Skip to main content

High School Center Programs ⟶ Program Themes ⟶ Machine Learning and Data Science

Machine Learning and Data Science

The Machine Learning and Data Science Center Program prepares high school students for a data‑driven future by building strong foundations in data science and machine learning. Through hands‑on learning and real‑world problem solving, students develop critical‑thinking skills, computational fluency, and curiosity about how data informs decisions. The program emphasizes ethical reasoning, collaboration, and innovation, empowering students to use technology responsibly to make meaningful contributions to their communities and beyond.
 

Core Experiences

  • Earn Microsoft Certified: Azure Data Scientist Associate or CompTIA Data+ Certification
  • Participate in internships
  • Earn college credit
  • Connect with industry professionals, university faculty, and industry mentors
  • Participate in competitions and student organizations

 

Program Goals

  1. Develop Foundational Knowledge and Skills
    Equip students with a strong foundation in computer science, and data analysis through rigorous, standards-based coursework. Emphasize problem-solving, critical thinking, creativity, and collaboration, while building the required skills to prepare for advanced study and careers in machine learning and data science.
  2. Provide Hands-On, Real-World Experience
    Engage students in labs, collaborative projects, and real-world data challenges. Provide experience in data collection, analysis, and application of machine learning techniques. Offer internships, mentorships, and project-based work with local and national organizations to develop practical skills, and career readiness.
  3. Foster Career and College, Career and/or Community Pathways
    Support students in exploring and pursuing postsecondary education and careers in data science, machine learning, AI, and related technology fields through the College Preparatory pathway.

 

Application & Locations

Application: Interest-based application

Schools:

  • Central South Region: Woodward

Course Progression

Below is a sample sequence of courses for students enrolled in the Machine Learning & Data Science Program. Cells in blue are related to the program pathway. This is subject to change as the programs are developed.

Grades 6–8 Grade 9 Grade 10 Grade 11 Grade 12

Career Advising

Summer Enrichment & Preparation
AP Computer Science Principles with Python Machine Learning and Data Science Course 1 Machine Learning and Data Science Course 2 Machine Learning and Data Science Course 1 or Work-based Learning or Dual Enrollment
Machine Learning & Data Science Center Essentials Elective (PE, Fine Arts or Health) AP Computer Science Java

Modeling & Simulation (0.5 credit)

AND

Introduction to Artificial Intelligence (0.5 credit)
Math Math Math Math* (at least one math credit needs to be in statistics)
World Language World Language World Language or Elective World Language or Elective
U.S. History NSL Government World History Elective (PE, Fine Arts or Health)
English AP English 10 Seminar with Center Focus English English
Science Science Science Elective (PE, Fine Arts or Health)

Program Requirements

In addition to MCPS requirements for graduation, tudents must complete the following to remain enrolled in the Machine Learning and Data Science Program and earn the related certificate:

  • 1 credit introductory computer science based on student experience
    • AP Computer Science Principles with Python
    • AP Computer Science A (Java)
  • 2 Credits Machine Learning & Data Science (1 & 2)
  • 1 Credit advanced Machine Learning & Data Science
    • Machine Learning & Data Science 3 or
    • Related work-based learning or
    • Related dual enrollment course
  • 1 credit statistics course
    • Statistics and Math Modeling or
    • Honors Statistics or
    • AP Statistics
  • 2 credits advanced computer science elective
    • AP Computer Science A (Java) or
    • Modeling & Simultion (0.5 credit)
    • Introduction to AI (0.5 credit)