includes but is not limited to practical experiences in problem identification; creation, selection, and curation of data sets; evaluation of the societal impact of data science applications, employing ethical and responsible development methodologies and decision making, essential data processing techniques, and implementation issues. 

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

Program Structure

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 course structure:
Course Number	Course Title	Teacher Certification	Length	
pending
	Exploring and Applying Data Science	BUS ED 1@2   
INFO TECH 7G  
WEB DEV 7G   
ENG 7G 
EG TEC 7G 
TECH ED 1 @2 
ENG & TEC ED 1@2 
MATH 6-12 
Science 6-12
Any Computer Science	Year	

Common Career Technical Core – Career Ready Practices

Career Ready Practices describes 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 course, the student will be able to perform the following:

Exploring Data Science
01.0	Demonstrate proficiency using the Internet to locate information.  
02.0	Demonstrate proficiency locating, gathering, and preparing textual, graphical, and image-based web content. 
03.0	Demonstrate an understanding of Internet safety and ethics. 
04.0	Demonstrate an understanding of computer networks. 
05.0	Describe different kinds of data, their sources, and how they might be used in decision making.  
06.0	Think critically about data. 
07.0	Understand how data is accessed, sorted, and stored. 
08.0	Develop and apply fundamental spreadsheet skills.
09.0	Develop and apply database knowledge and skills. 

Applying Data Science
10.0	Explain and use design thinking to solve a problem.
11.0	Understand human and organizational behavior in the context of the data science process. 
12.0	Demonstrate knowledge of Data Governance (safety and privacy) practices for data use. 
13.0	Identify and apply different data collection methods and understand the importance of data quality. 
14.0	Generate and tell stories with data.
15.0	Know and apply best practices in data visualization. 
16.0	Be aware of the limitations of statistics and cognitive biases in data analysis. 
17.0	Critique data and data-based claims to avoid being misled by data. 

	Florida Department of Education
	Student Performance Standards

Course Title: 	Exploring and Applying Data Science
Course Number: 	9003110
Course Credit:	Year

Course Description: 

