Award-Winning Computer Science Tutors
serving Concord, NC

Florence doesn't just study computer science at Duke — she teaches it, having served as a TA for Intro to Databases and Computer Network Architecture while also interning in software development at IBM. That combination of academic depth and industry experience means she can explain everything from relational algebra to TCP/IP networking with concrete, real-world context. Rated 5.0 by students.

Studying computer science at Duke means Susie is immersed in everything from algorithm analysis to systems design right now, which keeps her explanations current and grounded. She's especially sharp at connecting theoretical ideas like Big-O notation and recursion to the practical code students actually write. Co-founding a tutoring club in high school gave her early practice at making tough material click.

Studying computer science at UNC Chapel Hill means Jack lives in the material — algorithms, data structures, operating systems, and computational theory aren't abstract topics for him but daily coursework. He breaks down concepts like recursion, Big-O analysis, and memory management into intuitive explanations that stick.

Tackling a computer science minor alongside mechanical engineering at Duke, Shaan lives at the intersection of hardware thinking and software logic. He teaches core CS concepts — algorithm complexity, recursion, data structures, and object-oriented design — by connecting abstract ideas to concrete implementations, which is especially useful for students encountering Big-O notation or sorting algorithms for the first time.

Understanding computer science means thinking algorithmically: knowing not just that a sorting algorithm works, but why merge sort outperforms bubble sort at scale and how to analyze that difference with Big-O notation. Shourya codes in Python, C++, and MATLAB, which lets him adapt explanations to whichever language a student's course uses while keeping the focus on the underlying logic of data structures and algorithms.

I graduated from the University of North Carolina at Chapel Hill with a Bachelor of Science in Physics. I work with students in a variety of subjects, including Math, Physics, and Chemistry. I have experience working with students on the Autism spectrum and with ADHD.

I am a graduate student at George Mason University studying Computer Science. I also graduated at Vanderbilt University, with Bachelors Degrees in Computer Science, Mathematics, and Philosophy. When a student does not understand a topic or concept, I believe the best way to teach it is to meet students where they are at, providing individually catered feedback, and exploring the aspects of a student's weaknesses, finding a way to explain the concept in a way that resonates with and makes sense to the student. I am most familiar with tutoring for the SAT, where I have generally increased scores by 120-300 points.

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.

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.

Studying computer science at Cornell, Eric tackles everything from algorithm analysis and Big-O notation to systems-level concepts like memory management and recursion. He breaks down problems the way a CS student actually encounters them — whiteboarding a solution, tracing through edge cases, then translating logic into clean code. His 5.0 rating speaks to how clearly that approach lands.

Corrina's mechanical engineering degree required extensive programming coursework, and she now teaches core computer science concepts — data structures, algorithms, Boolean logic, and computational thinking — in a way that makes abstract ideas tangible. She connects each concept to real applications, whether that's sorting algorithms in a search engine or conditionals inside a robot's control loop.

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.

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.

A Stanford MS in Computer Science means David can teach everything from data structures and algorithms to object-oriented design with the depth that comes from building real systems — not just reading about them. He spent a summer teaching web and app development to high school students in Palestine, so he knows how to make abstract CS concepts click through hands-on projects.

From data structures and algorithm analysis to the fundamentals of how operating systems and networks function, Nicholas covers computer science with the depth his Penn State CS degree provided. He's especially strong at explaining recursion, sorting algorithms, and Big-O notation — the concepts that separate students who can code from students who truly understand computation. Rated 5.0 by students.

Allison's CS degree from Dartmouth means she's worked through the full arc — from writing first programs to tackling data structures, algorithms, and computational theory. She unpacks abstract concepts like recursion and Big-O analysis by walking through concrete code examples, making the logic visible before the notation takes over.

As an engineering student at Brown, Kashish works with programming languages and computational thinking daily — from writing algorithms to debugging code and understanding data structures like arrays, linked lists, and trees. She breaks down abstract CS concepts by connecting them to concrete problems, making topics like recursion or object-oriented design click for students encountering them for the first time. Her analytical precision and 5.0 rating make her a strong fit for introductory through intermediate computer science.

Ryan is a computer science major at Cornell, which means he's actively working through the same core curriculum — algorithms, data structures, computational complexity — that college CS students encounter. He explains concepts like recursion, Big-O analysis, and graph traversal by tracing through concrete examples rather than relying on abstract definitions. Rated 4.8 across his sessions.

John transitioned from law into co-founding a software company, which meant teaching himself to think in algorithms, data structures, and system design under real deadlines. He approaches computer science the same way — breaking problems into smaller, solvable pieces before writing a single line of code. That builder's mindset makes debugging and logic design feel less intimidating.