College Computer Science Tutoring
Get Connected to a Great Computer Science Tutor Today
Computer science is a challenging subject, touching upon many different facets of computation. Students will be required to understand high level mathematics such as calculus, differential equations, and linear algebra. They will explore the theory and the practical applications of computing. They will take a peek into the problems that proliferated in the early years of technology, and they will learn high-demand problem solving, logical reasoning, and creative thinking skills that will allow them to tackle the issues we face today. Computer science is an exciting field! Every day, computer scientists are pushing the envelope on what is possible.
It is challenging, but it is equally rewarding, and our tutors are here to help.
Frequently Asked Questions
Why Tutoring By A College Professor?
Today, many students are required to take at least one CS course during their time in college, regardless of their major. Our tutors are excited to help both technical and non-technical students with their assignments. We have an incredible team of tutors who are industry professionals and/or champions of academia and are ready to begin scheduling to help with your computer science course. Please call 614-264-1110 today for a free consultation and sign up now.
How are tutoring sessions conducted?
Tutoring sessions are online on Zoom. You can tutor from the comfort of home or the bustle of a coffee shop. It’s up to you.
How long are most tutoring sessions?
Students typically tutor in 1 hour increments, but sessions can run as long as you need.
What if I need an emergency tutoring session for a test tomorrow?
Don’t hesitate to call us at 614-264-1110. We can have you connected with a tutor today to begin scheduling.
What Computer Science courses do you help with?
We offer tutoring for ALL courses at ALL colleges and universities nationwide.
If you don’t see your school or course listed in our course directory, please text/call us at 614-264-1110. We will have your student connected to an expert in less than 24 hours.
Intro to Computer Science
The Ohio State University: CSE 1110, CSE 1111, CSE 1112, CSE 1113, CSE 1114, CSE 1211, CSE 1213, CSE 1222, CSE 1223, CSE 1224
Purdue University: CS 18200, CS23500
University of Michigan: CSE 102, CSE 232
Indiana University: CSCI-A 591
Clemson University: CPSC 1010, CPSC 1020,
University of Alabama: CS 100, CS 101, CS 104,
Georgia Tech: CS 1301, CS 1331
Carnegie Mellon University: SCS 07-120, SCS 15-106, SCS 15-112
Washington University in St. Louis: CSE 131, CSE 132,
Duke University: COMPSCI 101L, COMPSCI 102L
Data Structures and Algorithms
The Ohio State University: CSE 2122, CSE 2123, CSE 2321, CSE 2331, CSE 2321
Purdue University: CS 25100, CS 38100
University of Michigan: CSE 260, CSE 331, CSE 431
Indiana University: CSCI-A 594, CSC1-B 403, CSCI-B 405
Vanderbilt University: CS 2201 , CS 2212, CS 3250
Clemson University: CPSC 2070, CPSC 2120, CPSC 3120
University of Alabama: CS 201, CS 470
Georgia Tech: CS 1332, CS 3510, CS 4520, CS 4530, CS 4540
Case Western Reserve University: CSDS 233, CSDS 310
Carnegie Mellon University: SCS 15-121, SCS 08-722, SCS 02-414, SCS 02-514, SCS 02-613, SCS 15-351, SCS 15-451, SCS 15-495
Washington University in St. Louis: CSE 247
Duke University: COMPSCI 201, COMPSCI 330, COMPSCI 333
Computer Architecture
The Ohio State University: CSE 2421, CSE 3421
University of Michigan: CSE 320, CSE 420
Indiana University: CSCI-B 443
Vanderbilt University:CS 2281
Case Western Reserve University: CSDS 314
Carnegie Mellon University: SCS 15-213, SCS 15-346
Washington University in St. Louis: CSE 362M
Duke University: COMPSCI 250D
Database Management Systems
The Ohio State University: CSE 1114, CSE 3241, CSE 3244
Purdue University: CS 44800
University of Michigan: CSE 480
Clemson University: CPSC 4620
University of Alabama: CS 301, CS 302, CS 305
Georgia Tech: CS 4400, CS 4420, CS 4423
Case Western Reserve University: CSDS 341
Carnegie Mellon University: SCS 15-415, SCS 15-445
Washington University in St. Louis: CSE 435S
Duke University: COMPSCI 316
Operating Systems
The Ohio State University: CSE 2431
University of Michigan: CSE 410
Clemson University: CPSC 3220
University of Alabama: CS 300
Georgia Tech: CS 3210, CS 4210
Case Western Reserve University: CSDS 338
Carnegie Mellon University: SCS 15-410
Washington University in St. Louis: CSE 422S, CSE 522S
Duke University: COMPSCI 310
Artificial Intelligence
The Ohio State University: CSE 3521
Purdue University: CS 47100
University of Michigan: CSE 440
Indiana University: CSCI-B 351
Vanderbilt University: CS 4260
Clemson University: CPSC 4420
University of Alabama: CS 465
Georgia Tech: CS 3600
Case Western Reserve University: CSDS 391
Carnegie Mellon University: SCS 07-180, SCS 15-181, SCS 15-281, SCS 05-317
Washington University in St. Louis: CSE 412A, CSE 513T, CSE 555T
Duke University: COMPSCI 370D
Machine Learning
University of Michigan: CSE 404
Indiana University: CSCI-B 455
Vanderbilt University: CS 3262
