Computer Science | Worcester Polytechnic Institute

Undergraduate Courses

CS 1004. INTRODUCTION TO PROGRAMMING FOR NON-MAJORS

Cat. I
This course introduces students to the fundamental principles of
programming in imperative and scripting languages. Topics include control
structures, iterators, functional decomposition, basic data structures (such
as records). Students will be expected to implement, test and debug
programs. Through the use of compelling applications and lab exercises,
students will learn how to interface with external data systems and control
devices.
Recommended background: none.

All Computer Science students and other students wishing to prepare for
3000-level courses in Computer Science should take CS 1101/1102
instead of CS 1004. This course provides sufficient background for CS
2301 Systems Programming for Non-Majors.

CS 1101. INTRODUCTION TO PROGRAM DESIGN

This course introduces principles of computation and programming with an emphasis on program design. Topics include the design, implementation and testing of programs that use a variety of data structures (such as structures, lists, and trees), functions, conditionals, recursion and higher-‐order functions. Students will be expected to design simple data models, and implement and debug programs in a functional programming language.
Recommended background: none.
Either CS 1101 or CS 1102 provides sufficient background for further courses in the CS department. Undergraduate credit may not be earned for both this course and CS 1102.

CS 1102. ACCELERATED INTRODUCTION TO PROGRAM DESIGN

In the first half of the term, this course covers the same functional programming material as CS 1101 at roughly twice the pace. The second half of the term is a preview of selected advanced Computer Science topics, such as the design and implementation of application-specific languages, macros, programming with the HTTP protocol and continuation-passing style. Students will be expected to complete an open-ended individual programming project.
Recommended background: Substantial prior programming experience (including functions, recursion, and lists, as would be covered in high-school Advanced Placement Computer Science A courses, but not necessarily AP CS Principles courses). Either CS 1101 or CS 1102 provides sufficient background for further courses in the CS department. Undergraduate credit may not be earned for both this course and CS 1101.

CS 2011. INTRODUCTION TO MACHINE ORGANIZATION AND ASSEMBLY LANGUAGE

Cat. I
This course introduces students to the structure and behavior of modern digital
computers and the way they execute programs. Machine organization topics include the Von Neumann model of execution, functional organization of computer hardware, the memory hierarchy, caching performance, and pipelining. Assembly language topics include representations of numbers in computers, basic instruction sets, addressing modes, stacks and procedures, low-level I/O, and the functions of compilers, assemblers, linkers, and loaders. The course also presents how code and data structures of higher-level languages are mapped into the assembly language and machine representations of a modern processor. Programming projects will be carried out in the C language and the assembly language of a modern processor.
Recommended background: CS 2301 or CS 2303, or a significant knowledge of C/C++.

CS 2022. DISCRETE MATHEMATICS

Cat. I
This course serves as an introduction to some of the more important concepts,
techniques, and structures of discrete mathematics, providing a bridge between
computer science and mathematics.
Topics include sets, functions and relations, propositional and predicate calculus, mathematical induction, properties of integers, counting techniques and graph theory.

Students will be expected to develop simple proofs for problems drawn
primarily from computer science and applied mathematics.
Recommended background: none.

CS 2102. OBJECT-ORIENTED DESIGN CONCEPTS

Cat. I
This course introduces students to an object-oriented model of programming.
Building from the design methodology covered in CS 1101/CS 1102, this
course shows how programs can be decomposed into classes and objects. By
emphasizing design, this course shows how to implement small defect-free
programs and evaluate design decisions to select an optimal design under specific
assumptions. Topics include inheritance, exceptions, interface, design by
contract, basic design patterns, and reuse. Students will be expected to design,
implement, and debug object-oriented programs composed of multiple classes
and over a variety of data structures.

Recommended background: CS 1101 or CS 1102.

CS 2103. ACCELERATED OBJECT-ORIENTED DESIGN CONCEPTS

This course covers the data structures and general program-design material from CS2102, but assumes that students have significant prior experience in object-oriented programming. The course covers object-oriented design principles and data structures more deeply and at a faster pace than in CS 2102. Students will be expected to design, implement, test, debug, and critique programs both for correctness and adherence to good object-oriented design principles. The course is designed to strengthen both the design skills and algorithmic thinking of students who already have a foundation in object-oriented programming. Recommended background: CS 1101 or CS 1102 and significant prior experience writing object-oriented programs from scratch. Advanced Placement Computer Science A courses should provide sufficient background; students from AP CS Principles courses or gentler introductions to Java Programming are advised to take CS2102 instead. Students may receive credit for only one of the following three courses: CS 2102, CS 210X, CS 2103.

CS 2119. APPLICATION BUILDING WITH OBJECT-ORIENTED CONCEPTS

Cat. I
This course introduces students to an object-oriented model of programming, with an emphasis on the programming approaches useful in creating software applications. Students will be expected to design, implement, and debug object-oriented programs. Topics include inheritance, user interfaces, and database access. This course is for non-CS majors with prior programming experience and an interest in building software applications.
Recommended background: Some programming experience such as found in CS 1004, CS 1101, or CS 1102.

CS 2223. ALGORITHMS

Cat. I
Building on a fundamental knowledge of data structures, data abstraction
techniques, and mathematical tools, a number of examples of algorithm design
and analysis, worst case and average case, will be developed.

Topics include greedy algorithms, divide-and-conquer, dynamic programming,
heuristics, and probabilistic algorithms. Problems will be drawn from areas such
as sorting, graph theory, and string processing. The influence of the computational
model on algorithm design will be discussed.
Students will be expected to perform analysis on a variety of algorithms.
Recommended background: CS 2102 and CS 2022.

CS 2301. SYSTEMS PROGRAMMING FOR NON-MAJORS

Cat. I
This course introduces the C programming language and system programming
concepts to non-CS majors who need to program computers in their own fields.
The course assumes that students have had previous programming experience. It
quickly introduces the major concepts of the C language and covers manual
memory management, pointers and basic data structures, the machine stack,
and input/output mechanisms. Students will be expected to design, implement,
and debug programs in C.
Recommended background: CS 1101 or CS 1102 or previous experience
programming a computer.

All Computer Science students and other students
wishing to prepare for upper-level courses in Computer Science should take
CS 2303 instead of CS 2301. Students who have credit for CS 2303 may not
receive subsequent credit for CS 2301.

