Data Science & Analytics

Shared first block for every VoTechWorks program. OSHA 10 (30 for construction management), first aid/CPR/AED, applied trade math and measurement, technical reading and writing, digital literacy, and career readiness. Completed before students enter the lab and live systems unsupervised.

• Hold an OSHA 10 card and current CPR/AED certification
• Convert, measure and estimate with trade units, fractions, percentages and basic geometry
• Read a specification, code excerpt or manual and write a clear technical report
• Use email, spreadsheets, and the program's digital job-management tools
• Present a resume, portfolio and interview answers to an employer panel

Competency course in Statistics for the Data Science & Analytics program. Instruction moves from demonstration to supervised practice to independent performance in the lab and live systems, measured against vendor documentation and industry standards. Students configure, secure and troubleshoot real work in Statistics and keep a lab notebook signed off by the instructor.

• Explain the principles, terminology and standards that govern Statistics
• Select and safely use the tools, equipment and materials for Statistics
• Perform the core tasks of Statistics to the standard, within time, without rework
• Identify faults, hazards and non-conformities in Statistics work and correct them
• Document Statistics work in the lab notebook to industry format

Competency course in Python/R for the Data Science & Analytics program. Instruction moves from demonstration to supervised practice to independent performance in the lab and live systems, measured against vendor documentation and industry standards. Students configure, secure and troubleshoot real work in Python/R and keep a lab notebook signed off by the instructor.

• Explain the principles, terminology and standards that govern Python/R
• Select and safely use the tools, equipment and materials for Python/R
• Perform the core tasks of Python/R to the standard, within time, without rework
• Identify faults, hazards and non-conformities in Python/R work and correct them
• Document Python/R work in the lab notebook to industry format

Competency course in SQL/Postgres for the Data Science & Analytics program. Instruction moves from demonstration to supervised practice to independent performance in the lab and live systems, measured against vendor documentation and industry standards. Students configure, secure and troubleshoot real work in SQL/Postgres and keep a lab notebook signed off by the instructor.

• Explain the principles, terminology and standards that govern SQL/Postgres
• Select and safely use the tools, equipment and materials for SQL/Postgres
• Perform the core tasks of SQL/Postgres to the standard, within time, without rework
• Identify faults, hazards and non-conformities in SQL/Postgres work and correct them
• Document SQL/Postgres work in the lab notebook to industry format

Competency course in data cleaning for the Data Science & Analytics program. Instruction moves from demonstration to supervised practice to independent performance in the lab and live systems, measured against vendor documentation and industry standards. Students configure, secure and troubleshoot real work in data cleaning and keep a lab notebook signed off by the instructor.

• Explain the principles, terminology and standards that govern data cleaning
• Select and safely use the tools, equipment and materials for data cleaning
• Perform the core tasks of data cleaning to the standard, within time, without rework
• Identify faults, hazards and non-conformities in data cleaning work and correct them
• Document data cleaning work in the lab notebook to industry format

Competency course in visualization for the Data Science & Analytics program. Instruction moves from demonstration to supervised practice to independent performance in the lab and live systems, measured against vendor documentation and industry standards. Students configure, secure and troubleshoot real work in visualization and keep a lab notebook signed off by the instructor.

• Explain the principles, terminology and standards that govern visualization
• Select and safely use the tools, equipment and materials for visualization
• Perform the core tasks of visualization to the standard, within time, without rework
• Identify faults, hazards and non-conformities in visualization work and correct them
• Document visualization work in the lab notebook to industry format

Competency course in dashboards for the Data Science & Analytics program. Instruction moves from demonstration to supervised practice to independent performance in the lab and live systems, measured against vendor documentation and industry standards. Students configure, secure and troubleshoot real work in dashboards and keep a lab notebook signed off by the instructor.

