TMU CS Class Tree
(Course Prerequisite List)

This course covers the fundamentals of discrete mathematics with a focus on proof methods. Topics include: propositional and predicate logic, notation for modern algebra, naive set theory, relations, functions and proof techniques.

This course is an introduction to the cellular and molecular mechanisms in the cell. Topics include macromolecule structure and function, enzymes, cell membrane structure and function, cell cycle control, cell division, metabolism, and photosynthesis. Also included is an introduction to genetics and patterns of inheritance, gene expression and developmental biology. Laboratory exercises complement lectures.

This foundation course begins with an introduction to types of chemical compounds, chemical reactions and stoichiometry. Subsequent topics include the investigation of the states of matter (primarily liquids and gases), solutions and colligative properties, chemical equilibrium, acids and bases, and thermochemistry. This course acts as the first half of a full year general chemistry sequence and is a precursor to CHY 113 General Chemistry II.

Units and vectors. Motion: linear, projectile, circular and oscillatory motion. Newton's laws: Force, mass and acceleration; work, energy and power; linear and angular momentum. Electrostatics: Electric force and field; potential and potential energy; capacitance. Electric current and DC circuits. Magnetic field and force: magnetic force on currents and charges; Hall effect; torque on current loops; Waves: classification of waves; energy transfer; light and electromagnetic waves; diffraction and interference. MAPLE used for simulation/visualization of physical phenomena and problem solving.

Transcendental functions, differential equations, techniques of integration, improper integrals, infinite series.

This course will cover some of fundamental social, legal, and ethical issues inherent in the discipline of computing. Topics include social context, analytical tools, professional ethics, intellectual property, privacy and civil liberties, professional communication, sustainability and equity issues.

Calculus of functions of one variable and related numerical topics. Derivatives of algebraic, trigonometric and exponential functions. Differentiation techniques and applications of derivatives. Techniques of integration.

Introduction to discrete structures as they apply to design and analysis. Review of proof techniques. Induction and recursion. Graphs and trees, and their applications in computing. Finite automata and regular expressions. Counting: arithmetic and geometric progressions, permutations and combinations, modular arithmetic.

A survey of major programming paradigms, with emphasis on the functional paradigm. Discussion of data typing, program decomposition, scoping rules, control structures parameter passing. Programming languages will likely include commercially important functional languages such as Haskell, Clojure and Erlang, with examples of others as time permits.

Introduction to O/S (system calls, interrupts, synchronous and asynchronous traps, O/S structure), using processes (process communication and synchronization), primitive communications (signals and signal management calls), pipes, messages, semaphores, shared memory, memory management, file systems, and (time permitting) remote procedure calls.

Probability and Statistics I: Descriptive statistics. Probability (Laws of probability. Conditional probability. Discrete probability distributions (binomial, hypergeometric, Poisson). Continuous probability distributions, Normal, t-exponential, x². Applications of discrete and continuous distributions. Sampling distributions (sample mean, sample proportion, difference between two samples, difference between two sample proportions). Sampling distribution concerning mean variance and proportion for one or two populations. Estimation for large and small samples. Hypothesis testing concerning mean, variance and proportion for one or two populations, (large samples and small samples) including paired data testing.

The student will creatively apply the material learned in core courses to a significant problem.

Introduction to analysis, management, and visualization of cellular information at the molecular level. The course includes an overview of mathematical modeling and simulation, pattern matching, methods for phylogenetics, gene recognition, distributed and parallel biological computing, designing and managing biological databases (both relational and object-oriented), linking disparate databases and data, data mining, reasoning by analogy, hypothesis formation and testing by machine.

Software and hardware considerations in computer graphics. Mathematical manipulation of graphical objects; interactive graphics and the user interface; representation of 3-D shapes; fundamental implementation of algorithms.

This course is an introduction to different aspects of data science. The focus is on data collection and management and then applying data analytics, statistical and machine learning models on the collected data. The topics of the course include big data, data models, distributed computing, regression and statistical analysis, neural networks, support vector machines, scalable machine learning models, basics of natural language processing, recommendation systems, and analyzing social graphs. All these topics will be discussed at the introductory level.

TThis course presents the concepts and applications of the technologies to design and develop creative and successful web services. It covers design fundamentals and also programming languages for both server-side and client-side environments. Responsive design, search engine optimization and monetization strategies are also introduced.