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Investigate real datasets in this two-week data science and AI academy for high school students at Dartmouth.
Course Description
A dataset can contain a useful pattern, an unexpected exception, or a result that changes when you ask a different question. Learn how to investigate it and explain what the evidence supports.
You’ll develop programming skills for processing, analyzing, and visualizing real-world data. Computational thinking helps you break a problem into steps a program can carry out; inferential thinking connects an observation with a conclusion. Exploratory data analysis gives you ways to examine a dataset of your choice, compare patterns, and decide which questions deserve a closer look. Visualizations make the results easier to inspect and discuss.
Artificial intelligence adds methods for finding and using patterns. Explore elementary supervised and unsupervised learning, train and evaluate basic models, and examine how neural networks are structured. Ethical questions ask you to consider the implications of an AI system alongside its performance. For the final project, apply AI techniques to a data-driven problem. Bring your analysis, model work, and explanation together so someone else can follow the choices you made and the limitations you found.
This two-week full-day academy for high school students is part of Dartmouth Precollege Summer Scholars in Hanover, New Hampshire. Students focus on one academy for the full campus program. Beyond class, students connect focused academic work with campus life, growing independence, and clearer direction about what they may want to study or pursue next.
What Will You Do?
• Write scripts to process, analyze, and visualize data
• Investigate a dataset of your choice through exploratory data analysis
• Create visualizations that make patterns and questions easier to examine
• Apply elementary supervised and unsupervised learning techniques
• Train and evaluate basic AI models and examine neural-network structure
• Develop a final data-driven project applying AI techniques and ethical reasoning
Learning Outcomes
By the end of the course, students will be able to:
• Construct scripts and visualizations to investigate a real-world dataset
• Apply and evaluate elementary machine-learning techniques for a data problem
• Explain an AI project’s reasoning, limitations, and ethical implications
You’ll leave with your final data-driven project and a clearer sense of which questions in programming, data science, or AI you want to study further.
Why Data Science Skills Matter
Working with data requires students to connect a question with a method and an explanation. A visualization can reveal a pattern, but deciding what it means still requires careful reasoning. Writing a script makes each processing step explicit enough to examine and repeat.
Model evaluation introduces a similar discipline. Students compare what an AI system does with what the problem requires and consider where its conclusions may be limited. Ethical questions ask them to think about the implications of those choices. These habits support research, programming, and discussions where a numerical result needs a clear account of how it was produced.
Who Should Attend?
For students who have completed 9th, 10th, 11th, or 12th grade.
This academy may interest you if you want to investigate patterns, explore how AI models work, or connect programming with real questions. You’ll write scripts, examine datasets, create visualizations, and explain the choices behind a final project. Students should be ready to work through errors and revise an approach when an evaluation reveals a limitation. The full-day format gives the class time to focus on one academy, with discussion of both technical methods and the ethical implications of using them.
Data Science in Action
Data science appears in questions about health, the environment, business, and many other fields. A researcher might examine a pattern in observations, while a team using an AI model needs to assess how well it answers a particular question and what its limitations mean for the people affected.
The academy introduces those responsibilities through scripts, visualizations, model evaluation, and a final project. Students can continue exploring computer science, statistics, or a subject whose questions interest them. A useful next step begins with a question and a plan for finding evidence, rather than choosing a technique simply because it is available.
After successfully finishing this course, you will be awarded a certification completion for your accomplishment.
*This is a preview, not what you will receive