• Explain the principles, terminology and standards that govern dashboards
• Select and safely use the tools, equipment and materials for dashboards
• Perform the core tasks of dashboards to the standard, within time, without rework
• Identify faults, hazards and non-conformities in dashboards work and correct them
• Document dashboards work in the lab notebook to industry format

Competency course in intro ML for the Data Science & Analytics program. Instruction moves from demonstration to supervised practice to independent performance in the lab and live systems, measured against vendor documentation and industry standards. Students configure, secure and troubleshoot real work in intro ML and keep a lab notebook signed off by the instructor.

• Explain the principles, terminology and standards that govern intro ML
• Select and safely use the tools, equipment and materials for intro ML
• Perform the core tasks of intro ML to the standard, within time, without rework
• Identify faults, hazards and non-conformities in intro ML work and correct them
• Document intro ML work in the lab notebook to industry format

Competency course in GIS/spatial data for the Data Science & Analytics program. Instruction moves from demonstration to supervised practice to independent performance in the lab and live systems, measured against vendor documentation and industry standards. Students configure, secure and troubleshoot real work in GIS/spatial data and keep a lab notebook signed off by the instructor.

• Explain the principles, terminology and standards that govern GIS/spatial data
• Select and safely use the tools, equipment and materials for GIS/spatial data
• Perform the core tasks of GIS/spatial data to the standard, within time, without rework
• Identify faults, hazards and non-conformities in GIS/spatial data work and correct them
• Document GIS/spatial data work in the lab notebook to industry format

Competency course in ethics & privacy for the Data Science & Analytics program. Instruction moves from demonstration to supervised practice to independent performance in the lab and live systems, measured against vendor documentation and industry standards. Students configure, secure and troubleshoot real work in ethics & privacy and keep a lab notebook signed off by the instructor.

• Explain the principles, terminology and standards that govern ethics & privacy
• Select and safely use the tools, equipment and materials for ethics & privacy
• Perform the core tasks of ethics & privacy to the standard, within time, without rework
• Identify faults, hazards and non-conformities in ethics & privacy work and correct them
• Document ethics & privacy work in the lab notebook to industry format

Competency course in capstone with real city/urbanicity data for the Data Science & Analytics program. Instruction moves from demonstration to supervised practice to independent performance in the lab and live systems, measured against vendor documentation and industry standards. Students configure, secure and troubleshoot real work in capstone with real city/urbanicity data and keep a lab notebook signed off by the instructor.

• Explain the principles, terminology and standards that govern capstone with real city/urbanicity data
• Select and safely use the tools, equipment and materials for capstone with real city/urbanicity data
• Perform the core tasks of capstone with real city/urbanicity data to the standard, within time, without rework
• Identify faults, hazards and non-conformities in capstone with real city/urbanicity data work and correct them
• Document capstone with real city/urbanicity data work in the lab notebook to industry format

An integrated, assessed piece of real work that draws on every trade course in Data Science & Analytics: planned, estimated, executed and documented by the student, reviewed by an employer advisor. Capstones are shown in the Student Yard or the Museum of VoTech where the work allows.

• Plan and estimate a complete job from a specification
• Execute the job to standard within the estimate
• Present the finished work and its documentation to an employer panel

Supervised placement with an employer partner in Data Science & Analytics work. Hours are logged against the certifying body's task list; the employer evaluates against the same rubric used in the shop.

• Perform the trade's tasks in a real workplace under supervision
• Meet employer expectations for attendance, safety and conduct
• Log hours and tasks in the format the certifying body accepts

Structured preparation for CompTIA Data+; Google Data Analytics Cert; IBM Data Science Professional; Microsoft PL-300 (Power BI): content review mapped to the exam blueprint, timed practice exams, practical-exam rehearsal, and registration/eligibility paperwork with the registrar.

• Score ≥ 80% on two timed mock exams
• Complete the eligibility application and hours verification
• Rehearse the practical exam format to time

The program completion credential(s). Sat at the school's authorized testing center or the body's test site; results recorded in the student record and reported in the annual outcomes report. Pass standard: Credential issued by the body.