Florida Department of Education
Curriculum Framework 

Program Title:	Data Science Technology
Career Cluster:	Information Technology

Associate in Science	
CIP Number	1530700100	
Program Type	College Credit	
Program Length	60 credit hours	
CTSO	BPA	
SOC Codes (all applicable) 	For program SOC codes, please see the Program and Course Tables section of the CTE Program Resources page linked below.	
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 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 Information Technology career cluster.

The content includes but is not limited to in-depth instruction on activities performed in the acquisition of data in structured and unstructured formats, cleaning, modeling, and analysis of acquired data, and extraction of knowledge or insights using statistical processes and systems. Additional content includes identification of data sources, retrieval issues and methodologies, data security, data ethics, and the use of other informational tools.

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 60 credit hours.

When developing the course sequence for the local implementation of this program, select courses that:
•	Meet the skill development requirements of the standards and benchmarks of this program.
•	Meet the general education core course requirements of Rule 6A-14.0303, Florida Administrative Code.
•	Where reasonable and practicable, align with programs offered by your institution or other postsecondary institutions in Florida, including: 
o	Related CCC and ATD credentials.
o	Baccalaureate degrees within the same career pathway and/or Federal CIP code.

Visit FloridaShines’ Discover Programs website to identify other institutions that offer similar programs to review their course sequencing. Where appropriate, course alignment between institutions offering related programs can facilitate interagency student articulation.

Visit FloridaShines’ Common Prerequisites Manual to identify courses that are prerequisites for admission into an upper division program.  When sub-baccalaureate courses are aligned to the course admissions requirements of baccalaureate programs with the same career pathway, students can enter upper division coursework seamlessly.   

Florida’s Career Readiness Skills for CTE Programs  

Employability Skills	
01.0	Apply academic skills to workplace scenarios.	
01.01	Use reading skills.	
01.02	Use writing skills.	
01.03	Use mathematical strategies and procedures.	
01.04	Use scientific principles and procedures.	
02.0	Design a solution to an industry problem.	
02.01	Use critical thinking.	
02.02	Use creativity.	
02.03	Make sound decisions.	
02.04	Solve problems.	
02.05	Reason. 	
02.06	Plan and organize.	
03.0	Manage resources within an industry project	
03.01	Manage time.	
03.02	Manage money or resources.	
03.03	Manage materials.	
03.04	Manage personnel.	
04.0	Oversee the subcomponents, operations and output of a technical or organizational system.	
04.01	Manage systems.	
04.02	Monitor systems.	
04.03	Improve systems.	
05.0	Use information for decision making.	
05.01	Locate information.	
05.02	Organize information.	
05.03	Use information.	
05.04	Analyze information.	
05.05	Communicate information.	
06.0	Apply relevant technology to workplace scenarios to aid productivity.	
06.01	Use technology.	
07.0	Interpret and express interpersonal communication.	
07.01	Communicate verbally.	
07.02	Listen actively.	
07.03	Comprehend written material.	
07.04	Convey information in writing.	
07.05	Communicate nonverbally.	
07.06	Interpret nonverbal communication.	
08.0	Interact with others to accomplish workplace goals.	
08.01	Collaborate with others in a team.	
08.02	Respond to customer needs.	
08.03	Exercise leadership.	
08.04	Negotiate to resolve conflict.	
08.05	Respect others.	
09.0	Manage personal behavior to maximize productivity and professional growth.	
09.01	Demonstrate responsibility and self-discipline.	
09.02	Adapt and show flexibility.	
09.03	Work independently.	
09.04	Demonstrate a willingness to learn.	
09.05	Demonstrate integrity.	
09.06	Demonstrate professionalism.	
09.07	Take initiative.	
09.08	Display positive attitude.	
09.09	Take responsibility for professional growth.	
Job Attainment	
10.0	Find, assess and apply to job opportunities.	
10.01	Identify online job posts relevant to his or her career aspirations.	
10.02	Compare and contrast the job posts’ required qualifications, job duties, compensation, benefits and employers. 	
10.03	Define what information, documentation and writing prompts are required for the positions.	
11.0	Communicate personal competence, character and fit for a job opportunity.	
11.01	Develop a resume.	
11.02	Write a cover letter.	
11.03	Curate a professional portfolio that includes work products.	
11.04	Prepare for and experience a mock job interview.	
12.0	Cultivate and leverage relationships to professionally advance.	
12.01	Request a signed reference letter, letter of recommendation and/or an online skill/professionalism endorsement. 	
12.02	Develop a plan to cultivate a professional digital footprint.	
12.03	Develop a networking plan for a specific industry of interest.	

