AI 101: Data Science and Machine Learning Foundation

Students learn through hands-on projects that take them from raw data to analysis, insights, and machine learning models.

This course opens a new way for students to understand the world through data. Rather than learning AI and data science only as technical skills, students will use real-world datasets to explore questions and discover new perspectives in areas they may not normally encounter. Through projects spanning healthcare, sports, business, politics, social sciences, and other fields, students will step outside their comfort zones, investigate unfamiliar topics, and learn how data can help us better understand people, society, and the world around us.

Former Students — Selected Projects and Research

Wharton Global High School Data Science Competition:

• Semi-finalist teams competing in a national data science competition.

Former Upper School Student Research:

• Educational Factors Associated With High School Students’ Use of Generative AI: A Cross-Institutional Mixed-Methods Study — research paper submitted to the National High School Journal of Science.

• Comprehensive Literature Review of LLM Architectures — student subsequently accepted to Carnegie Mellon University (CMU).

Former Student Independent Projects:

• Computer Vision & Robotics: A deep learning model for real-time hand-pose tracking to autonomously pilot hardware drones — student subsequently accepted to CMU.

• Socio-Economic Data Science Product: A personalized recommendation engine designed to help users discover affordable food options in high-poverty, nutrition-scarce geographic areas.

Unit 1: Data Visualization

• Explore and understand tabular data: rows, columns, variables, and data types

• Clean and prepare data for analysis

• Create and interpret different types of data visualizations

• Choose the right visualization for different questions

• Use visualizations to identify patterns, trends, and outliers

• Turn visual findings into meaningful data-driven insights

• Present insights clearly using charts and storytelling

  
  

Unit 2: Quantitative Analysis

• Understand how data can be used to answer questions quantitatively

• Learn correlation analysis and distinguish correlation from causation

• Calculate and interpret Pearson correlation

• Analyze relationships between categorical variables using the Chi-square test

• Understand statistical significance and p-values at an introductory level

• Use quantitative evidence to support or challenge hypotheses

• Combine statistical analysis with visualization to tell a stronger data story

Unit 3: Machine Learning Core Concepts

• Understand what machine learning is and how it differs from traditional programming

• Learn the basic machine learning workflow: data → features → model → prediction → evaluation

• Understand features, labels, training data, and test data

• Learn the difference between supervised and unsupervised learning

• Understand how models learn patterns from data

• Learn about training, validation, and testing

• Understand overfitting and underfitting

• Learn why we need appropriate evaluation metrics

• Explore the importance of data quality, feature selection, and model assumptions

• Develop an intuition for how machine learning models make predictions

Unit 4: Machine Learning Modeling

• Build and evaluate machine learning models using real datasets

• Understand classification vs. regression

• Learn and compare different modeling approaches:

  • Linear Models — predict outcomes using relationships between variables

  • Decision Trees — make predictions through a sequence of decisions

  • Neural Networks — learn complex patterns through layers of connected nodes

• Train models and make predictions

• Compare models using appropriate evaluation metrics

• Interpret model results and identify limitations

• Apply modeling techniques to a real-world project

• Communicate the model, results, and insights through a final presentation