CS 2303. SYSTEMS PROGRAMMING CONCEPTS

Cat. I
This course introduces students to a model of programming where the
programming language exposes details of how the hardware stores and executes
software. Building from the design concepts covered in CS 2102, this course
covers manual memory management, pointers, the machine stack, and input/
output mechanisms. The course will involve large-scale programming exercises
and will be designed to help students confront issues of safe programming with
system-level constructs. The course will cover several tools that assist programmers
in these tasks. Students will be expected to design, implement, and debug
programs in C++ and C. The course presents the material from CS 2301 at a
fast pace and also includes C++ and other advanced topics.

Recommended background: CS 2102 and/or substantial object-oriented
programming experience.

CS 3013. OPERATING SYSTEMS

Cat. I
This course provides the student with an understanding of the basic components
of a general-purpose operating system. Topics include processes, process
management, synchronization, input/output devices and their programming,
interrupts, memory management, resource allocation, and an introduction to
file systems. Students will be expected to design and implement a large piece of
system software in the C programming language.

Undergraduate credit may not be earned both for this course and for CS 502.
Recommended background: CS 2303 or CS 2301, and CS 2011.

CS 3041. HUMAN-COMPUTER INTERACTION

Cat. I

This course develops in the student an understanding of the nature and
importance of problems concerning the efficiency and effectiveness of human
interaction with computer-based systems.

Topics include the design and evaluation of interactive computer systems,
basic psychological considerations of interaction, interactive language design,
interactive hardware design, and special input/output techniques.

Students will be expected to complete several projects. A project might be a
software evaluation, interface development, or an experiment.
Recommended background: CS 2102 or CS 2118.

CS 3043. SOCIAL IMPLICATIONS OF INFORMATION PROCESSING

Cat. I
This course makes the student aware of the social, moral, ethical, and
philosophical impact of computers and computer-based systems on society, both
now and in the future.
Topics include major computer-based applications and their impact, humanmachine
relationships, and the major problems of controlling the use of
computers.

Students will be expected to contribute to classroom discussions and to
complete a number of significant writing assignments.
This course is recommended for juniors and seniors.
Undergraduate credit may not be earned both for this course and for CS 505.

Recommended background: a general knowledge of computers and computer
systems.

CS 3133. FOUNDATIONS OF COMPUTER SCIENCE

Cat. I

This course introduces the theoretical foundations of computer science. These

form the basis for a more complete understanding of the proficiency in
computer science.

Topics include computational models, formal languages, and an introduction to
compatibility and complexity theory, including NP-completeness.

Students will be expected to complete a variety of exercises and proofs.
Undergraduate credit may not be earned both for this course and for CS 5003.
Recommended Background: Discrete mathematics (CS 2022 or equivalent) and algorithms (CS 2223 or equivalent).
Students who have credit for CS 4121 cannot receive credit for CS 3133.

CS 3431. DATABASE SYSTEMS I

Cat. I
This course introduces the student to the design, use, and application of
database management systems.

Topics include the relational data model, relational query languages, design
theory, and conceptual data design and modeling for relational database design.
Techniques that provide for data independence, and minimal redundancy will
be discussed.
Students will be expected to design and implement database system
applications.

Undergraduate credit may not be earned both for this course and for CS 4431
or CS 542.
Recommended background: CS 2022 and either CS 2102 or CS 2118.

CS 3516. COMPUTER NETWORKS

Cat. I
This course provides a broad view of computer networks. The course exposes students to all seven layers of OSI Reference Model while providing an introduction into newer topics such as wireless networking and Internet traffic concerns. The objective is to focus on an understanding of fundamental concepts of modern computer network architecture from a design and performance perspective. Topics covered include: physical layer considerations, network protocols, wide area networks, local area networks, wireless networks, switches and routing, congestion, Internet traffic and network security. Students will be expected to do extensive systems/network programming and will be expected to make use of simulation and measurement tools to gain an appreciation of current network design and performance issues.
This course is also highly recommended for RBE and IMGD majors.
Recommended background: CS 2301 or CS 2303, or a significant knowledge of C/C++.

CS 3733. SOFTWARE ENGINEERING

Cat. I
This course introduces the fundamental principles of software engineering.
Modern software development techniques and life cycles are emphasized.

Topics include requirements analysis and specification, analysis and design,
architecture, implementation, testing and quality, configuration management,
and project management.

Students will be expected to complete a project that employs techniques from
the topics studied.

This course should be taken before any course requiring a large programming
project.
Undergraduate credit may not be earned both for this course and for CS 509.
Recommended background: CS 2102.

CS 4032. NUMERICAL METHODS FOR LINEAR AND NONLINEAR SYSTEMS

Cat. I
This course provides an introduction to modern computational methods for
linear and nonlinear equations and systems and their applications.

Topics covered include: solution of nonlinear scalar equations, direct and
iterative algorithms for the solution of systems of linear equations, solution of
nonlinear systems, the eigenvalue problem for matrices. Error analysis will be
emphasized throughout.

Recommended background: MA 2071. An ability to write computer programs
in a scientific language is assumed.

CS 4033. NUMERICAL METHODS FOR CALCULUS AND DIFFERENTIAL EQUATIONS

Cat. I
This course provides an introduction to modern computational methods for
differential and integral calculus and differental equations.
Topics covered include: interpolation and polynomial approximation,
approximation theory, numerical differentiation and integration, numerical
solutions of ordinary differential equations. Error analysis will be emphasized
throughout.

Recommended background: MA 2051. An ability to write computer programs
in a scientific language is assumed. Undergraduate credit may not be earned for
both this course and for MA 3255/CS 4031.

CS 4100. ARTIFICIAL INTELLIGENCE FOR INTERACTIVE MEDIA AND GAMES

Cat. II
Algorithms and programming techniques from artificial intelligence (AI) are key contributors to the experience of modern computer games and interactive media, either by directly controlling a non-player character (NPC) or through more subtle manipulation of the environment. This course will focus on the practical AI programming techniques currently used in computer games for NPC navigation and decision-making, along with the design issues that arise when AI is applied in computer games, such as believability and real-time performance. The course will also briefly discuss future directions in applying AI to games and media. Students will be expected to complete significant software development projects using the studied techniques.
Recommended background: object-oriented design concepts (CS 2102), algorithms (CS 2223), and knowledge of technical game development (IMGD 3000).
This course will be offered in 2016-17, and in alternating years thereafter.

CS 4120. ANALYSIS OF ALGORITHMS

Cat. II
This course develops the skill of analyzing the behavior of algorithms.
Topics include the analysis, with respect to average and worst case behavior
and correctness, of algorithms for internal sorting, pattern matching on strings,
graph algorithms, and methods such as recursion elimination, dynamic
programming, and program profiling.
Students will be expected to write and analyze programs.
Undergraduate credit may not be earned both for this course and for CS 5084.
Recommended background: algorithms (CS 2223 or equivalent), and some knowledge of probability.
This course will be offered in 2020-21, and in alternating years thereafter.

