Are you a high school student eager to dive into computer engineering and expand your technical skills? Free courses offer an excellent way to build foundational skills in areas like programming, digital systems, embedded computing, and even advanced topics like AI-driven hardware and cybersecurity. These courses offer the opportunity to gain hands-on experience, collaborate with other students, and learn from some of the most respected educators and engineers in the field.

Not only will you build practical, career-ready skills, but you’ll also strengthen core academic abilities like critical thinking, time management, and problem-solving — skills that will set you apart in college applications and beyond. Whether you’re curious about designing hardware, programming software, or the intersection of both, this list of 10 free computer engineering courses for high school students is designed to help you get started on your journey!

1. Circuits and Electronics 1: Basic Circuit Analysis - MIT

Location: Virtual

Application Deadline: Self-paced

Program Dates: 5 weeks; students typically dedicate 5–7 hours per week

Eligibility: High school students are eligible to apply

MITx: Circuits and Electronics 1: Basic Circuit Analysis is an introductory course that dives into fundamental circuit concepts essential to microchip design, supporting the technologies behind smartphones, computers, and self-driving cars. This free, self-paced online course is part of a three-part series offered by Professor Anant Agarwal, CEO of edX, and faculty from MIT, forming a cornerstone for students interested in Electrical Engineering and Computer Science.

The course covers key concepts such as resistive elements and networks, Kirchhoff’s Voltage and Current Laws, and node and mesh methods. Students also explore advanced techniques like superposition, Thevenin and Norton equivalency, and digital abstraction. In addition to theoretical lessons, participants engage in practical exercises involving digital logic gates, MOSFET transistors, and the building blocks of modern circuits. This course builds valuable problem-solving skills, along with the ability to design and analyze circuits from a foundational level.

2. Introduction to Engineering Simulations - Cornell University

Location: Virtual

Application Deadline: Self-paced

Program Dates: 6 weeks; students typically dedicate 4–6 hours per week

Eligibility: High school students are eligible to apply

Cornell University's A Hands-on Introduction to Engineering Simulations offers a comprehensive introduction to real-world engineering problem-solving through Ansys simulation software. This interactive, self-paced course is designed to give you a practical understanding of engineering simulations, helping you build skills in demand by employers.

The course emphasizes a problem-based learning approach, allowing you to actively engage with simulations for various physics scenarios, including structural mechanics, fluid dynamics, and heat transfer. You’ll learn foundational principles of finite-element analysis (FEA) and computational fluid dynamics (CFD) by solving textbook examples and then apply these concepts to model real-world systems like rocket assemblies and wind turbine rotors.  Participants gain hands-on experience with professional tools, as the course provides access to a free download of Ansys Student software. 

By the end of this course, you’ll understand the mathematical models behind simulations, gain expertise in checking simulations against calculations, and develop a structured approach to engineering analysis.

3. Data Science: Inference and Modeling - Harvard University

Location: Virtual

Application Deadline: Self-paced

Program Dates: 8 weeks; students typically dedicate 1–2 hours per week

Eligibility: High school students are eligible to apply

Harvard University's "Data Science: Inference and Modeling" is a foundational course for anyone interested in understanding and applying statistical methods crucial for data analysis, particularly in contexts where randomness plays a significant role. Through practical applications, such as election forecasting case studies, participants gain hands-on experience in applying inferential techniques to real-world data scenarios.

The curriculum covers concepts including parameter estimation, confidence intervals, and hypothesis testing. You will learn to construct and interpret statistical models, enabling you to make informed predictions and decisions based on data. The course also introduces Bayesian modeling, providing a framework for understanding probability statements about future events. 

4. Machine Learning with Python: Linear Models to Deep Learning - MIT

Location: Virtual

Application Deadline: Self-paced

Program Dates: 15 weeks; students typically dedicate 10–14 hours per week

Eligibility: High school students with proficiency in Python programming, probability theory, calculus, and linear algebra

Machine Learning with Python: From Linear Models to Deep Learning is an in-depth course offered by MIT as part of its MITx MicroMasters program in Statistics and Data Science, providing a rigorous curriculum that mirrors the pace and depth of on-campus MIT courses. This course equips you with the foundational principles of machine learning, covering topics such as classification, regression, and reinforcement learning through practical Python projects. Before enrolling, students should have proficiency in Python programming, probability theory, calculus, and linear algebra to meet the course prerequisites.

 

Throughout the program, you will learn to implement various algorithms, including linear models and neural networks, to make automated predictions based on training data. Ideal for those new to machine learning or looking to deepen their understanding, this course prepares you for advanced study in data science and offers experience with real-world applications.

5. Autonomous Cognitive Assistance (Cog*Works) - MIT Beaver Works Summer Institute (BWSI)

Location: Hybrid (Online and In-person at MIT)

Application Deadline: March 31, 2025 (Tentative)

Program Dates: Tentatively July 2025, with specific dates announced closer to spring. Virtual courses are open for interested students until November 

Eligibility: Applicants must be high school students in the U.S., currently in 11th grade or below, and must remain in the U.S. during the program

The Autonomous Cognitive Assistance (Cog*Works) course, offered by the MIT Beaver Works Summer Institute (BWSI), is an advanced course that immerses students in the practical applications of modern machine learning and data science. Established in 2017, this program employs project-based learning to explore machine learning across audio, vision, and language domains. Participants collaborate in small teams, utilizing tools like Git and Visual Studio Code to foster a highly collaborative environment.  

