Award-Winning Computer Science Tutors
serving Peoria, IL

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.

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.

A doctorate that spans both mathematics and computer science gives Irene unusual depth across the theoretical side of CS — from graph theory and combinatorics to algorithm analysis and discrete structures. She unpacks abstract concepts like Big-O notation and recursion by grounding them in the mathematical reasoning underneath.

Currently pursuing a Master's in Computer Science at DePaul, Ignacio teaches core CS concepts — data structures, algorithms, and object-oriented design — with the perspective of someone actively working through advanced coursework. His environmental science background also means he can connect abstract computing ideas to real-world applications like data modeling and simulation.

Currently pursuing a Computer Science degree, Michael digs into the concepts that trip students up most — recursion, data structures like linked lists and trees, and algorithm efficiency using Big-O notation. He codes across multiple languages including Java, C++, and JavaScript, so he can adapt explanations to whatever language a student's course uses.

From Big-O analysis to recursion to finite automata, computer science is really applied mathematical reasoning — and that's exactly where Joseph's strengths converge. His math degree at the University of Chicago gives him a rigorous foundation in the proofs and logic that underpin CS theory, while his programming experience across multiple languages keeps the practical side grounded.

Though English and writing are Eliza's strongest suits, her coursework in AP Computer Science Principles and coding gives her a solid handle on the conceptual side of CS — how data is represented, how algorithms are designed, and how the internet actually works. She approaches these topics the way she approaches essay structure: by teaching students to organize their thinking clearly before executing, which translates surprisingly well to debugging and program design.

From data structures and algorithm complexity to the theory behind how compilers and operating systems work, Devanshi covers computer science with the depth her own BS program demanded. She's particularly sharp on discrete math foundations — graph theory, recursion, Big-O analysis — that underpin so much of upper-level coursework. Rated 5.0 by students.

Holding a bachelor's degree in computer science, Charles digs into everything from introductory programming logic to data structures and algorithm design. He emphasizes understanding why a particular approach works — tracing through loops, recursion, and memory allocation step by step — so students can debug their own thinking, not just their code.

From data structures and algorithm efficiency to the basics of how operating systems manage memory, computer science covers a huge range of concepts that build on each other fast. Daniel is pursuing his CIS degree right now, which means the material is fresh and he knows exactly where students tend to hit walls — particularly around recursion, sorting algorithms, and Big-O analysis.

From data structures and algorithms to object-oriented design, Muntaser covers computer science fundamentals with the perspective of someone who uses them daily as a software engineer. His two years as a university-level Java TA sharpened his ability to explain concepts like recursion, linked lists, and sorting in ways that click on the first pass rather than the fifth.

Matt's computer science degree means he can walk through everything from basic control flow and data structures to algorithm analysis and object-oriented design. He teaches programming the way he learned it — by building things, breaking them, and understanding why the fix works.

From recursion and sorting algorithms to data structures like trees and hash maps, Nicholas tackles computer science as a discipline built on logical reasoning — the same reasoning his physics degree drilled into him. He walks students through not just how an algorithm works but how to derive its time complexity and recognize which approach fits a given problem.

Debugging a program you wrote at 2 a.m. teaches you things a lecture never will — and Shivam's CS degree means he's been through that cycle hundreds of times. He unpacks core topics like data structures, algorithm complexity, and object-oriented design by writing and breaking real code alongside students.

Blake's computer science minor covers data structures, algorithms, and systems-level thinking, and his professional web development work means he applies those concepts daily. He unpacks topics like recursion, sorting efficiency, and memory management by tying abstract ideas to code students can actually run and modify. Rated 4.9 by students.

I'm a former Stanford grad, where I majored in Math and History. I've also spent a few years working as a software developer.

I am currently studying Computer Engineering at the University of Illinois at Urbana-Champaign. I have had experience tutoring students of all ages through programs at my school and the local library. I also teach private percussion lessons to help students prepare for auditions and concerts. While I tutor many subjects, I especially enjoy helping students with math, physics, computer science, and music. In my free time, I enjoy playing percussion (much to my neighbors dismay) and tinkering with robotics. I also enjoyed scouting as a kid and still love hiking and exploring the outdoors.