CS 4123. THEORY OF COMPUTATION

Cat. II
Building on the theoretical foundations from CS 3133, this course addresses the
fundamental question of what it means to be “computable,” including different
characterization of computable sets and functions.
Topics include the halting program, the Church-Turing thesis, primitive
recursive functions, recursive sets, recursively enumerable sets, NP-completeness,
and reducibilities.
Students will be expected to complete a variety of exercises and proofs.
Recommended Background: CS 3133.
This course will be offered in 2015-16, and in alternating years thereafter.

CS 420X. GRAPHICL SIMUL OF PHYSICAL SYS

CS 4233. OBJECT-ORIENTED ANALYSIS AND DESIGN

Cat. II
This Software Engineering course will focus on the process of Object-Oriented
Analysis and Design. Students will be expected to complete a large number of
exercises in Domain Modeling, Use Case Analysis, and Object-Oriented Design. In addition, the course will investigate Design Patterns, which are elements of
reusable object-oriented software designs. This course will survey a set of design
patterns and consider how these patterns are described and used to solve design
problems.
Recommended Background: CS 2303 and CS 3733.
This course will be offered in 2016-17, and in alternating years thereafter.

CS 4241. WEBWARE: COMPUTATIONAL TECHNOLOGY FOR NETWORK INFORMATION SYSTEMS

Cat. I
This course explores the computational aspects of network information systems
as embodied by the World Wide Web (WWW). Topics include: languages for
document design, programming languages for executable content, scripting
languages, design of WWW based human/computer interfaces, client/server
network architecture models, high level network protocols (e.g., http), WWW
network resource discovery and network security issues.

Students in this course will be expected to complete a substantial software
project (e.g., Java based user interface, HTML/CGI based information system,
WWW search mechanisms).

Recommended background: CS 2102 and CS 3013.

CS 4341. INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Cat. I

This course studies the problem of making computers act in ways which we call
"intelligent".

Topics include major theories, tools and applications of artificial intelligence,
aspects of knowledge representation, searching and planning, and natural
language understanding.
Students will be expected to complete projects which express problems that
require search in state spaces, and to propose appropriate methods for solving
the problems.
Undergraduate credit may not be earned both for this course and for CS 534.
Recommended background: CS 2102, CS 2223, and CS 3133.

CS 4342. MACHINE LEARNING

Cat.I
In this course, students will explore both theoretical and practical aspects of machine learning, including algorithms for regression, classification, dimensionality reduction, clustering, and density estimation. Specific topics may include neural networks and deep learning, Bayesian networks and probabilistic graphical models, principal component analysis, k-means clustering, decision trees and random forests, support vector machines, and kernel methods.
Recommended background: Multivariate Calculus (MA 1024 or MA 1034), Linear Algebra (such as MA 2071), Probability (MA 2621 or MA 2631), and Algorithms (CS 2223).
Students may not earn credit for both CS 453X and CS 4342.
Undergraduate credit may not be earned both for this course and for CS 539.

CS 4401. SOFTWARE SECURITY ENGINEERING

Cat. I
This course provides an introduction to the pitfalls and practices of building
secure software applications. Topics will include threat modeling, secure software
development, defensive programming, web security and the interaction between
security and usability. The course focuses on the application level with minor
attention to operating-system level security; network-level security is not
covered. Assignments involve designing and implementing secure software,
evaluating designs and systems for security-related flaws, and presentations on
security issues or tools. All students will be required to sign a pledge of
responsible conduct at the start of the course.

Recommended Background: CS3013 and CS3733. The course assumes nontrivial experience with C and Unix, familiarity with operating systems,
filesystems, and databases, and experience with technologies for building web
applications (from CS4241 or personal experience).

CS 4404. TOOLS AND TECHNIQUES IN COMPUTER NETWORK SECURITY

This course introduces students to modern network security concepts, tools, and techniques. The course covers security threats, attacks and mitigations at the operating-system and network levels (as opposed to the software level). Topics include: authentication, authorization, confidentiality, integrity, anonymity, privacy, intrusion detection and response, and cryptographic applications. Students will become familiar with modern security protocols and tools. Assignments will involve using security-testing software to uncover vulnerabilities, network packet analyzers, and existing security applications to create secure network implementations. The course requires enough programming and systems background to understand attacks and use systems tools, but does not involve significant programming projects. Assignments and projects will use a Linux base for implementation.
Recommended Background: Knowledge of operating systems (CS3013 or equivalent) and computer networks (CS3516 or equivalent). Familiarity with Linux or Unix is essential.

CS 4432. DATABASE SYSTEMS II

Cat. II
This course concentrates on the study of the internals of database management
systems. Topics include: principles and theories of physical storage management,
advanced query languages, query processing and optimization, index structures
for relational databases, transaction processing, concurrency control, distributed
databases, and database recovery, security, client server and transaction
processing systems. Students may be expected to design and implement software
components that make up modern database systems.
Undergraduate credit may not be earned both for this course and CS 542.
Recommended background: CS 3431 and CS 3733.
This course will be offered in 2015-16, and in alternating years thereafter.

CS 4445. DATA MINING AND KNOWLEDGE DISCOVERY IN DATABASES

Cat. II
This course provides an introduction to Knowledge Discovery in Databases
(KDD) and Data Mining. KDD deals with data integration techniques and with
the discovery, interpretation and visualization of patterns in large collections of
data. Topics covered in this course include data warehousing and mediation
techniques; data mining methods such as rule-based learning, decision trees,
association rules and sequence mining; and data visualization. The work
discussed originates in the fields of artificial intelligence, machine learning,
statistical data analysis, data visualization, databases, and information retrieval.
Several scientific and industrial applications of KDD will be studied.

Recommended background: MA 2611, CS 2223, and CS 3431, or CS 3733.
This course will be offered in 2016-17, and in alternating years thereafter.

CS 4513. DISTRIBUTED COMPUTING SYSTEMS

Cat. II
This course extends the study of the design and implementation of operating
systems begun in CS 3013 to distributed and advanced computer systems.

Topics include principles and theories of resource allocation, file systems,
protection schemes, and performance evaluation as they relate to distributed and
advanced computer systems.
Students may be expected to design and implement programs that emphasize
the concepts of file systems and distributed computing systems using current
tools and languages.