Clemson University: CPSC 4430
University of Alabama: CS 484
Georgia Tech: CS 4641
Case Western Reserve University: CSDS 340
Carnegie Mellon University: SCS 10-301, SCS 10-315, SCS 10-335, SCS 10-405, SCS 10-418, SCS 02-620, SCS 05-434, SCS 17-634
Washington University in St. Louis: CSE 417T, CSE 513T, CSE 515T, CSE 517A, CSE 519T
Duke University: COMPSCI 371
Software Development / Software Engineering
The Ohio State University: CSE 2221, CSE 2231
Purdue University: CS30700
University of Michigan: CSE 435
University of Alabama: CS 200
Georgia Tech: CS 3300
Case Western Reserve University: CSDS 393
Carnegie Mellon: SCS 15-214, SCS 15-313
Washington University in St. Louis: CSE 437S, CSE 454A
Duke University: COMPSCI 307D, COMPSCI 308
Programming Languages
The Ohio State University: CSE 2451, CSE 3341
Purdue University: CS 45600
University of Michigan: CSE 450
Indiana University: CSCI-A 597, CSCI-B 522
Vanderbilt University: CS 3270
University of Alabama: CS 403
Georgia Tech: CS 3240, CS 4392
Case Western Reserve University: CSDS 345
Washington University in St. Louis: CSE 425S, CSE 431S
Testimonials
See what Computer Science students and their parents say about their experience with Tutoring By A College Professor. To search for testimonials by school, go to our Testimonials section at the top:
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“Thanks for working hard to find me the right tutor for this CS class! Probably the hardest class ‘ve ever taken. I'm feeling a lot better now. Thank you!”
Benji K
Student at Vanderbilt University
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“We highly recommend Tutoring by a College Professor and their team. Leslie went above and beyond to find a CS tutor for my freshman son who was a perfect fit. They are true professionals. My son’s grade went sky high after 3 sessions and he needed the semester with a great grade and a high confidence level.”
Marla Shulman Amos
Parent at Emory University
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“From coding to statistics, all my tutors have been incredibly helpful. They make sure I understand the material and walk me through it step-by-step. My grades truly reflect the quality of tutoring and I highly recommend them for anyone struggling with any course.”
Jesse Bernstein
Student at Syracuse University
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“Leslie was very accommodating in finding the right fit for my son and his Computer Science tutor. He was able to connect with him within 24 hours and was on his way to understanding his assignments. I highly recommend Leslie Marx!”
Laura Monteleone
Parent at Clemson University
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“This tutoring was very helpful and my tutors worked very well with me to help me succeed in my classes and I will be using this again!”
Sophia Ohmes
Student at University of Missouri
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“Tutoring By A College Professor tutoring service has been great for our daughter. Scheduling is easy, the tutors are very knowledgeable, they are friendly, and they certainly have helped Julia master the information in her upper level classes. I highly recommend this service. It’s well worth it.”
Dr. Jonathan Zager
Parent at University of Florida
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“Maggie was more than just a tutor. She gave her such positive feedback in general. On some days, I know it made all the difference between a good and terrible day. Just with her encouragement. So thankful.”
Toni R
Parent at University of Texas at Austin
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“Tutoring By A College Professor helped connect me with a knowledgeable graduate student who helped me by giving me quality one-on-one time to learn a difficult subject.”
Zoe Worman
Student at Indiana University
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“Vikram was great! He was very helpful with test prep and I ended up with a B in the class which was much better than I thought I could achieve!”
Natalie Faloona
Student at New York University
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“Tutoring was a great experience. I liked being able to see what he was doing on his screen and being able to share both of our resources as well. Always made me feel comfortable when I didn’t know something or needed extra help. He was very helpful in explaining things when needed. He was very flexible with times and when I had some technical issues on my laptop too!”
Tim Hollander
Student at The Ohio State University
Computer Science Courses:
Intro to Computer Science
Common Topics in Intro to Computer Science:
Intro to CS might include a high overview on various popular programming languages like Java, Python and C++. It might go over the pros and cons of different languages and their use cases. Concepts like Object-oriented programming, writing clean code, writing unit tests could be explored. Additional topics might include how to use a debugger, how to test your code, algorithm analysis and recursion.
