Course Dates: June 28 - July 10 

Course Description

Data Science is a multidisciplinary field that blends data inference, algorithm development, and technology, transforming raw data into meaningful insights and innovations. This course introduces high school students to this critical and burgeoning field. Emphasizing both quantitative analysis and qualitative interpretation, the program begins with Python programming fundamentals and advances through key concepts like data structures, manipulation, and exploratory data analysis (EDA). A special focus on Natural Language Processing (NLP) highlights the interdisciplinary nature of data science, integrating computational methods with linguistic insights. Students will engage in hands-on projects, delve into real-world datasets, and acquire skills to convert data into compelling stories and actionable intelligence. This course is a gateway into the expansive world of data science, where machine learning, artificial intelligence, and big data are pivotal tools in shaping our future.

Learning Outcomes

• Python for Data Science: Gain hands-on experience in Python, focusing on its application in data science, including understanding data structures, and libraries like Pandas and NumPy.
• Fundamentals of Data Analysis and Visualization: Perform exploratory data analysis (EDA), interpret data through statistical methods, and create meaningful visualizations using tools like Matplotlib and Seaborn.
• Natural Language Processing (NLP): Learn to process and analyze text data, including text manipulation, sentiment analysis, and creating visual representations like word clouds.
• Execution of Data Science: Work on exercises aligned with each day's topic, culminating in a project where students reflect on their discoveries.
• Critical Thinking and Problem-Solving in Data Science: Develop critical thinking skills specific to data science, learning to approach problems analytically, question assumptions, and interpret results within context.

Tangible Outcomes

Students have the option of downloading all coding notebooks and programs for future use

Hands-On Activities

• Data science project on Spotify data including interactive data visualizations
• Coding a simple python game
• Robotics competition
• Murder mystery exercise using python,
• Experience with motion capture
• Building an AI chatbot

Guest Speakers

• DREAM Studio, AR/VR, Motion Capture: Claire Preston, John Bell
• Robotics: Simon Stone, Jonathan Crossett
• Databases: Elijah Gagne, Simon Stone
• Spatial Data: Stephen Gaughan
• Artificial Intelligence: Jonathan Crossett Simon Stone

Benefits for Future Study

This course exposes students to work in data science, computer science, artificial intelligence, virtual reality, and robotics. Many possible academic paths are possible, and it broadly exposes students to future careers in data and technology.

Course Description

Energy Implications of AI Data Centers is a multidisciplinary survey course that explores the growing energy demands driven by AI data centers and their impact on the electric grid. Students will study the fundamentals of electricity supply and demand, examine emerging technologies for meeting increased energy needs, and analyze regulatory and market mechanisms that influence power generation choices. Special focus is given to Virtual Power Plants (VPPs) and their role in managing grid reliability, cost-efficiency, and environmental impact. Through case studies and comparative analyses, students will evaluate the benefits, risks, and customer perspectives of innovative energy programs supporting sustainable AI infrastructure.

Learning Outcomes

At the end of this survey course, students will be able to:

• Describe electricity supply and demand on the electric grid and projected increases in demand.
• Compare technologies for meeting increased demand – for both generating electricity and managing demand – on attributes including cost, capacity, speed of deployment, and societal impacts.
• Explain essentials of the market that determines which power plants (and Virtual Power Plants) are chosen to supply the grid while minimizing cost and complying with regulations.
• Present a comparison of benefits, risks, and costs of two Virtual Power Plant programs from the customer perspective.

Tangible Outcomes

Presentation that compares benefits, risks, and costs of two Virtual Power Plant programs from the customer perspective.

Hands-on Activites

In an activity where students move around the room, the group will simulate how the wholesale market in the electric grid chooses which power plants will supply the grid for each time period.

Field Trips

Classroom learning will be brought to life on tours of power plants fueled by several of the following technologies: solar, wind, hydropower, coal, natural gas, petroleum, landfill gas, biomass, nuclear, and battery storage.

Course Description

From bridges and airplanes to medical devices and renewable energy systems, the world runs on engineered materials—and the ability to shape them. This two-week immersive course, taught by the Dartmouth Machine Shop staff, introduces students to the fundamentals of fabrication and materials engineering. Students will gain hands-on experience with machining, welding, 3D printing, and computer-aided design (CAD), while also exploring how these skills translate into careers in materials science and engineering.