Standards

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

01.0	Describe the data life cycle.   
02.0	Describe basic statistical concepts and apply statistical methods used in data science problems.    
03.0	Describe selection, collection, preprocessing, and transformation processes used with data sources.    
04.0	Describe modelling, analysis, and visualization techniques applied to acquired data.    
05.0	Describe security best practices for each phase of acquisition, analysis, and retention of data.    
06.0	Describe ethical best practices related to each phase of the data life cycle.    
03.0	

Florida Department of Education
Student Performance Standards

Program Title:	Data Science Technology
CIP Number:		1530700100
Program Length:	60 credit hours

At the completion of this program, the student will be able to:	
01.0	Describe the data life cycle.  The student will be able to:	
01.01	Describe ways in which data can be acquired:	
•	Describe data sources and methods for acquiring data. 	
•	Describe how data is captured (i.e., from control systems devices or Internet of things devices, etc.).	
•	Describe how acquired data is cleaned and transformed.	
01.02	Describe techniques for analyzing data: 	
•	Describe data models.	
•	Describe techniques used for data visualization.	
•	Describe statistical methods that are applied to data to extract useful information.	
•	Describe the process of Exploratory Data Analysis.	
01.03	Describe how data analysis results can be reported.	
•	Describe dashboards and how they can be used to make business decisions.	
•	Determine appropriate reporting formats.	
02.0	
Describe basic statistical concepts and apply statistical methods used in data science problems.  The student will be able to:	
02.01	Describe the difference between population and sample data.	
02.02	Construct frequency distributions.	
02.03	Use descriptive statistical methods to analyze sets of data.	
02.04	Use probability rules to solve probability problems.	
02.05	Solve problems involving discrete probability distributions including the binomial probability distribution.	
02.06	Construct confidence intervals from sample data.	
02.07	Conduct tests of hypotheses with one and two samples.	
02.08	Use correlation and linear regression methods to analyze data.	
03.0	Describe selection, collection, preprocessing, and transformation processes used with data sources.  The student will be able to:	
03.01	Describe criteria and procedures used for data selection.	
03.02	Compare attributes and benefits of data sources and associated collection strategies including:	
•	Structured and unstructured data.	
•	RDBMS (Relational Database Management Systems).	
•	Data warehouses and OLAP (Online Analytical Processing) Cubes.	
•	Spreadsheets.	
•	Data Serialization Languages, e.g., XML (eXtensible Markup Language), JSON (JavaScript Object Notation), YAML (Yet Another Markup Language, or "YAML Ain't Markup Language")	
•	CSV (Comma Separated Values) data.	
•	Web data.	
•	GIS (Geographical Information Systems) data.	
•	Raw data.	
03.03	Describe and utilize data preprocessing and normalization:	
•	Describe and apply common techniques for cleaning textual, numeric, and categorical data.	
•	Describe the use of probabilistic methods and decision trees for classification.	
•	Describe the use and applicability of transformations used to normalize data distributions.	
03.04	Describe OLTP (Online Transaction Processing) design concepts and principals.	
03.05	Use data processing tools (e.g., SAS, PowerBI, Tableau, etc.) or programming languages (e.g., Python, R, Java, etc. and associated libraries) to collect and clean data from various sources.	
03.06	Create a data warehouse:	
•	Design an OLAP database using dimensional modeling techniques.	
•	Create a data warehouse based on OLAP design.	
•	Create an ETL (Extract/Transfer/Load) process to populate and update the data warehouse from OLTP database utilizing SQL Server Integration (SSIS).	
04.0	Describe modelling, analysis, and visualization techniques applied to acquired data.  The student will be able to:	
04.01	Describe and apply the use of linear regression and decision trees in data modeling.	
04.02	Develop data cubes utilizing SQL Server Reporting Services (SSRS).	
04.03	Use statistical tools (e.g., SAS, SPSS, etc.) or programming languages (e.g., R, Python, etc. with associated libraries) to solve various statistical problems:	
•	Calculate summary statistics.	
•	Calculate correlation values.	
•	Construct confidence intervals from sample data.	
•	Conduct tests of hypotheses with one and two samples.	
•	Analyze data using correlation and linear regression methods.	
04.04	Describe the advantages of large-scale data analysis tools (e.g., Apache Hadoop, Apache Spark, Google BigQuery, Data Torrent RTS, etc.) for solving data science problems.	
04.05	Create graphical representations of data models and analysis results using data visualization tools (e.g., Tableau, PowerBI, SAS, etc.) or programming languages (e.g., R, JavaScript, Python, Java, etc. with associated libraries).	
04.06	Develop dashboards utilizing reporting tools (e.g., SharePoint, Tableau, PowerBI, etc.).	
05.0	Describe security best practices for each phase of acquisition, analysis, and retention of data.  The student will be able to:	
05.01	Describe basic security principles including the CIA Triad, general security concepts, communication security, confidentiality, authentication and other cryptography concepts, and operational and organizational security.	
05.02	Describe risks associated with data privacy and integrity.	
05.03	Describe basic web security.	
05.04	Describe security methodologies as they relate to data protection and availability.	
06.0	Describe ethical best practices related to each phase of the data life cycle.  The student will be able to:	
06.01	Describe various professional organization ethics guidelines that can be applied to data science.	
06.02	Identify stakeholders in ethical reasoning scenarios.	
06.03	Describe scenarios where professional organization ethics guidelines can apply to each phase of the data lifecycle.	
06.04	Describe how ethical reasoning can be applied to support decision making in various scenarios.	