Undergraduate credit may not be earned both for this course and for CS 502.
Recommended background: CS 3013, CS 3516, and system programming
experience.
This course will be offered in 2015-16, and in alternating years thereafter.

CS 4515. COMPUTER ARCHITECTURE

Cat. II
This course explores the architectural design of modern computer systems in
terms of instruction sets and the organization of processors, controllers,
memories, devices, and communication links. Topics include an overview of
computer architectures and system components, theoretical foundations,
instruction-level and thread-level pipelining, multifunction pipelines, multi-core
systems, caching and memory hierarchies, and multi-core and parallel computer
organization. Students may be expected to design and implement programs that
simulate significant components of modern computer architectures.
Recommended background: CS 2011 or ECE 2049, and CS 3013.
This course will be offered in 2016-17, and in alternating years thereafter.

CS 4516. ADVANCED COMPUTER NETWORKS

Cat. II
This course provides an in-depth look into computer networks. While repeating
some of the areas from CS 3516, the goal is to go deeper into computer networks topics. This in-depth treatment in topics such as routing, congestion control, wireless layer protocols and physical signaling considerations will require the use of basic queuing theory and probability to provide a more formal treatment of computer networks performance. Other topics covered include: LAN and WLAN technologies, mobile wireless networks, sensor networks, optical networks, network security, intrusion detection and network management. Students will be expected to do more sophisticated network programming than seen in CS 3516 and will conduct laboratory activities involving measuring the performance of modern networking applications running on both wired networks and infrastructure wireless networks.
Undergraduate credit may not be earned both for this course and for CS 513.
Recommended background: CS 3013, CS 3516, and knowledge of probability. The course assumes a familiarity with operating systems including Unix or Linux, and significant experience with C/C++.
This course will be offered in 2016-17, and in alternating years thereafter.

CS 4518. MOBILE & UBIQUITOUS COMPUTING

The goal of this course is to acquaint students with fundamental concepts and state-of-the-art computer science literature in mobile and ubiquitous computing. Topics to be covered include mobile systems issues, human activity and emotion sensing, location sensing, mobile human-computer interaction, mobile social networking, mobile health, power saving techniques, energy and mobile performance measurement studies and mobile security.
The course will introduce the programming of mobile devices such as smartphones running the Android operating system.
Recommended background: Proficiency in programming in Java, including classes, inheritance, exceptions, interfaces, polymorphism (CS 2012 or equivalent).
Students may not earn credit for both CS 403X and CS 4518

CS 4533. TECHNIQUES OF PROGRAMMING LANGUAGE TRANSLATION

Cat. II
This course studies the compiling process for high-level languages.

Topics include lexical analysis, syntax analysis, semantic analysis, symbol
tables, intermediate languages, optimization, code generation and run-time
systems.
Students will be expected to use compiler tools to implement the front end,
and to write a program to implement the back end, of a compiler for a recursive
programming language.

Undergraduate credit may not be earned for both this course and for CS 544.
Recommended Background: CS 2102 and CS 3133.
This course will be offered in 2016-17, and in alternating years thereafter.

CS 4536. PROGRAMMING LANGUAGES

Cat. II
This course covers the design and implementation of programming languages.
Topics include data structures for representing programming languages,
implementing control structures (such as functions, recursion, and exceptions),
garbage collection, and type systems. Students will be expected to implement
several small languages using a functional programming language.
Recommended background: CS 2303, CS 3133, and experience programming
in a functional language (as provided by CS 1101 or CS 1102).

Undergraduate credit may not be earned for both this course and CS 536.
This course will be offered in 2016-17, and in alternating years thereafter.

CS 453X. MACHINE LEARNING

In this course, students will explore both theoretical and practical aspects of machine learning, including algorithms for regression, classification, dimensionality reduction, clustering, and density estimation. Specific topics may include: neural networks and deep learning, Bayesian networks and probabilistic graphical models, principal component analysis, k-means clustering, decision trees and random forests, support vector machines and kernel methods.
Recommended background: Knowledge of Multivariate Calculus (MA 1024 or MA 1034), Linear Algebra (such as MA 2071), Probability (MA 2621 or MA 2631), and Algorithms (CS 2223).

CS 4731. COMPUTER GRAPHICS

Cat. I

This course studies the use of the computer to model and graphically render
two- and three-dimensional structures.
Topics include graphics devices and languages, 2- and 3-D object representations,
and various aspects of rendering realistic images.
Students will be expected to implement programs which span all stages of the
3-D graphics pipeline, including clipping, projection, arbitrary viewing, hidden
surface removal and shading.
Undergraduate credit may not be earned both for this course and for CS 543.
Recommended background: CS 2223, CS 2303 and MA 2071.

CS 4801. INTRODUCTION TO CRYPTOGRAPHY AND COMMUNICATION SECURITY

This course provides an introduction to modern cryptography and communication security. It focuses on how cryptographic algorithms and protocols work and how to use them. The course covers the concepts of block ciphers and message authentication codes, public key encryption, digital signatures, and key establishment, as well as common examples and uses of such schemes, including the AES, RSA-OAEP, and the Digital Signature Algorithm. Basic cryptanalytic techniques and examples of practical security solutions are explored to understand how to design and evaluate modern security solutions. The course is suited for students interested in cryptography or other security related fields such as trusted computing, network and OS security, or general IT security.
Recommended background: ECE 2049 Embedded Computing in Engineering Design or CS 2301 Systems Programming for Non-Majors or equivalent
Suggested background: CS 2022/MA2201 Discrete Mathematics

CS 4802. BIOVISUALIZATION

Cat. II
This course will use interactive visualization to model and analyze biological
information, structures, and processes. Topics will include the fundamental
principles, concepts, and techniques of visualization (both scientific and
information visualization) and how visualization can be used to study
bioinformatics data at the genomic, cellular, molecular, organism, and
population levels. Students will be expected to write small to moderate programs
to experiment with different visual mappings and data types.
Recommended background: CS 2102, CS 2223, and one or more biology
courses.
This course will be offered in 2016-17, and in alternating years thereafter.

CS 4803. BIOLOGICAL AND BIOMEDICAL DATABASE MINING

Cat. II
This course will investigate computational techniques for discovering patterns in
and across complex biological and biomedical sources including genomic and
proteomic databases, clinical databases, digital libraries of scientific articles, and
ontologies. Techniques covered will be drawn from several areas including
sequence mining, statistical natural language processing and text mining, and
data mining.

Recommended Background: CS 2102, CS 2223, MA 2610 or MA 2611, and
one or more biology courses.
This course will be offered in 2015-16, and in alternating years thereafter.