Alongside technical training, the course emphasizes problem-solving, safety, and creativity in design. Students will visit Dartmouth engineering labs and learn how materials engineering intersects with innovation in fields such as sustainability, medicine, and aerospace. By the end of the program, participants will not only build their own projects but also develop a deeper understanding of how fabrication skills lay the groundwork for future careers in engineering and technology.

Learning Outcomes

By the end of this course, students will be able to:

• Demonstrate safe practices when working with machining, welding, and fabrication equipment.
• Use basic fabrication tools and techniques, including milling, turning, welding, and additive manufacturing.
• Apply computer-aided design (CAD) software to develop and prototype simple projects.
• Identify key properties of common engineering materials (metals, polymers, composites) and explain their applications.
• Understand how fabrication skills connect to broader areas of materials engineering and real-world innovation.
• Collaborate effectively on design-build challenges, applying creativity and engineering problem-solving.
• Produce a final project that demonstrates learned fabrication techniques and reflects engineering design principles.

Hands-on Activities 

Week 1: Foundations of Fabrication

• Safety & Orientation: Machine shop protocols, PPE, and safe tool use.
• Fabrication Basics: Introduction to milling, lathes, and welding through guided demonstrations and practice.
• CAD & Prototyping: Hands-on sessions with CAD software and 3D printing for rapid prototyping.
• Materials Overview: Mini-seminar on metals, polymers, ceramics, and composites—properties and uses.
• Mini-Project #1: Design and fabricate a simple mechanical component (e.g., a custom tool or small device).

Week 2: From Materials to Engineering Applications

• Advanced Fabrication Techniques: Students rotate through stations (precision machining, welding, 3D printing, laser cutting).
• Engineering in Action: Visits to Dartmouth labs (e.g., Thayer School of Engineering) to see materials research in medicine, aerospace, and sustainability.
• Innovation & Careers in Materials Engineering: Guest talks from faculty, researchers, and alumni.
• Design-Build Challenge: In small teams, students design and fabricate a functional object that addresses a real-world challenge (e.g., lightweight structure, ergonomic tool, or sustainable design).
• Final Project Showcase: Students present their prototypes and reflect on fabrication skills, materials selection, and career connections in materials engineering.

Course Description

Artificial Intelligence is a rapidly evolving field that integrates mathematics, computer science, and engineering to create systems capable of learning, reasoning, and decision-making. This course introduces high school students to the foundations, implementation and applications of AI, from the algorithms behind TikTok recommendations and ChatGPT to innovations in self-driving cars, finance, robotics, and healthcare. Emphasizing both conceptual understanding and practical experimentation, the program covers supervised and unsupervised learning, natural language processing, deep learning and large Language models (ChatGPT). Students will engage in guided assignments, Hackathons, interactive demonstrations, and project-based learning, gaining the skills to design creative AI solutions while critically examining the ethical questions surrounding this transformative technology.

Learning Outcomes

• Proficiency in Python for AI: Gain hands-on experience in Python programming with a focus on artificial intelligence and machine learning, including practice with core libraries such as Pandas, PyTorch, TensorFlow, and Hugging Face.
• Foundations of Artificial Intelligence: Understand key AI concepts including supervised and unsupervised learning, computer vision, natural language processing, and deep learning.
• Hands-On AI Applications: Build and deploy AI-driven web applications such as object detection tools, financial prediction models, and AI agents (like ChatGPT5 Agent mode) for tasks like email automation.
• AI Across Industries: Explore real-world case studies highlighting how AI transforms healthcare, finance, robotics, and social media, and connect theory with practice through interactive examples.
• Experiential Learning and Collaboration: Participate in an ethics-focused hackathon, engage in case study challenges, and learn from guest lectures by industry experts (e.g., Google, Toast, Amazon). Work in teams to design, present, and defend an innovative AI project that demonstrates technical understanding and creativity.

Tangible Outcomes 

• Team Project Demo: A fully functional AI web-based application, such as an object detection tool, financial prediction dashboard, or chatbot/AI agent.
• Coding Portfolio: Python notebooks and scripts showcasing proficiency with libraries such as Pandas, PyTorch, TensorFlow, and Hugging Face, organized and shared through GitHub and Kaggle.
• Case Study Competition: Written analyses and presentations on real-world AI applications in healthcare, finance, robotics, and social media.
• Hackathon Prototype: A rapid AI prototype designed and presented during the in-class hackathon, emphasizing creativity and teamwork.
• Certificates & Badges: Official recognition of course completion, along with optional digital badges such as GitHub project contributions, Kaggle profiles, and IBM Data Science certification.

