Award-Winning Finite Mathematics Tutors
serving Evansville, IN

Studying finance at Notre Dame means Charles is actively using the probability, matrix algebra, and linear programming that finite mathematics courses cover — present value calculations, portfolio optimization, and risk modeling all draw on the same toolkit. He breaks down the business-flavored word problems that trip students up, especially when translating a scenario into the right system of equations or figuring out which counting technique applies.

Graduate-level biostatistics and plant biology research forced Dan to live inside the probability, matrix algebra, and data modeling that finite mathematics courses cover — except he was applying them to real population genetics and ecological datasets. He breaks down counting problems and probability setups by connecting them to the quantitative reasoning he uses daily, which tends to make abstract notation feel less arbitrary. Rated 5.0 by students.

Studying finance and business analytics at Indiana University means Christopher regularly uses the matrix operations, probability calculations, and linear programming models that finite mathematics courses are built around — they're how he analyzes portfolios and optimizes resource allocation in his own coursework. His peer tutoring role in Microsoft Excel adds a practical edge, since he can show students how to set up and verify systems of equations or payoff matrices in a spreadsheet when the hand calculations get unwieldy.

Actuarial science training puts Jonathan squarely inside the probability, counting techniques, and matrix methods that finite mathematics courses revolve around — he's using these tools in his own coursework to model risk and evaluate outcomes. He's especially clear at unpacking the logic behind setting up a problem, whether it's defining events for a probability question or structuring an objective function for optimization, so the math follows naturally from the reasoning.

Studying finance at Indiana University means Kunal works with the same matrix algebra, probability, and linear programming tools that finite mathematics courses test — except he's applying them to portfolio analysis and resource allocation problems in real time. He's sharp at translating messy word problems into clean mathematical setups, particularly when students can't tell whether they're dealing with a system of inequalities or a straightforward optimization.

Having earned both a bachelor's and master's degree in mathematics, Andrew has taught nearly every level of math from pre-algebra through graduate statistics — which means he knows exactly how finite mathematics sits at the crossroads of set theory, probability, and matrix algebra. He's especially sharp at untangling the logic behind linear programming setups, where students often know the mechanics but can't figure out which constraints to write down. Rated 4.9 by students.

Currently pursuing a graduate degree in mathematics while holding an applied math bachelor's, Drisana has worked through the matrix algebra, linear programming, and probability that finite math courses pile on — and she's done it from both the theoretical and computational sides. She's especially sharp at unpacking the logic behind setting up systems of inequalities, where most students can follow the arithmetic but struggle to build the model from a word problem. Rated 5.0 by students.

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.

Sam's PhD in statistics means the probability and matrix algebra chapters in finite mathematics are second nature — he taught and applied those tools at a graduate level long before they showed up in an undergrad syllabus. His biomedical engineering background adds a practical edge when explaining how to set up linear programming problems or interpret a Markov chain, since he's used those models to solve real optimization and modeling questions. Rated 4.9 by students.

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.

Jacob's computer science master's work gave him daily practice with the graph theory, combinatorics, and algorithm design that underpin much of a finite mathematics course — so when he teaches topics like counting techniques or matrix operations, he can show exactly how each one functions inside a larger system. His 5.0 rating and ACT score of 35 speak to the precision he brings to breaking down multi-step setups, especially the probability and logic problems that tend to snowball when the initial framing is off.

Pursuing a statistics and machine learning certificate at Princeton alongside her philosophy degree means Julie regularly works with the probability, combinatorics, and matrix operations that finite mathematics courses are built around — but her philosophy training also sharpens the logical reasoning that makes set theory and counting arguments click. She's especially strong at unpacking problems where the challenge isn't computation but figuring out how to structure the setup in the first place. Rated 4.9 by students.

The jump from astrophysics to playwriting sounds dramatic, but Jacob's undergraduate work in astrophysics required exactly the kind of matrix algebra, probability, and systematic logic that finite mathematics courses test — he just happened to also write plays about it. He's sharp at unpacking problems where students need to set up a system of equations or figure out whether a counting problem calls for ordered or unordered selections, breaking the language of the question apart before touching any formulas.

Anna's policy analysis PhD at Pardee RAND means she uses matrix algebra, probability models, and optimization daily — the same toolkit that finite mathematics courses test in a more compressed format. She unpacks linear programming and decision-theory problems by tying them to the real policy scenarios where these methods actually get applied, which makes abstract setups feel less arbitrary.

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.

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.

Linear programming, Markov chains, and matrix operations can feel disconnected from anything practical — until someone ties them to real decision-making problems. Rithi's quantitative training across neuroscience and biotechnology gives her a natural way to ground Finite Mathematics in applied contexts that make the material stick.

William's applied statistics master's work and biomedical engineering background mean he's built and manipulated the matrices, probability models, and optimization setups that finite mathematics courses test — but in the context of real engineering and data problems. He's especially sharp at teaching students how to translate messy word problems into clean linear programming constraints or probability frameworks, since that translation step is where most errors actually happen. Rated 4.8 by students.