Award-Winning Computer Science Tutors
serving Baltimore, MD

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.

Philosophy trained Calin to build rigorous logical arguments; his math and CS degrees taught him to express that logic in C++ and JavaScript. That combination means he teaches programming concepts — from algorithm design to data structure selection — as exercises in precise reasoning, where every line of code follows from a clear chain of thought. Rated 5.0 by students.

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.

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.

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.

From data structures and algorithms to computational complexity, Victoria covers the core CS concepts that show up in both coursework and technical interviews. Studying computer science and math simultaneously at WashU gave her a knack for explaining the mathematical reasoning behind topics like recursion, sorting efficiency, and graph traversal. She holds a 5.0 rating from 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.

Between his coursework at Rice and his background in algorithms, Daniel tackles computer science from both the practical and theoretical sides — writing clean code and understanding why one sorting algorithm outperforms another for a given dataset. He's especially strong at breaking down recursion, data structures, and algorithmic complexity into steps that build logically on each other.

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.

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.

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.

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.