Award-Winning Computer Science Tutors
serving Tacoma, WA

Applied mathematics at UCLA means Maria writes code constantly — from implementing numerical methods to building optimization algorithms for economics models. She brings that practical, problem-solving mindset to computer science topics like data structures, algorithm analysis, and debugging logic errors in real programs.

As a dedicated tutor with over 2 years of experience, I am passionate about fostering a supportive learning environment where students can thrive in subjects like Algebra, Business Analytics, and Data Science. Currently pursuing my Bachelor's in Informatics at the University of Washington, I incorporate real-world applications into my teaching to engage students and enhance their understanding of complex concepts. My approach emphasizes personalized learning, encouraging students to ask questions and develop critical thinking skills. I find great joy in witnessing my students' growth and success, and I strive to instill a love for learning that extends beyond the classroom.

Intensely curious, I am interested in STEM subjects and the liberal arts. I tutor to help you reach your educational goals and because it's immensely gratifying to see my students succeed.

Studying Computer Science at the University of Washington means Antonia is immersed in algorithms, data structures, and computational thinking every day. She unpacks topics like sorting algorithms, Big-O analysis, and abstraction by connecting them to practical problems students can visualize. Her dual-language tutoring background also makes her particularly skilled at explaining technical jargon in plain, accessible terms.

I'm a tutor who loves the moment when a concept finally clickswhether that's solving a calculus problem or debugging a first Python script. With experience teaching math from elementary to college level and professional training in full-stack development, I help students build confidence alongside competence in both math and programming. At Mathnasium, I worked with groups of 4-6 students at different levels, learning to adapt quickly to different learning styles. I bring that same flexibility to teaching HTML, CSS, JavaScript, Python, and other programming languages. I don't just give answersI guide students to discover solutions themselves because that's what sticks. Whether it's explaining why a for-loop isn't working, walking through derivatives, preparing for the SAT, or building a first web page, I focus on understanding over memorization. I'm patient but direct, and I adjust my approach based on what's actually working for each student. My goal is simple: make learning feel achievable, not intimidating, and give students tools they can use long after our sessions end.

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.

Margaret studies Computer Science at Stanford alongside Political Science, giving her a broad perspective on how computational thinking applies beyond just writing code. She breaks down core topics like data structures, algorithms, and recursion by connecting each one to real problems students can visualize. Rated 4.8 by her 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.

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.

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.

From automata theory and computational complexity to practical algorithm design, Firas covers computer science as both a theoretical discipline and a hands-on craft. His Ph.D. research at the intersection of machine learning and big data means he can connect abstract CS concepts — graph traversals, runtime analysis, NP-completeness — to the systems that actually use them. Rated 5.0 by students.

Julia is a computational science major at Stanford who writes code daily and understands the gap between knowing syntax and thinking like a programmer. She digs into core concepts like recursion, data structures, and algorithm design by walking through problems step by step until the logic becomes intuitive. Students learning Python, Java, or tackling their first real debugging challenge get someone who can explain the *why* behind every line.

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.

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.

Michael earned his B.S. in Computer Science from UCLA, where he dug into everything from data structures and algorithms to software design principles. He breaks down abstract concepts like recursion, Big-O analysis, and object-oriented programming into concrete, step-by-step logic that clicks. He also teaches JavaScript, giving him a practical edge when students need to connect theory to actual code.