This course is to instruct students in the best practices in data gathering and processing for data science and analytics. The content includes but is not limited to information searching and retrieval on the Internet, critical understanding of how data is used in society, plus foundational knowledge and skills related to spreadsheets and databases. Moreover, the course covers ethical 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. The course provides students with core foundational knowledge of working with data through practices and applications. This course builds an understanding of each stage in the data project pipeline from both the technical and the human behavioral perspectives. The content includes but is not limited to foundational knowledge and skills related to design thinking, data collection, data governance, storytelling with data visualization, and critical data analysis. 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	Demonstrate proficiency using the Internet to locate information. The student will be able to:	
01.01	Identify and use web terminology (WWW, web browser, Internet, web server, web page, address bar, hyperlinks, navigation buttons, search bar, bookmarks/favorites, tab, downloading, plug-ins, social media plug-ins). 	
01.02	Define Universal Resource Locators (URLs) and associated protocols (e.g., HTTP, FTP, telnet, mailto). 	
01.03	Compare and contrast the types of Internet domains (e.g., .com, .org, .edu, .gov, .net, .mil). 	
01.04	Demonstrate proficiency using search engines, including Boolean search techniques. 	
01.05	Demonstrate proficiency using various web tools (e.g., downloading of files, transfer of files, telnet, PDF). 	
01.06	Compare and contrast the roles of web servers and web browsers. 	
01.07	Evaluate online information for relevance, credibility and quality using basic guidelines and indicators (e.g., authority, affiliation, purpose, bias, date). 	
01.08	Identify and apply copyright and fair use guidelines and explain plagiarism as an ethical and legal violation. 	
01.09	Incorporate results from Internet searches into a research project (e.g., report, summary). 	
01.10	Download images as needed to support a research project, complying with copyright notices.   	
01.11	Properly cite Internet sources used to obtain information for a research project. 	
01.12	Understand how AI-based applications work, and how they can improve web searches or respond to user requests, e.g., ChatGPT, Bard, Claude, etc. 	
02.0	Demonstrate proficiency locating, gathering, and preparing textual, graphical, and image-based web content. The student will be able to:	
02.01	Define the elements of a webpage and what makes a good webpage.	
02.02	Describe effective text and image content for webpages based on how visitors use the web.   	
02.03	List guidelines and conventions for effective text on webpage.  	
02.04	Explain the inverted pyramid model of newspaper journalism and how it applies to web content.    	
02.05	Use word-processing software to create effective written content for a webpage.  	
02.06	Create and/or edit message-driven image content for a webpage using graphics software.  	
02.07	Access graphics through various resources (e.g., scanner, digital camera, CD-ROM, clipart, copyright-free online graphics).  	
02.08	Plan the content and design of a basic webpage using strategies for effective web communication, including brainstorming, determining audience, choosing content and media types, using white space.	
02.09	Explore prompt engineering with AI-based image generators, e.g., Dall-E 2, Midjourney, etc., see how prompt changes result in different output images.	
03.0	Demonstrate an understanding of Internet safety and ethics. The student will be able to:	
03.01	Describe risks associated with social networking sites (e.g., Facebook, Snapchat, Instagram, Twitter) and ways to reduce these risks. 	
03.02	Define “privacy” and relate it to the term “digital footprint”.	
03.03	Practice cyber-safety techniques to protect your personal information when using Internet searches, email, chat rooms, and social network websites. 	
03.04	Describe cyberbullying, its impact on perpetrators and victims and ways to respond. 	
03.05	Describe risks associated with sexting (including legal issues, social consequences), and discuss methods for response, reporting, and prevention.	
03.06	Describe risks associated with online gaming and identify ways to reduce these risks. 	
03.07	Discuss issues related to downloading music or videos from the Internet, including unethical vs. illegal actions.	
03.08	Compare and contrast rules for copyright and fair use, especially in relation to using online resources for school and educational purposes. 	
03.09	Distinguish between viruses and malware and discuss their impact on personal privacy and computer operation.   	
03.10	Describe common threats used to spread malware and viruses, including phishing, pharming, Trojans, spyware, malicious sites, “free” downloads. 	
03.11	Perform an antivirus scan on a computer system to check for viruses and malware. 	
03.12	Describe strong password practices.    	
03.13	Practice cyber safety techniques to protect your computer system when using Internet searches, email and social network websites.    	
03.14	Identify security issues related to mobile phones, including personal information compromised if a phone is lost or stolen.   	
03.15	Adhere to Acceptable Use Policies when accessing the Internet. 	
04.0	Demonstrate an understanding of computer networks. The student will be able to:	
04.01	Define “network” and give examples of networks used at home, school, and work.    	
04.02	Compare types of networks, including LAN, WAN, MAN, VPN, intranet, extranet, the Internet.	
04.03	Compare common network topologies, including bus, star, ring, mesh.   	
04.04	Compare various network models and their advantages, including client/server, mainframe/terminal, peer-to-peer. 	
04.05	Compare various methods and media for network connections, including broadband, wireless, Bluetooth, cellular, satellite. 	
04.06	Describe the functions of various network hardware devices, including NIC, hub, switch, router, bridge, gateway, access point.  	
04.07	Describe protocols' purpose and identify protocols commonly used in networks, including: TCP/IP, DHCP, DNS, HTTP, FTP, IMAP, POP, SMTP. 	
04.08	Describe the purpose and function of IP addressing and distinguish between public and private IP addresses. 	
