Award-Winning Computer Science Tutors
serving Scottsdale, AZ

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.

I am a graduate student currently working on my Master's Degree in Big Data Systems.

From sorting algorithms and Big-O analysis to data structures like trees and hash maps, computer science is ultimately about choosing the right tool for a given problem. Mehek earned her degree in computer and information sciences and teaches across multiple programming languages, which means she can explain concepts at the theoretical level without losing sight of implementation. She's rated 4.7 by students.

Geoffrey's electrical engineering coursework at ASU means he lives in code daily — from writing embedded C for microcontrollers to building data structures in Python. He tackles computer science as applied problem-solving, connecting abstract concepts like recursion and algorithmic complexity to tangible projects students can see working.

From sorting algorithms and Big-O analysis to database design and operating system fundamentals, computer science covers enormous ground. Ajay brings both an electrical engineering degree and hands-on software engineering experience to the table, which means he can explain not just how an algorithm works in pseudocode but what's actually happening at the hardware level when it runs.

Spencer approaches computer science the way he approaches any complex system: by breaking it into smaller, logical components and understanding how they connect. His analytical training across international relations, business, and quantitative coursework at BYU and Thunderbird gives him a structured problem-solving mindset that translates well to topics like algorithms, data structures, and programming logic. He's especially effective with students who think conceptually but struggle to translate ideas into code.

Concepts like recursion, data structures, and algorithm complexity click faster when someone can show you why a linked list matters, not just how to implement one. Ayushi studied computer science through her master's at Arizona State and spent time tutoring CS students at the university's engineering tutoring center, so she's seen exactly where beginners and intermediate students get stuck. She unpacks abstract ideas with concrete examples and traces through code step by step.

I'm excited to start sharing my love of learning with everyone else.

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.

From basic programming constructs all the way up to computational analysis and finite automata, Andrew has tutored the full range of computer science topics at both the high school and college level. His CS degree gives him the depth to explain not just how an algorithm works but why its time complexity matters. Rated 5.0 by students.

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.

Most CS tutors come from pure software backgrounds — Clive's path runs through economics at Brown, where he picked up Java, Python, JavaScript, SQL, and HTML as tools for data analysis and building real projects rather than just completing problem sets. That applied angle makes him especially effective at teaching programming fundamentals and web technologies to students who learn better when code solves a tangible problem.

Learning to code is really learning to decompose problems — figuring out what a program needs to do before writing a single line. Nat is double-majoring in computer science at Vanderbilt and unpacks core topics like loops, conditionals, data structures, and algorithm design in ways that build genuine understanding. Whether a student is writing their first Python script or debugging recursive functions, he connects each concept to the logic behind it.

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.

Eric treats coding problems the same way he treats logical puzzles — by breaking them apart, finding the pattern, and building a solution step by step. As a CS major at Washington University in St. Louis, he's deep in Java and JavaScript right now, which means he can walk students through everything from writing their first function to structuring a full object-oriented program. His approach emphasizes learning to think through problems algorithmically before jumping to syntax.

Studying Computer Science alongside Math/Stats at Carleton College, Thomas lives at the intersection of algorithms, data structures, and mathematical reasoning. He digs into topics like recursion, sorting algorithms, and object-oriented design by building understanding from first principles rather than rote code memorization.

Studying computer science at MIT, Brice digs into everything from data structures and algorithms to systems-level thinking with students at any stage. He's tutored over 30 students in the past year alone, tackling topics like recursion, object-oriented design, and algorithmic complexity. Rated 4.9 by students.

Between his AP Computer Science 5 and his engineering coursework at Vanderbilt, William has written code across contexts — from introductory Java to computational modeling in his chemical engineering classes. He breaks down abstract concepts like recursion, data structures, and algorithm efficiency by walking through concrete examples line by line. Students who can follow the logic but freeze when writing code from a blank screen tend to gain traction quickly with his approach.

A computer science degree paired with coursework in theoretical mathematics gives Lance an unusually strong grasp of the abstractions underneath the code — automata theory, algorithm complexity, data structures, and computational logic. He spent multiple semesters as a TA and independent instructor for CS courses, so he's seen firsthand which concepts trip students up and how to reframe them. Whether the topic is graph traversal or NP-completeness, he breaks it into pieces that actually make sense.

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.