CS 480X. DATA VISUALIZATION

This course exposes students to the field of data visualization, i.e., the graphical communication of data and information for the purposes of presentation, confirmation, and exploration. The course introduces the stages of the visualization pipeline. This includes data modeling, mapping data attributes to graphical attributes, visual display techniques, tools, paradigms, and perceptual issues. Students learn to evaluate the effectiveness of visualizations for specific data, task, and user types. Students implement visualization algorithms and undertake projects involving the use of commercial and public-domain visualization tools.
Recommended background: Knowledge of Linear Algebra (such as MA 2071), Probability theory (MA 2621), and Software Engineering (CS 3733).

Graduate Courses

CS 5003. FOUNDATIONS OF COMPUTER SCIENCE: AN INTRODUCTION

This is the study of mathematical foundations
of computing, at a slower pace than that of CS
503 and with correspondingly fewer background
assumptions. Topics include finite automata
and regular languages, pushdown automata and
context-free languages, Turing machines and decidability,
and an introduction to computational
complexity. (Prerequisite: an undergraduate course
in discrete mathematics.)

CS 5007. INTRODUCTION TO PROGRAMMING CONCEPTS, DATA STRUCTURES AND ALGORITHMS

This is an introductory graduate course teaching core computer science topics typically found in an undergraduate Computer Science curriculum, but at a graduate-level pace. It is primarily intended for students with little formal preparation in Computer Science to gain experience with fundamental Computer Science topics.
After a review of programming concepts the focus of the course will be on data structures from the point of view of the operations performed upon the data and to apply analysis and design techniques to non-numeric algorithms that act on data structures. The data structures covered include lists, stacks, queues, trees and graphs. Projects will focus on the writing of programs to appropriately integrate data structures and algorithms for a variety of applications.
This course may not be used to satisfy degree requirements for a B.S., M.S., or Ph.D. degree in Computer Science or a minor in Computer Science. It may satisfy the requirements for other degree programs at the discretion of the program review committee for the particular degree. (Prerequisites: Experience with at least one high-level programming language such as obtained in an undergraduate programming course.)

CS 502. OPERATING SYSTEMS

The design and theory of multi-programmed operating
systems, concurrent processes, process communication,
input/output supervisors, memory
management, resource allocation and scheduling
are studied. (Prerequisites: knowledge of computer
organization and elementary data structures, and a
strong programming background.)

CS 503. FOUNDATIONS OF COMPUTER SCIENCE

This is the study of mathematical foundations
of computing. Topics include finite automata
and regular languages, pushdown automata and
context-free languages, Turing machines and decidability,
and an introduction to computational complexity. (Prerequisites: Knowledge of discrete
mathematics and algorithms at the undergraduate
level, and some facility with reading and writing
mathematical proofs.)

CS 504. ANALYSIS OF COMPUTATIONS AND SYSTEMS

The following tools for the analysis of computer
programs and systems are studied: probability,
combinatorics, the solution of recurrence
relations and the establishment of asymptotic
bounds. A number of algorithms and advanced
data structures are discussed, as well as paradigms
for algorithm design. (Prerequisites: CS 5084 or
equivalent.)

CS 5084. INTRODUCTION TO ALGORITHMS: DESIGN AND ANALYSIS

This course is an introduction to the design,
analysis and proofs of correctness of algorithms.
Examples are drawn from algorithms for many areas.
Analysis techniques include asymptotic worst
case and average case, as well as amortized analysis.
Average case analysis includes the development of
a probability model. Techniques for proving lower
bounds on complexity are discussed, along with
NP-completeness. Prerequisites: an undergraduate
knowledge of discrete mathematics and data
structures. Note: students with a strong background
in design and analysis of computer systems, at the
level equal to a BS in computer science, should not
take CS 5084 and should consider taking
CS 504 or CS 584.

CS 509. DESIGN OF SOFTWARE SYSTEMS

This course introduces students to a methodology
and specific design techniques for team-based development
of a software system. Against the back-drop
of the software engineering life-cycle, this
course focuses on the object-oriented paradigm
and its supporting processes and tools. Students
will be exposed to industrial-accepted standards
and tools, such as requirements elicitation,
specification, modeling notations, design patterns,
software architecture, integrated development environments
and testing frameworks. Students will
be expected to work together in teams in the complete
specification, implementation and testing of
a software application. Prerequisites: knowledge of
a recursive high-level language and data structures.
An undergraduate course in software engineering
is desirable.

CS 513. COMPUTER NETWORKS

This course provides an introduction to the
theory and practice of the design of computer and
communications networks, including the ISO
seven-layer reference model. Analysis of network
topologies and protocols, including performance
analysis, is treated. Current network types
including local area and wide area networks are
introduced, as are evolving network technologies.
The theory, design and performance of local area
networks are emphasized. The course includes an
introduction to queueing analysis and network
programming. (Prerequisites: knowledge of the
C programming language is assumed. CS 504 or
ECE 502 or equivalent background in CS 5084
or CS 584.)

CS 514. ADVANCED SYSTEMS ARCHITECTURE

This Course covers techniques such as caching, hierarchical memory, pipelining and parallelism, that are used to enhance the performance of computer systems. It compares and contrast different approaches to achieving high performance in machine ranging from advanced microprocessors to vector supercomputers (CRAY, CYBER). It also illustrates how these techniques are applied in massively parallel SIMD machines (DAP, Connection Machine). In each case the focus is on the combined hardware/ software performance achieved and the interaction between application demands and hardware/software capabilities. (Prerequisites: This course assumes the material covered in ECE 505. The student should also have a background in computer programming and operating systems (CS 502). Familiarity with basic probability and statistics such as ECE 502 or MA 541 is recommended.

CS 521. LOGIC IN COMPUTER SCIENCE

This course is an introduction to mathematical
logic from a computer science perspective. Topics
covered include the exploration of model theory,
proof theory, and decidability for propositional
and first-order classical logics, as well as various
non-classical logics that provide useful tools for
computer science (such as temporal and intuitionistic
logics). The course stresses the application
of logic to various areas of computer science such
as computability, theorem proving, programming
languages, specification, and verification. The specific
applications included will vary by instructor.
(Prerequisites: CS 503, or equivalent background
in basic models of computation.)

