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.

Mathematical and algorithmic foundations of big data processing. Study of methods for sampling, sketching, and distributed processing of large scale databases, graphs, and data streams for purposes of scalable statistical description, querying, pattern mining, and learning. This course counts as a CS Elective for the CS Major (BS or BA).

Mathematical and algorithmic foundations of big data processing. Study of methods for sampling, sketching, and distributed processing of large scale databases, graphs, and data streams for purposes of scalable statistical description, querying, pattern mining, and learning. This course counts as a CS Elective for the CS Major (BS or BA).

Introduces students to the principal activities involved in developing high-quality software systems, including recent state-of-the-art advances. Topics include: requirements analysis, formal specification methods, software design, testing, program analysis, and automated software engineering.

Introduces students to the principal activities involved in developing high-quality software systems, including recent state-of-the-art advances. Topics include: requirements analysis, formal specification methods, software design, testing, program analysis, and automated software engineering.

How computers work, including digital logic, arithmetic, data path, and memory. Students build circuits with prototyping boards; program microcontrollers in assembly language and C to work with various I/O devices.

Satisfies the Junior Year Writing requirement. The impact of computers on modern society.

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.

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.

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.