Award-Winning Computer Science Tutors
serving Scranton, PA

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.

Studying computer science at the University of Pittsburgh while working as a software engineer gives Timothy a dual perspective — he knows the theory behind data structures and algorithms and also knows which concepts actually matter on the job. He breaks down topics like recursion, Big-O analysis, and object-oriented design by tying them to problems students can visualize and build.

Studying AI at Penn State gave Lucas a deep understanding of computer science from the ground up — data structures, algorithms, object-oriented design, and how they all connect in real applications. He's taught students brand-new to coding as well as those debugging complex projects, and he tailors each session to the language and concepts at hand. Rated 4.9 by students.

Whether a student is writing their first loop or debugging a recursive function, Kevin connects new programming concepts to the logic underneath them so the syntax stops feeling arbitrary. His data science concentration means he's comfortable across languages and paradigms, and his experience teaching first-time coders in underserved communities sharpened his ability to explain abstract ideas without jargon.

From sorting algorithms to data structures to the big-picture question of how computers actually process information, Steven covers computer science at both the high school and college level. He programs in Java and C#, which means he can explain concepts like recursion or object-oriented design in whichever language a student's course uses. Rated 5.0 by students across his subjects.

Jared's CS background spans both the theoretical and the applied — his master's in computer science covered areas like machine learning and NLP, while his professional work as a software engineer means he writes production code in Java, C++, and Python daily. That range lets him teach everything from foundational data structures and algorithms to more advanced topics like relational database design with the perspective of someone who's actually built systems around these concepts. Rated 5.0 by students.

I am a current sophomore at Cornell University pursuing a B.S. in Biomedical Engineering. I have done extensive coursework in biology, physics, chemistry, math, and lab sciences. I love applying engineering problem-solving skills to the biological sciences. For the past year, I have been a teaching assistant for introductory biology classes. In my free time, I participate in cancer immunotherapy research which focuses on melanoma.

I am a rising sophomore at Harvard College, currently on leave for the semester. I am a B.A. candidate in mathematics and physics, and I have both professional and academic experience in computer science as well.

A strong computer science education isn't just about writing code — it's about thinking algorithmically, understanding data structures, and knowing when a brute-force solution won't scale. Sarah is pursuing her CS degree at Saint Vincent College and brings hands-on experience with languages like C++ and Python to break down topics from recursion to sorting algorithms.

Hello, my name is Paul, and I am passionate about helping students in the areas of computer science and math and making sure that no question goes unanswered! I can guarantee a safe environment where hard work and creativity will be the basis for success! Let's have some fun solving problems!

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.

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.

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.

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.

Madeline's physics PhD work at Carnegie Mellon means she writes code daily — Python, Java, MATLAB, and Mathematica — to model complex systems and crunch data, which is a very different entry point into computer science than a pure software track. That scientific computing background makes her especially effective at teaching programming logic, debugging strategies, and algorithmic thinking to students who need CS skills for STEM applications rather than just app development.

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 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.

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.

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.

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.

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.