04.09	Describe the OSI reference model and its layers, including tracing the flow of data between two network nodes through the OSI layers.   	
05.0	Describe different kinds of data, their sources, and how they might be used in decision making. The student will be able to:	
05.01	Identify the different kinds of data we collect and share as Internet users. 	
05.02	Define numeric, text, date, graphics and sound types of data that computers use. 	
05.03	Distinguish that data requires context to be information. 	
05.04	Describe how computers store data using bits (binary digits). 	
05.05	Describe and construct a simple model of the data processing cycle (input-processing-output). 	
05.06	Explain why data is the fuel of machine learning and the importance of quality data for gaining insights.  	
05.07	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). 	
05.08	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. 	
05.09	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. 	
05.10	Identify and describe sources of time-based data such as real-time information surrounding events, politics, and Internet trends behavior (e.g., trending keywords and subjects, foot traffic, web traffic). 	
05.11	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. 	
06.0	Think critically about data. The student will be able to:	
06.01	Understand and explain how data changes over time, e.g., weather through seasons.	
06.02	Analyze information and come up with conclusions based on the data presented. 	
06.03	Show ability to interpret and make predictions based on data that changes over time. 	
06.04	Interpret data representations accurately and describe meaningful patterns within the data.  	
06.05	Identify potential bias and missing data; accurately connect position statements with datasets that support them. 	
06.06	Separate fact-based information from opinion-based inferences. 	
06.07	Make quantitative and qualitative comparisons of data. 	
06.08	Use appropriate mathematical/ statistical methods to develop and/or support claims that address specific scientific questions.	
07.0	Understand how data is accessed, sorted, and stored. The student will be able to:	
07.01	Explain the role that computers play in storing and accessing data. 	
07.02	Explain how computing devices represent and manipulate information.  	
07.03	Identify and understand the roles of databases in everyday life. 	
07.04	Describe different kinds of databases (e.g., SQL, NoSQL, graph DBs) and how they organize and transform different kinds of data (e.g., tabular, knowledge graphs).	
07.05	Understand datasets with multiple categories/arranged in scaled graphs. 	
07.06	Understand and show ability to represent and manipulate data. 	
08.0	Develop and apply fundamental spreadsheet skills. The student will be able to:	
08.01	Define a “spreadsheet” and describe ways it may be used.   	
08.02	Identify the parts of the spreadsheet display, including cells, columns and rows, cell references, cell range.   	
08.03	Create and navigate through multiple spreadsheets in a file.   	
08.04	Insert and format various types of data (text, numeric, date/time) in a spreadsheet cell.    	
08.05	Select multiple cells, including adjacent and non-adjacent ranges, using mouse and keyboard techniques.   	
08.06	Cut, copy, and paste information from one or more cells to another part of the spreadsheet.   	
08.07	Use the undo and redo tools in a spreadsheet. 	
08.08	Apply and modify cell formatting for currency, date and percentage values.   	
08.09	Resize column width and row height in a spreadsheet.   	
08.10	Insert and delete columns and rows in a spreadsheet.   	
08.11	Merge and unmerge cells in a spreadsheet.    	
08.12	Apply shading and borders to a spreadsheet.   	
08.13	Describe the purpose of a table and how it relates to a spreadsheet.   	
08.14	Create and print a table and/or range that displays, sums, and averages the values of numeric data types.   	
08.15	Identify various types of charts (e.g., line, bar, pie, scatter) and common chart components (e.g., vertical axis, horizontal axis, legend), and explain when to use each chart type.   	
08.16	Create a chart from existing data and format the pieces (data set), change the background color, and add appropriate titles and a legend.    	
08.17	Insert common functions (SUM, AVERAGE, COUNT, MAX, MIN) and simple mathematical formulas which include addition, subtraction, multiplication, or division into a spreadsheet.    	
08.18	Distinguish between absolute and relative cell references in a spreadsheet.   	
08.19	Use the SORT function to organize information numerically or alphabetically, including multiple levels of sorting.   	
08.20	Use the FILTER function to display spreadsheet data based on specific criteria.    	
08.21	Use the PERCENTILE, MEDIAN, and MODE functions to understand data distributions better. 	
08.22	Use conditional formatting to highlight text in a spreadsheet.   	
08.23	Describe what a pivot table is and apply it to categorical data. 	
08.24	Apply custom functions to tabular data, e.g., linear fit.	
09.0	Develop and apply database knowledge and skills. The student will be able to:	
09.01	Define what a database is and describe real-world uses (e.g., student info, search engines, social media user data, etc.).      	
09.02	Identify the components of a database.    	
09.03	Distinguish between databases and spreadsheets.   	
09.04	Identify advantages of using a database instead of alternatives (e.g., spreadsheets, electronic documents, paper).   	
09.05	Define “Big Data” and describe how it is used in advertising.   	
09.06	Distinguish between fields and records in a database.   	
09.07	Describe the basic data types and formats used in a database.   	
09.08	Distinguish between a table and a query. 	
09.09	Describe Structured Query Language (SQL) and discuss its use with databases. 	
10.0	Explain and use design thinking to solve a problem. The student will be able to:	
10.01	Distinguish between social and personal problems to be solved through a design. 	
10.02	Identify a challenge of social importance and personal interest. 	
10.03	Develop problem statements embedded within a complex challenge.  	
10.04	Understand challenges and user needs more deeply. 	
10.05	Pattern and sort user data to define design challenges. 	
10.06	Use a formal brainstorming process to generate ideas.  	
10.07	Test and iterate prototypes. 	
10.08	Refine prototypes based on user feedback. 	
