Award-Winning Computer Science Tutors
serving Canton, OH

Software development taught Michael something that textbooks often skip: the discipline of decomposing a massive, ambiguous problem into small, testable pieces — and that's exactly how he teaches computer science. His professional coding experience across languages like Java, Python, Ruby, and C means he can ground abstract topics like object-oriented design or control flow in real working code rather than classroom-only exercises. Rated 4.9 by students.

Between his engineering curriculum and competition math background, Kevin tackles computer science from both the theoretical and applied sides — algorithm complexity, recursion, sorting strategies, and how hardware constraints shape software decisions. He's especially effective at translating the mathematical foundations of CS into intuition students can actually use when writing and debugging code.

I'm a recent college graduate with degrees in Biological Sciences and Russian from Ohio University. During my time there, I tutored students in a variety of subjects, including biology, chemistry, and Spanish. In addition, I worked as both a peer advisor and teaching assistant, which gives me insight into the learning strategies and study skills that students need to succeed. As a tutor, I like to focus on doing actual problems with students because it is the most effective way to immediately identify their strengths and weaknesses and to address them. In my free time, I like to lift weights, read books, and spend time with my friends.

I am a recent graduate with a master's in electrical engineering from Case Western Reserve University. I won the Bill and Melinda Gates Millennium Scholarship which covers full tuition up to Ph.D. I was on the Dean's List for three consecutive years. Additionally, I won the OZY Media Genius Award in 2015 to work on high-temperature superconductors. I currently work as a Technology Analyst at Accenture. I am also seriously considering whether I should go for a Ph.D. or not.

Between his CS degree at Case Western Reserve and running a web design business, Brian writes code in both academic and real-world contexts every day. He unpacks foundational topics like data structures, sorting algorithms, and recursion by connecting them to practical applications — showing why a hash map outperforms a list search, or how recursion models real problems like file system traversal.

Studying computer science engineering at Ohio State with honors, Samuel digs into the discipline well beyond surface-level coding — think algorithm complexity, memory management, and how data structures like hash maps and trees actually work under the hood. He connects abstract CS theory to real implementations so the concepts stick long after the exam.

Two years writing computerized tasks for Ohio State's Clinical Neuroscience Laboratory taught Luke how to translate experimental logic into working code — structuring programs around precise conditions, variables, and outcomes. That lab experience, combined with his Java and AP Computer Science teaching, means he approaches programming as applied problem-solving rather than abstract syntax drills. Rated 4.8 by students.

I am passionate about the subject, they may feel that they can tap into that passion. Second, I like to engage the student personally. Education can sometimes feel transactional or even coercive. I want my student to feel that I am working for their benefit, and because I care about them, not because I'm getting paid.

I am currently pursuing my B.S. in microbiology as a pre-medical student with hopes to attend medical school.

I am passionate about teaching because I love helping someone understand and become confident in a concept that previously baffled them. I am a student at The Ohio State University working towards a Computer Science degree. I have experience tutoring math up through AP Calculus BC, French, and AP Literature. My experience in tutoring comes from the free weekly French tutoring program I initiated at my high school, which grew to me tutoring other subjects as well. My teaching philosophy involves discussion and helping the student come to an answer themselves, which allows the topic to stick. Because of this one of my favorite subjects to tutor is writing. Writing tutoring helps the student grow and allows them to see the problems in their writing themselves. Outside of academia I enjoy calligraphy and playing piano.

Three Bachelor of Science degrees — including Neuroscience — meant Anna was writing code long before she started teaching it, using Java, Python, and MATLAB to analyze data and build computational models across disciplines. That cross-field experience shapes how she teaches CS fundamentals: students don't just learn syntax, they learn to think about what a program needs to do before structuring it in any particular language. Rated 5.0 by students.

From recursion and sorting algorithms to Big-O analysis and object-oriented design, Ethan covers core computer science concepts with the rigor of someone studying it as a second major alongside aerospace engineering. He emphasizes understanding why an algorithm works — tracing through execution step by step — so students can adapt their thinking to new problems on exams and in projects.

As a computer science major at Boston University, Irene is immersed in the subject daily — from data structures and algorithms to object-oriented design. She breaks down abstract programming concepts into concrete steps, walking through how to trace logic, debug code, and think like a developer rather than just memorize syntax.

Between a master's in Computer Science, an active PhD program, and a full-time software development career, Daniel lives this subject from multiple angles — theoretical and applied. He unpacks everything from algorithmic complexity and data structures to systems-level thinking, tailoring the depth to wherever a student currently sits in their CS journey.

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.

Building AI systems and low-level software at Stanford — in both Python and C++ — Kevin knows where the theoretical meets the practical in computer science. His biocomputation specialization means he can explain not just how to implement an algorithm, but why certain computational approaches work better for different problem domains. Rated 5.0 by students.

Studying both chemical engineering and computer science at Cornell gives Jonathan an unusual angle on programming — he's constantly writing code to solve quantitative, real-world problems rather than just completing standalone assignments. That dual perspective makes him especially effective at teaching algorithmic thinking and Java or Python fundamentals, since he can show students how CS concepts like iteration and data manipulation actually get applied in technical fields outside of software development.

I'm trying to work on personal projects. I really enjoy snowboarding, and have been doing that since the third grade. I also enjoy playing sports and video games.

A Duke CS graduate now pursuing a Master's in Cybersecurity, Noah covers everything from foundational data structures and algorithms to systems-level concepts like memory management and network protocols. He breaks down abstract topics — recursion, Big-O analysis, object-oriented design — by connecting them to real problems rather than leaving them as textbook definitions. Students get someone who's still actively building in the field, not just teaching from notes.

Benjamin's finance and economics training at Notre Dame means he learned to code as a problem-solving tool — building models, analyzing datasets, and automating calculations — rather than through a traditional CS curriculum. That pragmatic entry point makes him effective at teaching programming logic and computational thinking to students who want to understand how code actually gets used in business and quantitative fields. Rated 5.0 by students.

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.

Justin's PhD research in computational mathematics meant writing code daily — building simulations, implementing algorithms, and debugging in MATLAB and other languages. He teaches computer science concepts like data structures, recursion, and algorithmic complexity by connecting them to real computational problems rather than treating them as abstract definitions to memorize.