Award-Winning Finite Mathematics Tutors
serving Philadelphia, PA

Economics training at the undergraduate level means Simon spent real time inside the linear programming and matrix models that finite mathematics courses test — building objective functions, interpreting shadow prices, and optimizing under constraints weren't abstract exercises but core tools for economic analysis. He's especially useful when students need to connect the algebra of systems of inequalities to what the solution actually means in context.

Physics training builds a particular kind of comfort with matrices and systems of equations — Erik used them constantly for modeling physical systems, which translates directly into the matrix algebra and linear programming that finite mathematics courses test. He unpacks each problem by clarifying the structure first, making sure students see how to organize constraints or set up a payoff table before jumping into computation.

I am a very passionate teacher who has worked in public and private school settings at both the high school and college level. I have an undergraduate mathematics degree, and taught AP Calculus at a North Carolina high school. I also have a PhD in philosophy, and have taught at major research universities in Missouri and Ohio. I am personally invested in my students' success, and take pride in being clear, conscientious, and accessible.

Nursing school requires more discrete math than most people realize — dosage calculations, probability in diagnostics, and interpreting statistical data are baked into the curriculum. Megan applies that practical number sense to finite mathematics topics like counting techniques and basic probability, breaking down word problems into concrete scenarios that make the setup phase less intimidating. Rated 5.0 by students.

Most finite mathematics students struggle not with the computation but with identifying which tool fits which problem — is this a matrix setup, a counting argument, or a linear programming model? Alex's broad math background spanning pre-algebra through calculus gives him the range to meet those translation moments head-on, breaking down the problem's language before jumping to formulas. Rated 4.8 by students.

Seven years of teaching across math levels from elementary through calculus gives Kate a clear read on which algebraic gaps cause students to stall when they hit finite math topics like counting techniques and probability setups. She's particularly effective at slowing down on the translation step — turning a dense word problem into a clean mathematical framework — which is where most finite mathematics students actually lose points. Rated 4.9 by students.

Most finite mathematics students hit a wall not on the computation but on knowing which tool to reach for — is this a matrix problem, a counting argument, or a linear programming setup? Tessa's mathematics major at Yale means she can trace the connections between these topics instead of treating each chapter as isolated, which makes the decision-making step click. Rated 4.9 by students.

Engineering PhD coursework forced Sabry to treat matrix operations, linear systems, and optimization not as textbook exercises but as daily tools for modeling real physical systems — which is exactly the mathematical machinery that finite mathematics courses test. He unpacks each problem type by connecting it back to the underlying structure, whether that's setting up a system of inequalities for linear programming or building a transition matrix for a Markov chain. His years running recitations for both undergrad and graduate students mean he's seen the specific algebraic missteps that slow people down.

Daniel's applied mathematics degree and computer science graduate work mean he's implemented the matrix operations, set logic, and combinatorial reasoning that finite mathematics courses cover — not just on paper, but in actual code where getting the structure wrong produces immediate, visible errors. That precision carries over when he teaches students to set up systems of inequalities or work through probability problems, because he insists on nailing the logical framework before any computation begins. Rated 5.0 by students.

Discrete math and computer science grad work at DePaul keep Ignacio immersed in the logic, set operations, and counting techniques that finite mathematics courses revolve around — he's using these tools actively, not recalling them from years ago. He's especially sharp at unpacking the matrix algebra side of the course, connecting row operations and system setups to the computational thinking he applies daily in his CS program.

I like helping students. I am very patient. I have experience teaching Calculus classes at the University of Miami. I have done private tutoring for all levels of math up to Calculus, as well as Statistics, Business Math, and Math Finance. I have worked in the actuarial field. I have an undergraduate degree in mathematics from Michigan State University and a Master's degree in mathematics from the University of Miami. I worked for The Princeton Review as a tutor for the SAT. I did very well on both the SAT and ACT, and like teaching students how to do better on those. I like history, too, and always find it fun to tutor history.

Graduating from an IB high school with top marks gave Zofia early exposure to the discrete reasoning and probability logic that finite mathematics courses revisit at the college level — and her Brown math degree deepened that foundation considerably. She's especially sharp at unpacking matrix operations and translating messy real-world scenarios into clean systems of equations, making the algebraic setup feel less arbitrary and more deliberate.

Notre Dame's Natural Sciences program puts Mark through enough applied math — probability models, matrix operations, data analysis — that the core of a finite mathematics course isn't unfamiliar territory. He breaks down counting and probability setups by asking students to identify what's being counted and why before reaching for any formula, which clears up the permutation-versus-combination confusion that derails most homework sets. Rated 4.8 by students.

Applied math majors don't just pass through finite mathematics — topics like linear programming, matrix operations, and combinatorics are foundational to the optimization and modeling work Roel trained in throughout his degree. He's especially sharp at teaching students how to set up and interpret systems of linear inequalities, connecting the algebraic steps to what the feasible region actually represents on a graph.

Linear programming, matrix operations, and combinatorics can feel disconnected from the rest of a student's math experience, which is part of what makes Finite Mathematics tricky. Cory's engineering training leaned heavily on these exact tools for optimization and systems modeling, so he teaches them with a practical clarity that makes the material stick. Rated 4.9 by students.

Qualifying for the AIME and MIT's Math Prize for Girls required exactly the kind of combinatorial and logical reasoning that finite mathematics courses test — counting arguments, set operations, and probability setups where one wrong assumption derails the whole problem. Lainie, now a biological engineering student at MIT, brings that competition-trained precision to breaking down whether a problem calls for a permutation or a conditional probability framework. Rated 5.0 by students.

Linear programming, matrix operations, and combinatorics all converge in Finite Mathematics, and Oluwatosin's electrical engineering training means he's applied every one of these tools in practice. He walks through each problem type with an emphasis on setting up the model correctly — the step most students skip and most exams punish.

Most finite mathematics students hit a wall when the course jumps from straightforward matrix operations to applied problems — setting up a linear programming model from a word problem, or knowing when to use a transition matrix for a Markov chain. Adel's PhD in mechanical engineering means he built and solved these kinds of optimization and systems models routinely in his research, so he teaches the setup logic as naturally as the computation. Rated 4.8 by students.