An introductory course in problem solving, using the programming language Java. Focuses on the fundamental concepts of problem solving and on computer implementation. Intended for computer science majors or those applying for the major. Satisfactory completion is a prerequisite for all higher-level computer science courses. Use of a laptop computer required. Prerequisite: high school algebra and basic math skills (R1). (Gen.Ed. R2)

An introductory course in problem solving, using the programming language Java. Focuses on the fundamental concepts of problem solving and on computer implementation. Intended for computer science majors or those applying for the major. Satisfactory completion is a prerequisite for all higher-level computer science courses. Use of a laptop computer required. Prerequisite: high school algebra and basic math skills (R1). (Gen.Ed. R2)

An introductory course in problem solving, using the programming language Java. Focuses on the fundamental concepts of problem solving and on computer implementation. Intended for computer science majors or those applying for the major. Satisfactory completion is a prerequisite for all higher-level computer science courses. Use of a laptop computer required. Prerequisite: high school algebra and basic math skills (R1). (Gen.Ed. R2)

Lecture, discussion. Basic concepts of discrete mathematics useful to computer science: set theory, strings and formal languages, propositional and predicate calculus, relations and functions, basic number theory. Induction and recursion: interplay of inductive definition, inductive proof, and recursive algorithms. Graphs, trees, and search. Finite-state machines, regular languages, nondeterministic finite automata, Kleene's Theorem. Problem sets, 2 midterm exams, timed final.

This course introduces and develops methods for designing and implementing abstract data types using the Java programming language. The main focus is on how to build and encapsulate data objects and their associated operations. Specific topics include linked structures, recursive structures and algorithms, binary trees, balanced trees, and hash tables. These topics are fundamental to programming and are essential to other courses in computer science. The course involves weekly programming assignments, in-class quizzes, discussion section exercises, and multiple exams.

Large-scale software systems like Google - deployed over a world-wide network of hundreds of thousands of computers - have become a part of our lives. These are systems success stories - they are reliable, available ("up" nearly all the time), handle an unbelievable amount of load from users around the world, yet provide virtually instantaneous results.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

This course provides an introduction to the principles and practice of computer and network security. A focus on both fundamentals and practical information will be stressed. The three key topics of this course are cryptography, privacy, and network security. Subtopics include ciphers, hashes, key exchange, security services (integrity, availability, confidentiality, etc.), security attacks, vulnerabilities, anonymous communications, and countermeasures.

Practical experience in the management of software development projects. Managing people, coaching, mentoring, planning, scheduling, evaluation, negotiation, and expectation management are explored. The focus is on the power of communication.

Development of mathematical reasoning skills for problems that involve uncertainty. Counting and probability -- basic counting problems, probability definitions, mean, variance, binomial distribution, discrete random variables, continuous random variables, Markov and Chebyshev bounds, Laws of large number, and central limit theorem. Probabilistic reasoning -- conditional probability and odds, Bayes' Law, Markov Chains, Bayesian Network, Markov Decision Processes.