I am a Molecular Engineering major at the University of Chicago, I am currently taking time off to focus on other aspects of my career but I don't want to stop tutoring outside college campus!. I am a child of immigrants and have spent my life tutoring my siblings and younger students, and I loved working with them! See y'all in class!

I am a software programmer who specializes in Python applications and some web design. I have a deep understanding of mathematics and how computers work, but software development also allows room for creativity and exploration of an endless supply of tools available online. I like to think I bring this versatility to every aspect of life, including tutoring. Another example of this is my affinity for realistic drawing; by using proportions and geometry to analyze a scene as well as incorporating different techniques,

As a Ph.D. student in Computational and Applied Mathematics at the University of Chicago, with a Bachelor's in Mathematics from MIT, I am passionate about making mathematics accessible and engaging for all learners. My experience as a teaching assistant for calculus courses has honed my ability to design effective problem sets and lead dynamic recitations, while also providing personalized support through office hours and one-on-one tutoring. I specialize in subjects such as Algebra I & II, AP Calculus, Differential Equations, and Linear Algebra. I strive to create a supportive learning environment where students feel comfortable asking questions and exploring concepts. My teaching philosophy centers on patience, encouragement, and tailored lessons that align with each student's unique goals and learning style. I am dedicated to helping students build both their mathematical skills and their confidence, ensuring they can tackle challenges with a sense of accomplishment.

I am a second-year student at the University of Michigan-Ann Arbor and I am in the College of Engineering. I am not exactly sure as to what I want to do after College, but I do know that I would like to do something that involves Physics, Chemistry, and Computer Science. In High School, I would teach/tutor elementary school students at a tutoring center in Chicago called Ivy Garden. I tutor many High School Math and Science subjects (including AP courses) as well as Math and Science test prep for the ACT. My favorite subjects to tutor are Calculus and Physics because they are applicable with many things in your daily life. I am inspired to tutor those who are struggling in Math and Science courses because people are very discouraged while studying these subjects and I feel that, with the proper instruction, not only will people get better at those subjects; but they will become more confident in themselves. I believe that understanding something conceptually is ten times more valuable than knowing how to use an equation and I like using real world examples when I am tutoring. On my free time I enjoy playing NBA 2K and actual basketball.

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.

Sakibul's graduate work at Rice sits at the intersection of computer science and applied mathematics, which means he tackles programming concepts — loops, recursion, data structures — with the analytical rigor of a mathematician. He breaks down abstract ideas like algorithmic complexity into concrete, step-by-step reasoning that clicks for students encountering CS for the first time.

Jeff tackles computer science by connecting abstract concepts like recursion, data structures, and algorithmic complexity to concrete problem-solving steps students can follow. His science background means he's comfortable with the mathematical logic underlying CS, from Boolean algebra to Big-O analysis.

Software development taught Michael something that textbooks often skip: the discipline of decomposing a massive, ambiguous problem into small, testable pieces — and that's exactly how he teaches computer science. His professional coding experience across languages like Java, Python, Ruby, and C means he can ground abstract topics like object-oriented design or control flow in real working code rather than classroom-only exercises. Rated 4.9 by students.

Building AI systems and low-level software at Stanford — in both Python and C++ — Kevin knows where the theoretical meets the practical in computer science. His biocomputation specialization means he can explain not just how to implement an algorithm, but why certain computational approaches work better for different problem domains. 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.

Trained in computer science at UT Austin and currently pursuing a PhD that blends computational methods with social science, David brings both theoretical depth and applied versatility to CS instruction. He digs into core topics like algorithm analysis, data structures, and computational complexity, connecting them to the kind of real-world problem-solving that makes the discipline click.

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.

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.

From sorting algorithms to recursion to object-oriented architecture, computer science rewards the ability to think in layers of abstraction. Joel is pursuing CS at Cornell alongside physics, which means he approaches programming problems with both mathematical rigor and practical debugging instincts. He's comfortable across Python and Java and adapts to whatever language a student's course requires.

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.

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.