Award-Winning Computer Science Tutors
serving Bridgeport, CT

I believe every person has potential, which must be drawn out of them. With over 4 years of leading workshops, being a Teaching Assistant and creating curriculum, and nearly two years of private tutoring, I know how to help students and people of all ages easily understand concepts in Science, Technology, Engineering, and Mathematics. Having graduated from Vanderbilt University with a Bachelors degree in Engineering Science, I know how to move from not understanding a subject to understanding it well. The subjects I tutor include: - Math (Algebra 1, Geometry, Algebra 2, Pre-Calculus**, Calculus 1**, Calculus 2** and Statistics**) - Computer Science (AP CSP / AP Comp SciA, Python, ReactJS/JavaScript, HTML/CSS, Powershell/Bash Scripting) - Science (Physics**) ** - tutor subject at high school/AP/college level My teaching style includes completely modeling a problem on the board. Then, I would guide a student through solving a problem before having them solve it independently.

Earning a certificate in Statistics and Machine Learning at Princeton gave Julie hands-on experience with core computer science concepts — algorithm design, data structures, and computational complexity. She approaches CS the way she approaches philosophy: by asking students to reason through *why* a solution works, not just whether it compiles.

Sakibul's graduate work at Rice sits at the intersection of computer science and applied mathematics, which means he tackles programming concepts — loops, recursion, data structures — with the analytical rigor of a mathematician. He breaks down abstract ideas like algorithmic complexity into concrete, step-by-step reasoning that clicks for students encountering CS for the first time.

From sorting algorithms to recursion to object-oriented architecture, computer science rewards the ability to think in layers of abstraction. Joel is pursuing CS at Cornell alongside physics, which means he approaches programming problems with both mathematical rigor and practical debugging instincts. He's comfortable across Python and Java and adapts to whatever language a student's course requires.

Studying computer science since before college and now pursuing a master's at UMass Amherst, Milo covers the full stack — from data structures and algorithms to systems-level concepts like memory management and concurrency. He spent three years in his university's tutoring center breaking down topics like recursion, sorting complexity, and graph traversal for students at every level.

Programming starts making sense when you stop memorizing syntax and start thinking about what the computer is actually doing step by step. June's electrical engineering background at Brown gives her insight into both the hardware and software sides — she can explain why an algorithm is efficient, not just how to write it. From loops and conditionals to data structures and recursion, she connects each concept to real projects she's built in robotics and hackathons.

From sorting algorithms and Big-O analysis to data structures like linked lists and binary trees, Rhamy covers the foundational CS concepts that show up in coursework and technical interviews alike. His computer engineering degree at Vanderbilt, paired with experience in multiple languages, lets him explain abstract ideas through concrete code. Rated 5.0 by students.

I am a undergraduate freshman of the University of Michigan, studying business at the Ross School of Business. Working together with students and having a good time while seeing steady improvements has proven to provide me great joy. I believe that communication and relationship building is crucial for students to open up about their struggles and also for me to identify problems they don't realize they can improve on, so this is a key aspect of all of my lessons. During my free time, I enjoy playing sports or snacking on desserts while binge-watching Friends!

From computer architecture and theory to parallel computing and machine learning, Brandon's master's coursework at RIT covers the full stack of computer science concepts. His two years of professional industry experience mean he can connect abstract topics — algorithm complexity, concurrency models, data structures — to how they actually show up in production code. Students rated him 4.9, which tracks with his ability to make dense CS material feel approachable.

Engineering science at Vanderbilt means Ethan writes code to solve real problems — simulations, data analysis, algorithm design — not just textbook exercises. He breaks down core concepts like recursion, data structures, and object-oriented design by connecting them to projects that actually do something interesting.

Pursuing both computer science and data science at NYU's Courant Institute with a cybersecurity minor, Diego lives in this material daily — from algorithms and data structures to networking and systems-level thinking. He breaks abstract CS concepts into smaller, buildable pieces so students can trace the logic themselves rather than just copying solutions.

Jeff tackles computer science by connecting abstract concepts like recursion, data structures, and algorithmic complexity to concrete problem-solving steps students can follow. His science background means he's comfortable with the mathematical logic underlying CS, from Boolean algebra to Big-O analysis.

Trained in computer science at UT Austin and currently pursuing a PhD that blends computational methods with social science, David brings both theoretical depth and applied versatility to CS instruction. He digs into core topics like algorithm analysis, data structures, and computational complexity, connecting them to the kind of real-world problem-solving that makes the discipline click.