Award-Winning AP Computer Science Principles Tutors
serving Detroit, MI

Pratik doesn't come from a traditional CS background, but his premed training at Cornell — where he regularly works with data sets, statistical models, and logical reasoning — maps directly onto the computational thinking AP CSP tests. He's especially effective at breaking down the data analysis and algorithm units, translating abstract pseudocode into the kind of step-by-step problem solving he uses in biology and chemistry every day.

Nikitha earned her CS degree from Michigan writing actual code — which gives her a practical lens on AP CSP's more conceptual terrain, where students often stumble because the material feels too broad to pin down. She zeroes in on the impact-of-computing and cybersecurity threads that many tutors gloss over, tying them back to real design decisions she encountered in her own coursework. Rated 5.0 by students.

I'm working towards a B.S. in Physics at Michigan State University with a minor in Computational Mathematics, Science, and Engineering. I've made the Chancellor's List every semester and am a member of my university's Honors College. I've worked with students as young as 11 and as old as 25 for the past couple of years, both online and in-person. Since I'm still a student myself, I can relate to clients and teach in a way that creates a comfortable and safe space. I tutor a broad range of subjects and would love to help you!

I am a recent graduate with a master's in electrical engineering from Case Western Reserve University. I won the Bill and Melinda Gates Millennium Scholarship which covers full tuition up to Ph.D. I was on the Dean's List for three consecutive years. Additionally, I won the OZY Media Genius Award in 2015 to work on high-temperature superconductors. I currently work as a Technology Analyst at Accenture. I am also seriously considering whether I should go for a Ph.D. or not.

Computer engineering majors live inside the concepts AP CSP tests — binary representation, how the internet's layers talk to each other, the way abstraction turns messy problems into clean designs. Alfred studies exactly this at URI, so he can ground pseudocode questions and Create Task planning in the real engineering systems he's building in his own coursework. Rated 5.0 by students.

Having TA'd computer science courses at MIT and now pursuing a PhD in Operations Research at Georgia Tech, Isabella brings real programming fluency — particularly in Python — to the algorithmic thinking and data analysis threads that run through AP CSP. She digs into how pseudocode on the exam maps to actual code students write for the Create Task, making the connection between abstract logic and working programs click. Rated 5.0 by students.

Studying computational science at Stanford while coming from a creative arts background at Juilliard's Pre-College program, Julia brings an unusual lens to AP CSP — she naturally bridges the course's technical units on algorithms and data representation with its emphasis on the societal and creative dimensions of computing. Her 1560 SAT and 5.0 tutoring rating back up the analytical chops, but it's that crossover between engineering and the arts that makes her especially effective at coaching the Create Task, where students need both programming logic and clear written explanation.

Lance studied both theoretical mathematics and computer science as an undergraduate before entering medical school, which means he's written real code in Java, C, and SQL — not just traced pseudocode on a worksheet. That practical depth lets him teach AP CSP's algorithmic thinking and abstraction concepts through actual programming logic, giving students a clearer mental model when they hit the Create Task or encounter unfamiliar algorithm-tracing questions on the exam.

Cindy doesn't come from a traditional CS background, but her 36 ACT and analytical training as a Harvard English student give her a sharp edge on AP CSP's most underestimated challenge: the written responses. The Create Task and exam both reward students who can explain computational ideas — abstraction, algorithms, data patterns — in clear, precise language, and that's exactly the skill set she brings to every session.

Stanford's economics curriculum leans heavily on data analysis and programming — skills that map directly onto AP CSP's units on data representation, algorithms, and computational thinking. Julia applies that quantitative training to demystify pseudocode logic and the Create Task's written responses, where clearly explaining your program matters as much as building it. Rated 4.8 by students.

Teaching discrete math at Penn means Keenan spends his weeks translating abstract computational thinking into language undergraduates actually absorb — a skill that maps directly onto AP CSP's pseudocode reasoning and algorithm analysis questions. His philosophy degree also gives him an unusual edge on the exam's societal-impact questions, where students need to construct clear written arguments about data privacy, bias in algorithms, and computing ethics. Rated 5.0 by students.

Software engineering internships at Apple and Microsoft gave Jerry a firsthand view of how the concepts in AP CSP — abstraction, algorithm design, data representation — actually play out in production code. He uses that industry context to make the Create Performance Task feel like a real development project rather than a classroom exercise, which sharpens both the programming and the written-response components. Rated 4.9 by students.

Victoria's CS coursework at Washington University in St. Louis means she can ground AP CSP's abstract Big Ideas — like how abstraction layers simplify complex systems or why algorithms have different efficiencies — in actual programming experience across Java, Python, and C++. That real coding fluency is especially useful when students need to move from pseudocode on the exam to functional code in the Create Task. Rated 5.0 by students.

Robotics engineering at WPI means Matthew writes code that has to actually control physical systems — sensors, motors, feedback loops — which gives him a practical lens on AP CSP's units on algorithms, abstraction, and data representation. He teaches the pseudocode reasoning behind exam questions by tying each concept back to how real machines process information. Rated 5.0 by students.

Statistics training builds exactly the kind of thinking AP CSP actually tests — reading data representations, reasoning through algorithms on paper, and understanding how information gets encoded and compressed. Kyle applies that quantitative lens to the course's Big Ideas, turning abstract pseudocode traces and binary problems into structured, solvable puzzles. Rated 4.9 by students.