CS 522. NUMERICAL METHODS

This course provides an introduction to a broad range of modern numerical techniques that are widely used in computational mathematics, science, and engineering. It is suitable for both mathematics majors and students from other departments. It covers introductory-level material for subjects treated in greater depth in MA 512 and MA 514 and also topics not addressed in either of those courses. Subject areas include numerical methods for systems of linear numerical methods for systems of linear and nonlinear equations, interpolation and approximation, differentiation and integration, and differential equations. Specific topics include basic direct and iterative methods for linear systems; classical rootfinding methods; Newton’s method and related methods for non-linear systems; fixed-point iteration; polynomial, piecewise polynomial, and spline interpolation methods: least-squares approximation; orthogonal functions and approximation; basic techniques for numerical differentiation; numerical integration, including adaptive quadrature; and methods for initial-value problems for ordinary differential equations. Additional topics may be included at the instructor’s discretion as time permits. Both theory and practice are examined. Error estimates, rates of convergence, and the consequences of finite precision arithmetic are also discussed. Topics from linear algebra and elementary functional analysis will be introduced as needed. These may include norms and inner products, orthogonality and orthogonalization, operators and projections, and the concept of a function space. (Prerequisite: knowledge of undergraduate linear algebra and differential equations is assumed, as is familiarity with MATLAB or a higher-level programming language.)

CS 528. MOBILE AND UBIQUITOUS COMPUTING

This course acquaints participants with the fundamental concepts and state-of-the-art computer science research in mobile and ubiquitous computing. Topics covered include mobile systems issues, human activity and emotion sensing, location sensing, mobile HCI, mobile social networking, mobile health, power saving techniques, energy and mobile performance measurement studies and mobile security. The course consists of weekly presentations on current advanced literature, discussions and a term project. The term project involves
implementing research ideas on a mobile device such as a smartphone.
Prerequisite: CS 502 or an equivalent graduate level course in Operating Systems, and CS 513 or an equivalent graduate level course in Computer Networks, and proficiency in a high level programming language.

CS 529. MULTIMEDIA NETWORKING

This course covers basic and advanced topics related
to using computers to support audio and video
over a network. Topics related to multimedia
will be selected from areas such as compression,
network protocols, routing, operating systems
and human computer interaction. Students will
be expected to read assigned research papers and
complete several programming intensive projects
that illustrate different aspects of multimedia
computing. (Prerequisites: CS 502 and CS 513 or
the equivalent and strong programming skills.)

CS 534. ARTIFICIAL INTELLIGENCE

This course gives a broad survey of artificial intelligence. The course will cover methods from search, probabilistic reasoning, and learning, among other topics. Selected topics involving the applications of these tools are investigated. Such topics might include natural language understanding, scene understanding, game playing, and planning. (Prerequisites: familiarity with data structures and a high-level programming language.)

CS 535. ADVANCED TOPICS IN OPERATING SYSTEMS

This course discusses advanced topics in the
theory, design and implementation of operating
systems. Topics will be selected from such areas
as performance of operating systems, distributed
operating systems, operating systems for multiprocessor
systems and operating systems research.
(Prerequisites: CS 502 and either CS 5084,
CS 504, CS 584, or equivalent background in
probability.) See the SUPPLEMENT section of
the on-line catalog at www.wpi.edu/+gradcat
for descriptions of courses to be offered in this
academic year.

CS 539. MACHINE LEARNING

The focus of this course is machine learning for
knowledge-based systems. It will include reviews
of work on similarity-based learning (induction),
explanation-based learning, analogical and
case-based reasoning and learning, and knowledge
compilation. It will also consider other approaches
to automated knowledge acquisition as well as
connectionist learning. (Prerequisite: CS 534 or
equivalent, or permission of the instructor.)

CS 541. DEEP LEARNING

This course will offer a mathematical and practical perspective on artificial neural networks for machine learning. Students will learn about the most prominent network architectures including multi-layer feedforward neural networks, convolutional neural networks (CNNs), auto-encoders, recurrent neural networks (RNNs), and generative-adversarial networks (GANs). This course will also teach students optimization and regularization techniques used to train them -- such as back-propagation, stochastic gradient descent, dropout, pooling, and batch normalization. Connections to related machine learning techniques and algorithms, such as probabilistic graphical models, will be explored. In addition to understanding the mathematics behind deep learning, students will also engage in hands-on course projects. Students will have the opportunity to train neural networks for a wide range of applications, such as object detection, facial expression recognition, handwriting analysis, and natural language processing.
Prerequisite: Machine Learning (CS 539), and knowledge of Linear Algebra (such as MA 2071) and Algorithms (such as CS 2223).

CS 542. DATABASE MANAGEMENT SYSTEMS

An introduction to the theory and design of database
management systems. Topics covered include
internals of database management systems, fundamental
concepts in database theory, and database
application design and development. In particular,
logical design and conceptual modeling, physical
d atabase design strategies, relational data model
and query languages, query optimization, transaction
management and distributed databases.
Typically there are hands-on assignments and/or
a course project. Selected topics from the current
database research literature may be touched upon
as well. (Prerequisite: CS 5084, CS 504, or
CS 584.)

CS 543. COMPUTER GRAPHICS

This course examines typical graphics systems,
both hardware and software; design of low-level
software support for raster displays; 3-D surface
and solids modeling; hidden line and hidden
surface algorithms; and realistic image rendering
including shading, shadowing, reflection,
refraction and surface texturing. (Prerequisites:
familiarity with data structures, a recursive high-level
language and linear algebra. CS 509 would
be helpful.)

CS 544. COMPILER CONSTRUCTION

A general approach to the design of language
processors is presented without regard for either
the source language or target machine. All phases
of compilation and interpretation are investigated
in order to give the student an appreciation for the
overall construction of a compiler. Typical projects
may include implementation of a small compiler
for a recursive or special-purpose language.
(Prerequisites: knowledge of several higher-level
languages and at least one assembly language. The
material in CS 503 is helpful.)

CS 545. DIGITAL IMAGE PROCESSING

This course presents fundamental concepts of
digital image processing and an introduction to
machine vision. Image processing topics will include
visual perception, image formation, imaging
geometries, image transform theory and applications,
enhancement, restoration, encoding and
compression. Machine vision topics will include
feature extraction and representation, stereo vision,
model-based recognition, motion and image
flow, and pattern recognition. Students will be
required to complete programming assignments
in a high-level language. (Prerequisites: working
knowledge of undergraduate level signal analysis
and linear algebra; familiarity with probability
theory is helpful but not necessary.)

CS 546. HUMAN-COMPUTER INTERACTION

This course prepares graduate students for research
in human-computer interaction. Topics include
the design and evaluation of interactive computer
systems, basic psychological considerations of
interaction, interactive language design, interactive
hardware design and special input/output
techniques. Students are expected to present and
review recent research results from the literature,
and to complete several projects. (Prerequisites:
students are expected to have mature programming
skills. Knowledge of software engineering
would be an advantage.)