Hands-on Activities

• Hands-On Labs: Guided coding sessions where students build AI models and Deploy them on Kaggle platform
• Simulations & Demos: Live demonstrations of AI in action (e.g., self-driving car simulations, financial prediction models, and real-time image recognition).
• Case Study Challenges: Team-based analysis of AI applications in healthcare, finance, robotics, and social media, culminating in short presentations.
• AI Hackathon: A fast-paced group challenge where students prototype an AI solution to a real-world problem.
• Guest Lectures: Talks from industry professionals and researchers (e.g., experts from Google, Toast, and Dartmouth CPHAI)

Guest Speakers

• Hareeshwar Karthikeyan, AI Engineer at Toast
• Ganesh Rohit N. , Software Engineer at Google
• Ansh Gupta , Founding AI Engineer at Anything

Field Trip 

DALI Lab at Dartmouth College

Benefits for Future Study

Students will leave this course with a strong foundation in artificial intelligence that can be applied to future academic and career pursuits. The skills gained—ranging from Python programming and machine learning fundamentals to hands-on experience with real-world AI applications—will prepare them for advanced coursework in computer science, engineering, quant finance, and related STEM fields. Beyond academics, students will understand how AI is shaping industries such as healthcare, finance, and robotics, giving them a competitive edge in research opportunities. This course also encourages critical thinking and innovation, empowering students to become future leaders in technology.

Course Description

What does it take to build a future in STEM? This two-week precollege program, led by Dr. Ansley Booker of Dartmouth NEXT, invites students to explore the wide range of careers and pathways in science, technology, engineering, and mathematics. Through engaging seminars, hands-on workshops, and behind-the-scenes field trips, students will experience Dartmouth’s cutting-edge labs, medical centers, sustainability initiatives, and makerspaces. Along the way, they’ll learn from Dartmouth faculty, researchers, and alumni who are pushing the boundaries of innovation in medicine, engineering, data science, and beyond.
The program goes beyond exposure—it provides a roadmap. Students will gain practical tools for career readiness, from understanding the steps toward graduate school or research opportunities to connecting with mentors and building a professional network. The experience culminates in a closing symposium where each student presents a personalized “STEM Futures Pathway Plan,” reflecting the insights and inspiration gathered throughout the program. By the end of the two weeks, participants will not only discover what’s possible in STEM but also envision their own next steps with clarity and confidence.

Learning Outcomes 

By the end of this course, students will be able to:

• Identify a wide range of STEM disciplines and career pathways through exposure to Dartmouth faculty, researchers, and alumni.
• Explain the steps involved in pursuing STEM careers, including higher education pathways, research opportunities, and professional development strategies.
• Engage with cutting-edge STEM research and innovation in fields such as medicine, engineering, sustainability, and data science.
• Develop foundational skills for career readiness, including networking, mentorship-seeking, and effective communication of personal goals.
• Reflect on their own interests and strengths to envision a personalized trajectory within STEM fields.
• Create a “STEM Futures Pathway Plan” that integrates academic exploration, career goals, and actionable next steps.

Tangible Outcomes

• Reflective Journaling: Capture daily insights and track evolving career interests, forming the foundation for the final project.
• Closing Symposium: Present a personalized “STEM Futures Pathway Plan” to peers and faculty, articulating both inspiration gained and concrete next steps.

Hands-On Activities 

To achieve these outcomes, students will:

• Seminar Sessions: Participate in interactive talks led by Dartmouth faculty, alumni, and guest experts on STEM careers, innovations, and emerging fields.
• Hands-On Workshops: Experiment in makerspaces, medical labs, and sustainability centers, engaging directly with tools and techniques used by STEM professionals.
• Field Trips & Site Visits: Go behind the scenes at Dartmouth labs, research centers, and innovation hubs to observe cutting-edge science in action.
• Career Pathway Panels: Hear from alumni and professionals who represent diverse trajectories in STEM, followed by Q&A networking opportunities.
• Mentorship Activities: Pair with Dartmouth graduate students or researchers for guided conversations about academic and career journeys.
• Skill-Building Sessions: Learn practical skills in resume building, science communication, and how to prepare for college-level research.