High School, Computer Science

Computer Science and computational problem solving are fundamental skills for engaging the 21st-century marketplace of ideas and economies.  All students should have the opportunity to learn these skills as they will use them in whatever career they are likely to enter. This is one of the reasons NYS has made computer science a graduation requirement.

There are several units of study in computer science essentials gradually building students up to a level of comfort that they too can create and develop sophisticated computing innovations and computational artifacts. The topics include: Your Digital Life, Computer Science For All, Introduction to Web Development, Introduction to App Development, Graphics & Animation with Python and Data Science, Artificial Intelligence and Machine Learning.

This course is equivalent to a semester-long, college-level course in computer science. The course continues to teach students about computer science focused around seven big ideas: creativity, abstraction, data & information, algorithms, programming, the Internet and global impact. The course will use MIT App Inventor and the Blockly programming language to teach students about programming concepts in the context of mobile application development. The AP Computer Science Principles course includes a performance based task where students creatively design their own unique programming app. Collaboration will also be a key component in the class. AP CSP is designed to be a prerequisite for AP CS A and/or Mobile Apps & Entrepreneurship IS. 

College Python Programming is equivalent to a first-semester, college-level course in programming. The course introduces students to coding essentials including problem solving and program design, algorithms (sequence, selection/decisions, iteration/loops), data collection (lists, sets, dictionaries and scalar values), abstractions (procedures, functions), graphical user interfaces and user experience design. This is a project-based learning course where Python applications will be created and explored within a backdrop of traditional problems and more current computer science fields such as data visualization, machine learning, web scraping and integration with engineering projects. Collaboration will also be a key component in the class. Students may opt to earn college credit through LIU upon successful completion of this course.

This course introduces students to the main ideas in Data Science (DS), and Artificial Intelligence (AI) through project-based learning. Students will learn to ask questions of data and represent data through visualizations. They will also use critical thinking skills to look at how data is presented to them or used in articles and social media. The projects will range from exploring how AI is used in image recognition or price predictions, to how Spotify creates a shuffle list of their favorite song list. The course will cover the technical side of DS and AI, where students will be introduced to software used in the industry: Python, Pandas, scikit-learn, Colab Notebooks. In addition, the course will examine the implications of DS and AI including Data Ethics, Data Privacy, and how AI impacts all areas of our life. 

This course is equivalent to a semester-long, college-level course in computer science. The course introduces students to computer science with fundamental topics that include problem solving, design strategies and methodologies, organization of data (data structures), approaches to processing data (algorithms), analysis of potential solutions, and the ethical and social implications of computing. The course emphasizes both object-oriented and imperative problem solving and design using Java language. The AP Computer Science A course includes a minimum of 20 hours of hands-on structured lab experiences to engage students in individual or group problem solving. Prerequisite: AP Computer Science Principles. Teacher recommendation and administrative approval are required for enrollment.

In this Machine Learning class (ML) students will take a deep dive into the world of AI and Machine Learning and will understand the “magic” that’s behind chatGPT, image recognition, and more. Students will work in Python and will be introduced to various types of machine learning - supervised ML, unsupervised ML, and reinforcement learning. By the end of the class students will have a portfolio of projects that will include Regression Models, Classification Models, neural networks, Sentiment analysis, Clustering, and Natural Language Processing.