CS 548. KNOWLEDGE DISCOVERY AND DATA MINING

This course presents current research in Knowledge
Discovery in Databases (KDD) dealing
with data integration, mining, and interpretation
of patterns in large collections of data. Topics
include data warehousing and data preprocessing
techniques; data mining techniques for classification,
regression, clustering, deviation detection,
and association analysis; and evaluation of patterns
minded from data. Industrial and scientific applications
are discussed. Recommended background:
Background in artificial intelligence, databases,
and statistics at the undergraduate level, or
permission of the instructor. Proficiency in a high
level programming language.

CS 549. COMPUTER VISION

This course examines current issues in the computer
implementation of visual perception. Topics
include image formation, edge detection, segmentation,
shape-from-shading, motion, stereo,
texture analysis, pattern classification and object
recognition. We will discuss various representations
for visual information, including sketches
and intrinsic images. (Prerequisites: CS 534,
CS 543, CS 545, or the equivalent of one of these
courses.)

CS 557. SOFTWARE SECURITY DESIGN AND ANALYSIS

Software is responsible for enforcing many central
security goals in computer systems. These goals
include authenticating users and other external
principals, authorizing their actions, and ensuring
the integrity and confidentiality of their data.
This course studies how to design, implement,
and analyze mechanisms to enforce these goals in
both web systems and programs in traditional languages.
Topics include: identifying programming
choices that lead to reliable or flawed security
outcomes, successful and unsuccessful strategies
for incorporating cryptography into software,
and analysis techniques that identify security
vulnerabilities. The course will cover both practical
and theoretical aspects of secure software, and
will include a substantial secure software design
project. (Prerequisites: Programming and software
engineering experience (commensurate with an
undergraduate Computer Science major), and
background in foundational models of computing
systems (on par with CS 5003 or CS 503).)

CS 558. COMPUTER NETWORK SECURITY

This course covers core security threats and
mitigations at the network level. Topics include:
denial-of-service, network capabilities, intrusion
detection and prevention systems, worms, botnets,
Web attacks, anonymity, honeypots, cybercrime
(such as phishing), and legality and ethics. The
course prepares students to think broadly and concretely
about network security; it is not designed
to teach students low-level tools for monitoring
or maintaining system security. Assignments and
projects will assess each student’s ability to think
both conceptually and practically about network
security. (Prerequisites: a strong background in
computer networking and systems, either at the
undergraduate or graduate level, and moderate
programming experience.)

CS 561. ADVANCED TOPICS IN DATABASE SYSTEMS

This course covers modern database and information
systems as well as research issues in the field.
Topics and systems covered may include object oriented,
workflow, active, deductive, spatial, temporal
and multimedia databases. Also discussed
will be recent advances in database systems such
as data mining, on-line analytical processing,
data warehousing, declarative and visual query
languages, multimedia database tools, web and
unstructured data sources, and client-server and
heterogeneous systems. The specific subset of
topics for a given course offering is selected by the
instructor. Research papers from recent journals
and conferences are used. Group project required.
(Prerequisites: CS 542 or equivalent. Expected
background includes a knowledge of relational database
systems.) See the SUPPLEMENT section
of the on-line catalog at www.wpi.edu/+gradcat
for descriptions of courses to be offered in this
academic year.

CS 565. USER MODELING

User modeling is a cross-disciplinary research
field that attempts to construct models of human
behavior within a specific computer environment.
Contrary to traditional artificial intelligence
research, the goal is not to imitate human
behavior as such, but to make the machine able to
understand the expectations, goals, knowledge, information
needs, and desires of a user in terms of
a specific computing environment. The computer
representation of this information about a user is
called a user model, and systems that construct
and utilize such models are called user modeling
systems. A simple example of a user model would
be an e-commerce site which makes use of the
user’s and similar users’ purchasing and browsing
behavior in order to better understand the user’s
preferences. In this class, the focus is on obtaining
a general understanding of user modeling, and
an understanding of how to apply user modeling
techniques. Students will read seminal papers in
the user modeling literature, as well as complete a
course project where students build a system that
explicitly models the user. (Prerequisites: Knowledge
of probability.)

CS 566. GRAPHICAL MODELS FOR REASONING UNDER UNCERTAINTY

This course will introduce students to graphical
models, such as Bayesian networks, Hidden Markov
Models, Kalman filters, particle filters, and
structural equation models. Graphical models are
applicable in a wide variety of work in computer
science for reasoning under uncertainty such as
user modeling, speech recognition, computer
vision, object tracking, and determining a robot’s
location. This course will cover 1) using data to
estimate the parameters and structure of a model
using techniques such as expectation maximization,
2) understanding techniques for performing
efficient inference on new observations such as
junction trees and sampling, and 3) learning about
evaluation techniques to determine whether a
particular model is a good one. (Prerequisites: CS
534 Artificial Intelligence or permission of the
instructor.)

CS 567. EMPIRICAL METHODS FOR HUMAN-CENTERED COMPUTING

This course introduces students to techniques for
performing rigorous empirical research in computer
Science. Since good empirical work depends
on asking good research questions, this course will
emphasize creating conceptual frameworks and using
them to drive research. In addition to helping
students understand what makes a good research
question and why, some elementary statistics will
be covered. Furthermore, students will use and
implement computationally intensive techniques
such as randomization, bootstrapping, and
permutation tests. The course also covers experiments
involving human subjects, and some of the
statistical and non-statistical difficulties researchers
often encounter while performing such work
(e.g., IRB (Institutional Review Board), correlated
trials, and small sample sizes). While this course
is designed for students in Human Computer
Interaction, Interactive Media and Game Development,
and Learning Sciences and Technologies,
it is appropriate for any student with programming
experience who is doing empirical research.
(Prerequisites: MA 511 Applied Statistics for Engineers
and Scientists or permission of the instructor.)

CS 568. ARTIFICIAL INTELLIGENCE FOR ADAPTIVE EDUCATIONAL TECHNOLOGY

Students will learn how to enable educational
technology to adapt to the user and about typical
architectures used by existing intelligent tutoring
systems for adapting to users. Students will
see applications of decision theoretic systems,
reinforcement learning, Markov models for action
selection, and Artificial Intelligence (AI) planning.
Students will read papers that apply AI techniques
for the purpose of adapting to users. Students will
complete a project that applies these techniques to
build an adaptive educational system. (Prerequisites:
CS 534 Artificial Intelligence or permission
of the instructor.)