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.

General Education Course Requirements for AS and AAS Degrees

State Board of Education Rule 6A-14.030(4), F.A.C., identifies 15 credit hours as the minimum amount of general education coursework required in the Associate of Science (AS) degree and the Associate of Applied Science (AAS) degree.  In addition, Rule 6A-14.0303, F.A.C., implements s. 1007.25, F.S., and requires students entering a technical education degree program in the 2022-2023 academic year, and thereafter, to complete at least one identified core course in each subject area as part of the general education course requirements (15 credit hours total) before a degree is awarded) The core subject areas include: 
•	Communication. 
•	Humanities.
•	Mathematics. 
•	Natural Sciences.
•	Social Sciences.

Career and Technical Student Organization (CTSO)

CTSOs are 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. Other CTSOs not listed in this curriculum framework or recognized by the Florida Department of Education are permissible provided they support student mastery over the standards and benchmarks of this curriculum framework.

Accommodations

Federal and state legislation requires the provision of accommodations for students with disabilities to meet individual needs and ensure equal access. Postsecondary students with disabilities must self-identify, present documentation, request accommodations if needed, and develop a plan with their counselor and/or instructors. Accommodations received in postsecondary education may differ from those received in secondary education. 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.

Certificate Programs

A College Credit Certificate consists of a program of instruction of less than sixty (60) credits of college-level courses, which is part of an AS or AAS degree program and prepares students for entry into employment (Rule 6A-14.030, F.A.C.).  This AS degree program includes the following College Credit Certificates:

Data Science Technician (0530700100) – 36 credit hours 
FinTech Technician (0530710400) – 33 credit hours 

Standards for the above certificate programs are contained in separate curriculum frameworks.