Sharad Agarwal Senior Principal Researcher. Vishesh Agarwal Software Engineer. Janhavi Agrawal Research Software Engineer. Ankita Agrawal Data Scientist. Faisal Ahmed. Hao Ai Senior Data Scientist.
She also served as IBM's strategist for brain-inspired computing where she led the commercialization strategy for IBM's brain inspired computing hardware and ecosystem. In this role, she developed the first commercial uses of brain inspired computing hardware. Prior to that, she led a research team in enterprise storage systems which created technologies to improve the reliability and performance of IBM's enterprise storage products, including the first deployment of commodity flash memory in an enterprise storage system.
She has also held several key technical roles in IBM Research in the areas of microprocessor architecture and design, EDA tools, and formal verification. The concentration prepares students for careers in business analytics with a focus on practical applications in financial operations, investment, and risk mitigation strategy development.
This concentration focuses on analytics for health care. It provides coverage across several data analytics areas with specific application to the health care domain. The focus of this concentration is driven by sensors that collect the staggering amount of data that exists today. The Internet of Things IoT is expanding, at a geometric level, the number of devices that collect, forward, and offer for analysis data.
Analog and digital sensing design and deployment, hardware options, power consumption, security, sampling and quantization, Fourier transform, time analysis, and synchronization are topics covered in this concentration. The ultimate goal of analytics of Big Data is to derive value by suggesting effective actions for the future.
Predictive analytics focuses on the methods for deciding on the best course of action, taking into account possible constraints and risks. The concentration will provide students with skills that drive effective decision making and optimization.
Students will learn the techniques to analyze both structured and unstructured data to derive meaningful knowledge, which will be useful for developing effective strategies and making optimal decisions. The concentration emphasizes both analytical and practical aspects of predictive analytics. Students are expected to master the practical aspects of modeling and methods for optimization. Students are also expected to demonstrate proficiency in decision making, design of decision support systems, and risk analysis.
The program prepares students for careers in big data analytics with a focus on strategic decision making in practical applications including financial engineering, health care, transportation, and intelligence. Provides students with skills necessary for gaining insight from data. Enables students to evaluate large data-sets from a rigorous statistical perspective, including theoretical, computational, and analytical techniques. Emphasis will be placed on developing deep analytical talent in the two areas of statistical modeling and data visualization.
To gain knowledge from these data and hence inform decisions, elucidation of the core interactions and relationships must be done in a manner that acknowledges uncertainties in order to both minimize false signals and maximize true discoveries. Statistical modeling does exactly this — it accounts for uncertainty while identifying relationships.
Visualization is often a critical component of modeling, but visualization also stands alone as an important tool for presentation of information, decision analysis, and process improvement.
Policies governing all graduate degrees are in the catalog under AP. They may begin taking graduate courses after completing 75 undergraduate credits and successfully completing CS Computer Systems and Programming.
The following graduate courses can replace the corresponding undergraduate courses. Students in the Computer Game Design and Geography concentrations of the Applied Computer Science, BS program may also register for one or both of the following courses:. Students must apply for degree conferral the semester before they expect to complete their BS requirements. Notes: Computer science majors may use this course to satisfy the Mason Core synthesis requirement, so long as they have not previously taken CS for credit.
Recommended Prerequisite: Junior standing at least 60 credit hours. CS Data Structures. Focuses on object-oriented programming with an emphasis on tools and techniques for developing moderate to large programs.
Topics include use and implementation of linear and nonlinear data structures and the design and analysis of elementary algorithms. CS Software Engineering. An introduction to concepts, methods, and tools for the creation of large-scale software systems.
Methods, tools, notations, and validation techniques to analyze, specify, prototype, and maintain software requirements. Introduction to object-oriented requirements modeling, including use of case modeling, static modeling, and dynamic modeling using the Unified Modeling Language UML notation.
Concepts and methods for the design of large-scale software systems. Fundamental design concepts and design notations are introduced. A study of object-oriented analysis and design modeling using the UML notation.
Students participate in a group project on software requirements, specification, and object-oriented software design. Equivalent to SWE CS Introduction to Game Design.
Game design, in various electronic entertainment technologies, involves a diverse set of skills and backgrounds from narrative and art to computer programming.
Surveys the technical aspects of the field, with an emphasis on programming. CS Formal Methods and Models.
Abstract concepts that underlie much advanced work in computer science, with major emphasis on formal languages, models of computation, logic, and proof strategies. In-depth study of software design and implementation using a modern, object-oriented language with support for graphical user interfaces and complex data structures. Topics covered will be specifications; design patterns; and abstraction techniques; including typing, access control, inheritance, and polymorphism.
Students will learn the proper engineering use of techniques such as information hiding, classes, objects, inheritance, exception handling, event-based systems, and concurrency. CS Visual Computing. Focuses on programming essential mathematical and geometric concepts underlying computer graphics. Covers fundamental topics in computational geometry, 3D modeling, graphics algorithms, and graphical user interfaces using both 2D and 3D implementations.
Reinforces object-oriented programming practices. CS Computer Systems and Programming. Introduces students to computer systems from a programmer's perspective. Foundation for courses on compilers; networks; operating systems; and computer architecture, where a deeper understanding of systems-level issues is required. This course introduces students to the research and project design process within the computing field. Students will learn about the tools of the trade, work through design principles beginning with the articulation of a question, reviewing methods of exploration, gathering evidence, communicating results, and assessing and evaluating research or project outcomes.
CS Advanced Programming Lab. Programming-intensive lab course. Students refine problem-solving and programming skills while gaining experience in teamwork. Focuses on data structures, recursion, backtracking, dynamic programming, and debugging. Central focus is applying familiar and new algorithms and data structures to novel circumstances. May be repeated within the degree for a maximum 3 credits. Special and emerging topics of interest to computer science undergraduates.