CS 571. CASE STUDIES IN COMPUTER SECURITY

This course examines security challenges and
failures holistically, taking into account technical
concerns, human behavior, and business decisions.
Using a series of detailed case studies, students will
explore the interplay among these dimensions in
creating secure computing systems and infrastructure.
Students will also apply lessons from the
case studies to emerging secure-systems design
problems. The course requires active participation
in class discussions, presentations, and writing
assignments. It does not involve programming,
but assumes that students have substantial prior
experience with security protocols, attacks, and
mitigations at the implementation level. This
course satisfies the behavioral component of the
MS specialization in computer security. (Prerequisites:
A prior course or equivalent experience in
technical aspects of computer security, at either
the software or systems level.)

CS 573. DATA VISUALIZATION

This course exposes students to the field of data visualization, i.e., the graphical communication of data and information for the purposes of presentation, confirmation, and exploration. The course introduces the stages of the visualization pipeline. This includes data modeling, mapping data attributes to graphical attributes, visual display techniques, tools, paradigms, and perceptual issues. Students learn to evaluate the effectiveness of visualizations for specific data,
task, and user types. Students implement visualization algorithms and undertake projects involving the use of commercial and public-domain visualization tools. Students also read papers from the current visualization literature and do classroom presentations.
Prerequisite: a graduate or undergraduate course in computer graphics.

CS 577. ADVANCED COMPUTER AND COMMUNICATIONS NETWORKS

This course covers advanced topics in the theory, design and performance of computer and communications networks. Topics will be selected from such areas as local area networks, metropolitan area networks, wide area networks, queueing models of networks, routing, flow control, new technologies and protocol standards. The current literature will be used to study new networks concepts and emerging technologies. (Prerequisite: CS 513/ECE 506 and CS 533/ECE 581).

CS 578. CRYPTOGRAPHY AND DATA SECURITY

This course gives a comprehensive introduction
to the field of cryptography and data security.
The course begins with the introduction of
the concepts of data security, where classical
algorithms serve as an example. Different attacks
on cryptographic systems are classified. Some
pseudo-random generators are introduced. The
concepts of public and private key cryptography
are developed. As important representatives for
secret key schemes, DES and IDEA are described.
The public key schemes RSA and ElGamal, and
systems based on elliptic curves are then developed.
Signature algorithms, hash functions, key
distribution and identification schemes are treated
as advanced topics. Some advanced mathematical
algorithms for attacking cryptographic schemes
are discussed. Application examples will include
a protocol for security in a LAN and a secure
smart card system for electronic banking. Special
consideration will be given to schemes which
are relevant for network environments. For all
schemes, implementation aspects and up-to-date
security estimations will be discussed. (Prerequisites:
Working knowledge of C; an interest in
discrete mathematics and algorithms is highly
desirable. Students interested in a further study of
the underlying mathematics may register for MA
4891 [B term], where topics in modern algebra
relevant to cryptography will be treated.)

CS 582. BIOVISUALIZATION

This course will use interactive visualization to model and analyze biological information,
structures, and processes. Topics will include the
fundamental principles, concepts, and techniques
of visualization (both scientific and information
visualization) and how visualization can be used
to study bioinformatics data at the genomic,
cellular, molecular, organism, and population
levels. Students will be expected to write small to
moderate programs to experiment with different
visual mappings and data types. (Prerequisite:
strong programming skills, an undergraduate or
graduate course in algorithms, and one or more undergraduate biology courses.) Students may not
receive credit for both CS 582 and CS 4802.

CS 583. BIOLOGICAL AND BIOMEDICAL DATABASE MINING

This course will investigate computational
techniques for discovering patterns in and across
complex biological and biomedical sources including
genomic and proteomic databases, clinical
databases, digital libraries of scientific articles,
and ontologies. Techniques covered will be drawn
from several areas including sequence mining,
statistical natural language processing and text
mining, and data mining. (Prerequisite: strong
programming skills, an undergraduate or graduate
course in algorithms, an undergraduate course in
statistics, and one or more undergraduate biology
courses.) Students may not receive credit for both
CS 583 and CS 4803.

CS 584. ALGORITHMS: DESIGN AND ANALYSIS

This covers the same material as CS 5084 though at a more advanced level. As background, students
should have experience writing programs in a
recursive, high-level language and should have
the background in mathematics that could be
expected from a BS in Computer Science.

CS 585. BIG DATA MANAGEMENT

Emerging applications in science and engineering disciplines generate and collect data at unprecedented speed, scale, and complexity that need to be managed and analyzed efficiently. This course introduces the emerging techniques and infrastructures developed for big data management including parallel and distributed database systems, map-reduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloud-based computing. Query processing, optimization, access methods, storage layouts, and energy
management techniques developed on these infrastructures will be covered. Students are expected to engage in hands-on projects using one or more of these technologies. Prerequisites: A beginning course in databases at the level of CS4432 or equivalent knowledge, and programming experience.

CS 586. BIG DATA ANALYTICS

Innovation and discoveries are no longer hindered by the ability to collect data, but the ability to summarize, analyze, and discover knowledge from the collected data in a scalable fashion. This course covers computational techniques and algorithms for analyzing and mining patterns in large-scale datasets. Techniques studied address data analysis issues related to data volume (scalable and distributed analysis), data velocity (high-speed data streams), data variety
(complex, heterogeneous, or unstructured data), and data veracity (data uncertainty). Techniques include mining and machine learning techniques for complex data types, and scaleup and scale-out strategies that leverage big data infrastructures. Real-world applications using these techniques, for instance social media analysis and scientific data mining, are selectively discussed. Students are expected to engage in hands-on projects using one or more of these technologies. Prerequisites: A beginning course in databases and a beginning course in data
mining, or equivalent knowledge, and programming experience.

DS 501. INTRODUCTION TO DATA SCIENCE

This course provides an overview of Data Science, covering a broad selection of key challenges in and methodologies for working with big data. Topics to be covered include data collection, integration, management, modeling, analysis, visualization, prediction and informed decision making, as well as data security and data privacy. This introductory course is integrative across the core disciplines of Data Science, including databases, data warehousing, statistics, data mining, data visualization, high performance computing, cloud computing, and business
intelligence. Professional skills, such as communication, presentation, and storytelling with data, will be fostered. Students will acquire a working knowledge of data science through hands-on projects and case studies in a variety of business, engineering, social sciences, or life sciences domains. Issues of ethics, leadership, and teamwork are highlighted. Prerequisites:None beyond meeting the Data Science admission criteria.