Here are some tips from our expert computer science tutors:
An Intro to CS course can cover a very large breadth of topics without going into too much detail. It will almost certainly include some programming. Popular introductory textbooks include:
Additionally, if you require supplementary learning material, Harvard offers a highly rated and well known online intro to CS course called CS50. It is self-paced and can be an incredible resource to help ease you into the world of computer science.
Data Structures and Algorithms
Common Topics in Data Structures and Algorithms
A Data Structures and Algorithms course might include topics like basic data structures like arrays, Linked Lists, stacks, and queues. It could introduce you to the idea of algorithmic complexity. It could also explore topics such as divide-and-conquer algorithms, dynamic programming algorithms, network flow algorithms, linear and integer programming, large scale search algorithms, and NP-completeness.
Here are some tips from our expert computer science tutors:
The Data Structures and Algorithms will be one of the most (if not the most) important classes you will take for your CS degree. Not only will it teach you about the tools in your problem solving toolbox, it is also material that you will likely see during coding interviews for internships and jobs. Take this class seriously.
Leetcode is a popular website used to practice coding interview style questions. Leetcode offers its premium version at a discount for groups associated with universities. See if you can get a discount for Leetcode premium. It is immensely useful when preparing for interviews.
Neetcode is a useful resource that provides explanations and solutions to coding interview style questions.
Some popular books for this course are:
Computer Architecture
Common Topics in Computer Architecture
A Computer Architecture course might see topics like computer structure, assembly language, instruction execution, addressing techniques, digital representation of data. Additionally it might cover topics like computer system organization, logic design, microprogramming, cache and memory systems.
Here are some tips from our expert computer science tutors:
A popular Computer Architecture textbook is Computer Architecture: A Quantitative Approach
Database Management Systems
Common Topics in Database Management Systems
Key topics in a Database class include data models, query languages (like SQL), storage architecture, indexing, transaction processing, recovery, query processing and parallel architecture.
Here are some tips from our expert computer science tutors:
Structured Query Language (SQL) is a programming language used for storing and processing information in a relation database. SQL can be thought of as an umbrella term. In fact, there are several “SQL dialects” that are in use throughout industry. These include postgreSQL, mySQL, Oracle, SQL Server, etc.
Relational vs non-relational databases
Relational databases work with structured data. These are table and row oriented data. Non-relational databases do not have these constraints. These have no tables, rows, primary keys or foreign keys. Non-relational databases, however, have the ability to store a large amount of data and provide scalability and flexibility when meeting changing business requirements.
Operating Systems
Common Topics in Operating Systems
An OS course might cover topics like the process model, virtual memory, threads, synchronization and deadlock. It might cover higher level topics such as file systems, interprocess communication, networking, and security.
Artificial Intelligence
Common Topics in Database Management Systems
An AI course might explore topics like algorithms for search and planning, optimization, reasoning and inference mechanisms, machine learning, deep learning and neural networks, natural language processing, computer vision.
Here are some tips from our expert computer science tutors:
Artificial intelligence is a very broad umbrella term. In fact, LLMs (large language models) like chatGPT that have become popular recently represent a subset of the field of artificial intelligence. Machine Learning, Deep Learning and Reinforcement Learning all exist under the umbrella term “AI.” However, in industry, the terms AI and ML are used almost interchangeably.
A popular textbook for AI is Artificial Intelligence: A Modern Approach.
Machine Learning
Common Topics in Machine Learning
An ML course might include topics like supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines), unsupervised learning (clustering, dimensionality reduction, kernel methods), learning theory and adaptive control. Other topics might include perceptrons, logistic regression, decision trees, Bayesian learning, neural networks and backpropagation.
Here are some tips from our expert computer science tutors:
Supervised learning vs Unsupervised learning
One of the most important topics taught in an ML class is the difference between supervised and unsupervised learning. These represent different ways a model can be trained. To put simply, supervised learning uses labeled training data while unsupervised learning does not.
Supervised learning uses labeled data to train itself to make predictions. With supervised learning, the model is iteratively adjusting itself to minimize error. Unsupervised learning will work independently of labels to learn the data’s inherent structure without any guidance or instruction of what is “correct.”
A popular textbook for ML is Deep Learning by Ian Goodfellow.
Software Development/Software Engineering
Common Topics in Software Development/Software Engineering
A software development/engineering course could touch upon the software development lifecycle. This includes developing, delivering, and maintaining software products. You could learn basic development skills, common terminology used in industry, traditional coding standards/guidelines, techniques in modifying, building and testing software.
Here are some tips from our expert computer science tutors:
Some popular textbooks for this course are
Programming Languages
Common Topics in Software Development/Software Engineering:
Typical topics in a Programming Languages course might include language design tradeoffs; implementation considerations; functional, imperative and object-oriented paradigms; formal semantic methods and program analysis; higher order functions and closures; runtime support for language features; security issues
Here are some tips from our expert computer science tutors:
A popular textbook used in Programming Languages courses is Programming Language Pragmatics by Michael Scott.