Award-Winning Computer Science Tutors
serving Miami, FL

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.

I am working towards a Bachelor of Arts in Pure and Applied Mathematics as well as a Bachelor of Arts in Astronomy and Physics. I have enjoyed studying math and science since I was in elementary school. I would always help my friends out by answering their questions about the material. For about the last five years, I have had my own tutoring business where I have tutored a wide variety of math courses from elementary school math to pre-calculus and calculus. I like to make sure my students have a complete understanding of the core concepts before going into practice questions. I have also had experience helping my peers with physics and computer science courses.

I am currently a Harvard student majoring in Computer Science with a minor in Applied Mathematics. I graduated Class Valedictorian in high school and was named National Merit Finalist. I took 16 AP classes in high school, including AP Calculus AB, AP Calculus BC, AP Computer Science A, AP Physics C : Mechanics and AP Physics 1, with a score of 5 in all of the tests. I scored a 1570/1600 in my SAT and 800 in the SAT Math Level 2 Subject Test and 790 in the SAT Physics Subject Test.

I am graduated from Penn State University in Industrial Engineering in 2017. I've tutored ever since I was in high school, and I love helping people! I like to help my students understand math (and other topics) instead of just doing it blindly. My goal is to help my students improve their math (and other topics) and build skills that will help them find learning easier in the future! Fun fact, I used to work for Disney and I like to salsa dance!

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.

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.

Pursuing a computer engineering degree at Georgia Tech means Yash lives in the overlap between hardware and software — algorithms, data structures, computational logic, and systems design are daily coursework. He's especially strong at explaining foundational concepts like Big-O analysis, sorting algorithms, and binary representation in ways that click for students seeing them for the first time.

Currently pursuing his B.S. in Computer Science at Florida Institute of Technology, Samuel digs into everything from data structures and algorithms to object-oriented design with the fluency of someone actively building projects in these areas. His 5.0 rating speaks to his ability to make abstract concepts like recursion and memory management click for learners at any level.

Pursuing a dual degree in computer science and studio art — with a math minor on top — Parker approaches coding problems with an unusual creative flexibility, treating program design as something closer to iterative sketching than rigid formula-following. That mindset is especially useful when teaching students to architect solutions from scratch, since real programming requires the same willingness to prototype, rethink, and restructure that drives any creative process. Holds a 5.0 rating.

Interdisciplinary studies forced Harrison to think computationally across fields — modeling biological systems, analyzing experimental datasets, automating lab workflows. He brings that applied perspective to computer science topics like algorithm design, data structures, and Big-O analysis, grounding abstract concepts in problems that actually matter outside a textbook.

There's a gap between watching a coding tutorial and actually solving problems independently, and that's exactly where Sebastian operates. As a CS undergrad at UCF, he explains foundational concepts like loops, conditionals, data structures, and algorithmic thinking by building small programs alongside students rather than lecturing through slides. His 4.8 student rating reflects sessions that are hands-on and specific to each learner's sticking points.

Big-O analysis, recursion, sorting algorithms, graph traversal — computer science is really about learning to think about efficiency and trade-offs. Nathan studies CS at UCF and digs into these core concepts daily, which means he can explain not just how an algorithm works but why one approach outperforms another on a given input. He holds a 4.9 rating from past students.

Learning to think computationally — breaking a big problem into smaller, solvable pieces — matters more than any single programming language. Hassan unpacks foundational topics like loops, conditionals, data structures, and algorithm efficiency in a way that transfers whether a student is writing Python, Java, or pseudocode. His ongoing CS studies at UCF keep his explanations grounded in current practice.

Currently pursuing a Computer Science degree at UCF, Gabriel teaches programming concepts like loops, conditionals, and data structures by connecting them to projects students actually want to build. He breaks down abstract logic into step-by-step pseudocode before ever touching a keyboard, which makes debugging feel less overwhelming.