Notes: May be repeated if topics are substantially different. May be repeated within the term for a maximum 3 credits. Recommended Prerequisite: Additional pre-requisites will vary by topic. CS Special Topics. Special and emerging topics in computer science or closely related disciplines. May be repeated within the term for a maximum 6 credits. Recommended Prerequisite: Additional prerequisites will vary by topic. Introduction to technologies and techniques used in modern computer games.
Teams will explore the various facets of a complete design using sophisticated tools. Includes a project in which a game is prototyped; this prototype and initial design will serve as the starting point for the project in CS Project-orientated continuation of CS with an emphasis on the implementation of a complete game. Survey of basic programming language processors and software development tools such as assemblers, interpreters, and compilers.
Topics include design and construction of language processors, formal syntactic definition methods, parsing techniques, and code-generation techniques. CS Introduction to Computational Biology. Introduces computational methods in molecular biology. Covers a broad array of topics in bioinformatics and computational biology.
Organized as 3 four-week modules intended to capture the current classification of bioinformatics and computational biology methods, thereby providing students with a broad view of the field. Recommended Prerequisite: C or better in CS CS Computational Methods for Genomics. Fundamental principles and techniques for implementing computational algorithms to solve problems in biology arising from the need to process large volumes of genomic information.
Topics include sequence analysis, alignment, and assembly, gene prediction, and knowledge-based protein structure prediction. Projects involve designing and programming basic alignment and prediction methods. CS Database Concepts. Covers basics to intermediate knowledge for the design, implementation, and use of relational database systems. Students will practice to design, develop, and implement a relational ORACLE database and use the database for queries, transaction processing, and report generation.
CS Computer Graphics. Basic graphics principles and programming. Topics include scan conversion, transformation, viewing, lighting, blending, texture mapping, and some advanced graphics techniques.
CS Computer Communications and Networking. Topics include role of various media and software components, local and wide area network protocols, network performance, and emerging advanced commercial technologies. CS Comparative Programming Languages. Key programming mechanisms described independently of particular machines or languages, including control, binding, procedural abstraction, types, and concurrency.
Includes basic programming competence in several different types of programming languages, including a language that provides concurrency. CS Computer Systems Architecture. Computer subsystems and instruction set architectures. Single-cycle, multiple-cycle, and pipeline architectures.
Memory hierarchy, cache, and virtual memory input-output processing. CS Secure Programming and Systems. Fundamental principles and techniques for implementing secure computer systems. Topics include security and cryptography basics, vulnerability analysis, secure software development, and distributed system security. Projects involve designing and programming basic security tools, secure programs, and distributed systems. As an example, analysts do not have to be malware reverse engineers, but they must at least understand that work and know what data can be sought.
This section continues from the previous one in identifying key collection sources for analysts. The considerable amount of what is commonly referred to as open-source intelligence OSINT is also presented. Students will also structure the data to be exploited for purposes of sharing internally and externally.
With great data comes great analysis expectations. Now that students are familiar with different sources of intrusions and collection, it is important to apply analytical rigor to how this information is used in order to satisfy intelligence requirements for long-term analysis.
In this section students will learn how to structure and store their information over the long term using tools such as MISP; how to leverage analytical tools to identify logical fallacies and cognitive biases; how to perform structured analytic techniques in groups such as analysis of competing hypotheses; and how to cluster intrusions into threat groups.
Intelligence is useless if not disseminated and made useful to the consumer. In this section students will learn about dissemination at the various tactical, operational, and strategic levels. Students will also learn about state adversary attribution, including when it can be of value and when it is merely a distraction. The section will finish with a discussion on consuming threat intelligence and actionable takeaways so that students will be able to make significant changes in their organizations once they complete the course.
The FOR capstone focuses on analysis. Students will be placed on teams, given outputs of technical tools and cases, and work to piece together the relevant information from a single intrusion that enables them to unravel a broader campaign. Students will get practical experience satisfying intelligence requirements ranging from helping the incident response team to satisfying state-level attribution goals.
This analytical process will put the students' minds to the test instead of placing a heavy emphasis on using technical tools.
At the end of the day the teams will present their analyses on the multi-campaign threat they have uncovered. In our complex and ever changing threat landscape it is important for all analysts to earn the GCTI whether or not they are directly involved in generating intelligence. Technical training has become common and helped further our security field the same has not been true for structured analysis training, until now.
Many of security practitioners consider themselves analysts but have not fully developed analysis skills in a way that can help us think critically and amplify our technical knowledge. It is in this structured analysis that we can challenge our biases, question our sources, and perform core skills such as intrusion analysis to better consume and generate intelligence. It is through cyber threat intelligence that organizations and their personnel can take on focused human adversaries and ensure that security is maintained.
Intelligence impacts us all and we are furthering the field together in a way that will extraordinarily limit the success of adversaries. FOR is a good course for anyone who has had security training or prior experience in the field. Students should be comfortable with using the command line in Linux for a few labs though a walkthrough is provided and be familiar with security terminology.
Students who have not taken any of the above courses but have real-world experience or have attended other security training, such as any other SANS class, will be comfortable in the course. New students and veterans will be exposed to new concepts given the unique style of the class focused on analysis training.
A properly configured system is required to fully participate in this course. If you do not carefully read and follow these instructions, you will likely leave the class unsatisfied because you will not be able to participate in hands-on exercises that are essential to this course. Therefore, we strongly urge you to arrive with a system meeting all the requirements specified for the course. This is common sense, but we will say it anyway.
Back up your system before class. Better yet, do not have any sensitive data stored on the system.
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