10.09	Create stakeholder maps. 	
10.10	Develop point-of-view statements for users.  	
10.11	Develop a blueprint for a prototype. 	
10.12	Gather feedback from users. 	
10.13	Revise prototypes and present rationale for final design. 	
11.0	Understand human and organizational behavior in the context of the data science process. The student will be able to:	
11.01	Identify the main stakeholders (personas) in a data analysis/ data science project, describe their roles and motivating factors (e.g. Data Scientist “free data” vs. DevOps "data security first”).  	
11.02	Not treat new technology (e.g., AI) either as the “worst thing ever” or “best thing ever”: Realize the existence and ramifications of opposing desires, (1) need for control/ preference for status quo/ fear of the unknown, vs. (2) fear of missing out on the next great technical breakthrough/ hype. 	
11.03	Recognize and apply learning theories, e.g., the impact of cognitive overload, zone of proximal development, etc. Understand the roadblocks in communicating new concepts to stakeholders and/or convincing them to adopt new data-related practices.	
11.04	Describe what Dunning-Kruger Effect and Imposter Syndrome are and understand how they affect stakeholders in a data science project. 	
12.0	Demonstrate knowledge of data governance (safety and privacy) practices for data use. The student will be able to:	
12.01	Understand that different datasets have different ownership structures and describe them. 	
12.02	Recognize the significance and motivations of various data owning entities, e.g., government, commercial, education, non-profit, personal. 	
12.03	Identify ways that data can be encrypted in storage and transit.  	
12.04	Name different data anonymization methods and know how they can be applied to different types of data.   	
12.05	Realize the importance of forming data access groups, how to implement them and ensure they are functioning. 	
13.0	Identify and apply different data collection methods and understand the importance of data quality. The student will be able to:	
13.01	Name different ways that data can be collected and their use cases: sensors (in fitness devices, cars, drones), Internet usage (cookies), online/offline surveys, etc. 	
13.02	Document relevant information (metadata) about the collected data: e.g., 5W’s and how it was collected, what each data field or column title means, what data format should each contain, how often the data might be updated and by what methods.  	
13.03	Describe different ways that data could be erroneous (e.g., data type errors, misspelling) and data collection practices that might result in bad or missing data (e.g., undocumented manual data entry).  	
13.04	Identify methods to correct bad or missing data and their pros vs. cons (e.g., omit, interpolate, impute). 	
13.05	Practice implementing corrective methods for improving data quality.	
13.06	Recognize the importance of and develop protocol for monitoring data quality.	
14.0	Generate and tell stories with data. The student will be able to:	
14.01	Describe the information various online datasets provide and identify the types of questions they can and cannot answer. 	
14.02	Decode data: Identify and extract measurements, values or data points and what they represent from graphs, tables, or other data representations. 	
14.03	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.) 	
14.04	Collect, organize, proofread, and manipulate data with spreadsheet functions. 	
14.05	Apply statistics to find patterns in data. 	
14.06	Use spreadsheet functions to create tables and graphs to visually represent and communicate findings. 	
14.07	Construct informed summaries, decisions, and predictions related to the data. 	
14.08	Tell a data story using appropriate visualizations that are clear, compelling and accessible for the audience (e.g., infographics, charts, graphs, etc.). 	
15.0	Know and apply best practices in data visualization. The student will be able to:	
15.01	Realize that the human brain combines visual cues and form immediate conclusions. 	
15.02	Give examples of "simple" vs. "complex" charts and identify situations where they work best.  	
15.03	Consider the audience and goals of the data visuals and make appropriate design decisions. 	
15.04	Decide whether or how to shape the data before constructing the graphs. 	
15.05	Determine which chart types to use and make appropriate design decisions. 	
15.06	Apply and shape visual elements to convey desired messages, e.g., color, size, placement, etc. 	
15.07	Make sure that chart elements e.g. axes, labels, titles, are accurate and don't mislead the readers.  	
15.08	Define "chart junk" and know how to minimize it.  	
15.09	Realize that stories and narratives often make data conclusions more compelling.  	
15.10	Construct a logical presentation flow in presenting findings from data analysis. 	
15.11	Apply appropriate design choices for presentation slides and other elements. 	
15.12	Present data in an appropriate and compelling way for the audience. 	
16.0	Be aware of the limitations of statistics and cognitive biases in data analysis. The student will be able to:	
16.01	Identify and understand what the Anscomb's Quartet illustrates about the limitation of summary statistics. 	
16.02	Recognize and understand how sampling bias can occur.  	
16.03	Define and give examples of cognitive biases, e.g., survivorship bias, confirmation bias, anchoring, availability, endowment effect, correlation vs. causation, etc. 	
17.0	Critique data and data-based claims to avoid being misled by data. The student will be able to:	
17.01	Provide examples of the social and personal consequences of predictions based on models built on data.  	
17.02	Distinguish between credible vs. unreliable/biased information sources.   	
17.03	Recognize and describe signs of compromised information or data.  	
17.04	Identify and describe errors in decisions and predictions due to faulty use of data (e.g., confounding, random errors). 	
17.05	Describe issues of privacy and security with respect to data collection, storage, analysis, and insights. 	
17.06	Discuss how and when data can support making decisions. 	

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 SkillsUSA and Florida Technology Student Association are the co-curricular career and technical student organizations 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. 

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