I am Ankit Jajoo. I am enrolled in Duke University Class of 2022 studying neuroscience and computer science. STEM is my number one passion, while history is my number one interest. Humanities and STEM not only coexist, but work together often in life. While STEM provides the foundation for the future, humanities contributes to how it is used to advance humanity as a whole. I love biology, chemistry, and computers. It is so cool to see how all these tiny minute parts of the world interact to create you, me, and everything we touch. On the other hand, history is cool to see how societies interacted to create the present world. The past is never separate from us, but always influencing everything from the various countries to cities to the tiny villages all over the world. The world is a combination of all of this and it is my passion to understand the world. Other than just about education and information, I love teaching other people about stuff. I enjoy teaching all my friends some stuff, in return they teach me other stuff. Contrary to popular opinion, teaching is a two way street. I've learned a tremendous amount from students and I hope they learned a similar amount from me. Tutoring students in a field I enjoy such as STEM or history is a dream come true.

I am listening to and learning about him or her as an individual. I can also discover what motivates the student during this conversation and plan for how to frame future tutoring sessions in terms of what the student already knows and enjoys.

I am currently an undergraduate student at Vanderbilt University. I am majoring in Biological Sciences and love helping others learn about STEM too! I have been a tutor since I was 12 years old and have tutored almost every age group in every subject.

I am a game designer, and tutor. I love teaching topics related to my skillset as well as other topics that I often use within my job. Very good with MATH as a passion. Helped so many students in Math

As a passionate educator with a Master's degree in Computer Science from Florida Atlantic University, I am dedicated to fostering a deep understanding of computational concepts in my students. With over 2 years of experience tutoring AP Computer Science A, AP Computer Science Principles, Python, and Java, I adopt a hands-on approach that emphasizes real-world problem-solving. My teaching philosophy revolves around creating a supportive environment where students feel encouraged to ask questions and actively engage with the material. I am motivated by the belief that every student has the potential to excel in technology, and I strive to guide them on their academic journeys. In my free time, I enjoy developing personal coding projects and exploring the latest technological advancements, sharing these insights with my students to spark their curiosity and enhance their learning experience. I am also a Software Engineer, and happy to discuss career development. Excited to work with you!

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.

Elizabeth's computer science degree and professional software development career mean she teaches CS concepts the way they're actually used — not just as textbook definitions. Whether it's understanding recursion, tracing through data structures, or reasoning about algorithmic efficiency, she connects abstract ideas to real code she's written on the job.

Pursuing a CS master's at Penn while TAing discrete math means Keenan lives in both the theoretical and practical sides of computer science every day. He unpacks core topics like algorithm complexity, data structure tradeoffs, and computational logic in a way that connects abstract ideas to real code. Rated 5.0 across all 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.

Biomedical engineering at Rice requires heavy computational coursework, so Theresa has tackled core computer science concepts — from object-oriented programming and data structures to algorithm complexity — in the context of solving real problems. She explains abstract ideas like recursion and sorting algorithms by connecting them to concrete examples rather than letting students drown in theory. Rated 5.0 by students.

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.

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.

Isabella TA'd multiple computer science courses at MIT, so she's seen exactly where students get stuck — whether it's tracing recursive calls, understanding how data structures like linked lists and trees actually work in memory, or debugging logic errors in their code. She explains the underlying concepts so that writing correct programs becomes intuitive rather than trial-and-error. Rated 5.0 by students.

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.

From data structures and algorithms to computational complexity, Michelle covers the core CS curriculum with the depth you'd expect from a Duke CS graduate heading into a PhD at Michigan. She's especially strong at explaining abstract concepts like recursion and graph traversal by connecting them to concrete, visual examples that make the logic intuitive.

From data representation and Boolean logic to algorithm design and computational thinking, Jack covers the full spread of computer science fundamentals as a CS major at the University of Chicago. He's the person his peers turn to when they're stuck on a problem set, which means he's constantly practicing the skill of explaining abstract concepts — recursion, runtime analysis, data abstraction — in plain language.

Holding both a B.S. in Computer Science from the University of Kentucky and a game development master's in progress at SCAD, Evan covers the full stack of CS fundamentals: data structures, algorithm analysis, object-oriented design, and software architecture. He connects abstract concepts like Big-O complexity or recursion to concrete implementations in C, C++, and Java so the theory actually sticks.