The curriculum is structured into three modules, each focusing on a specific application area. Students engage with foundational concepts in applied mathematics, science, and machine learning through compelling capstone projects with real-world relevance. Through modules on Python, natural language processing, neural networks, and machine cognition, you build your own cognitive systems using professional-grade tools like Amazon Alexa.  The program culminates in a four-week virtual summer session where students compete by developing and deploying their cognitive assistants in real-world scenarios. Students who well in the pre-requisite online courses become eligible to attend the four-week BWSI at MIT. 

6. Embedded Systems - Shape The World: Multi-Threaded Interfacing - UT Austin

Location: Virtual

Application Deadline: Self-paced

Program Dates: 8 weeks; students typically dedicate 8–10 hours per week

Eligibility: High school students are eligible to apply

This is a hands-on, lab-driven course that teaches students to design real-time embedded systems through a bottom-up approach. The course covers everything from basic interfacing with LEDs and switches to advanced multi-threaded systems and audio processing. You will work with the Texas Instruments TM4C123 microcontroller, learning practical skills in C programming, circuit design, and modular system building. 

Key projects include creating an audio player, a data acquisition system, and even an arcade-style game, with labs simulating real-world challenges. The course aims to provide participants with the foundational knowledge necessary to develop Internet of Things (IoT) devices and control systems that integrate sensors, motors, and displays, making this course ideal for those interested in applied computer engineering.

7. Coursera’s Applied Software Engineering Fundamentals Specialization

Location: Virtual

Application Deadline: Self-paced

Program Dates: Approximately 2 months, with an estimated 10 hours of study per week

Eligibility: High school students are eligible to apply

IBM's "Applied Software Engineering Fundamentals Specialization" is a comprehensive program designed to introduce learners to the core principles and practices of software engineering. This specialization encompasses a series of courses that cover essential topics such as version control, software development life cycle (SDLC), and deployment, gaining hands-on experience with Git, GitHub, Linux, and Python. 

Throughout the program, learners engage in projects that reinforce their understanding of software engineering concepts. These projects involve creating and managing GitHub repositories, implementing branching strategies using Git commands, and executing common Linux commands. The Python modules guide students through the process of developing, packaging, and deploying applications, utilizing libraries, APIs, and web services.

The course’s practical, project-oriented approach offers valuable experience, especially for students interested in software development careers or further study in software engineering. Applications are open to high school students with a background in math and science, providing a unique stepping stone for those exploring careers in technology and development.

8. HarvardX: CS50's Introduction to Computer Science

Location: Virtual

Application Deadline: Self-paced

Program Dates: Approximately 12 weeks, with an estimated 6–18 hours of study per week

Eligibility: High school students are eligible to apply.

Harvard University’s CS50's Introduction to Computer Science (CS50x) is a highly-regarded foundational course that provides students of all backgrounds with a comprehensive introduction to computer science and programming. Taught by Professor David J. Malan, CS50x covers a wide array of topics, including abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. The course utilizes languages such as C, Python, SQL, and JavaScript, along with CSS and HTML, providing students with a robust foundation in programming. 

Designed for both majors and non-majors, with or without prior programming experience, CS50x emphasizes algorithmic thinking and efficient problem-solving. The self-paced course allows students to complete nine problem sets and a final project, with the problem sets inspired by real-world domains like biology, cryptography, finance, forensics, and gaming. Harvard’s CS50 on-campus version is the university’s largest course, reflecting its popularity and quality. For more details, you can check out CS50x here!

9. Fundamentals of Computing Specialization - Rice University

Location: Virtual

Application Deadline: Self-paced

Program Dates: Approximately 2 months, with an estimated 10 hours of study per week

Eligibility: High school students are eligible to apply.

Rice University's "Fundamentals of Computing Specialization" is a comprehensive seven-course series designed to provide learners with a solid foundation in computer science and programming. This specialization covers a wide range of topics, including Python programming, algorithmic thinking, and the principles of computing, mirroring much of the material taught to first-year computer science students at Rice.

You will engage in over 20 hands-on projects that solidify your understanding of key concepts. These projects range from building simple interactive applications to tackling more complex computational problems. The specialization culminates in a capstone exam that allows you to demonstrate your acquired knowledge and skills, making it an ideal choice for high school students interested in deepening their computer science expertise. For more details, you can explore the specialization on Coursera!

10. Introduction to Computational Thinking and Data Science - MIT

Location: Virtual

Application Deadline: Self-paced

Program Dates: One semester; students typically dedicate 2 hours per week

Eligibility: Open to high school students with basic programming knowledge or completion of MIT’s introductory course, 6.0001.

MIT’s Introduction to Computational Thinking and Data Science is designed for students interested in how computation can address complex problems across various fields. Building upon foundational programming knowledge, this course delves into topics such as optimization, stochastic processes, and machine learning, emphasizing practical applications and data-driven problem-solving. Using Python as the primary language, students learn to build small, purposeful programs, enabling them to tackle real-world scenarios.

The course is structured around two weekly lectures and explores critical concepts such as stochastic thinking, Monte Carlo simulations, data sampling, and confidence intervals. Later sections introduce machine learning techniques like clustering and classification, encouraging you to engage with hands-on programming projects and practical applications. By the end of the course, participants will have developed a robust understanding of computational techniques and their applications in data science, preparing them for advanced studies or careers in fields that require strong analytical and programming skills. 

If you’re looking to build a project/research paper in the field of AI & ML, consider applying to Veritas AI! 

Veritas AI is founded by Harvard graduate students. Through the programs, you get a chance to work 1:1 with mentors from universities like Harvard, Stanford, MIT, and more to create unique, personalized projects. In the past year, we had over 1000 students learn AI & ML with us. You can apply here!

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