write a Ph.D. thesis that must be approved by a dissertation guidance committee and present an oral thesis defense, and satisfy all School of Engineering requirements for the Ph.D. degree, as described in the NYU Tandon School of Engineering bulletin, including graduate study duration, credit points, GPA, and time-to-degree requirements In our MSc program, you’ll learn to identify critical information and data so that healthcare professionals, patients and lay people can make effective decisions. You can study in either our on-campus stream or the distributed (online) stream. You’ll complete coursework and complete either a thesis or a research project Programme overview Summary Our Master of Science is organized in two tracks: Data Science & Engineering, centered on the management, analysis, and visualization of massive amounts of digital data for artificial intelligence, visual computing, data intensive computing, or business analytics; Software Security & Engineering, devoted to the development of high-quality,
PhD Syllabus Subjects, Entrance Exam Syllabus, List of PhD Courses
While the BS degree in Computer Science at Case Western Reserve University was approved inthe Department of Computer and Data Sciences CDS was recently established in Computer Science is the study of the theory, practice, and application of computer systems. Data Science is an interdisciplinary field that utilizes computer systems, computational algorithms, and statistical methods to manage, analyze, thesis data warehousing phd, and visualize data from different domains in order to extract information and knowledge from data.
Computer Science and Data Science are at the heart of modern technology with applications in many disciplines, thesis data warehousing phd. They thesis data warehousing phd have a profound impact on our society and drive job creation. Starting salaries in our fields are consistently ranked at the top of all college majors. Our graduates work in cutting-edge companies--from giants to start-ups, in a variety of technology sectors, including computer and internet, business and finance, healthcare and medical devices, energy, and consulting.
CDS also offers minors in Computer Science, Computer Gaming, and Artificial Intelligence. The CDS department is dedicated to developing high-quality graduates who will take positions of leadership as their careers advance. We recognize that the increasing role of technology in virtually every facet of our society, life, thesis data warehousing phd, and culture makes it vital that our students have access to progressive and cutting-edge higher education programs.
The program values for all of thesis data warehousing phd degree programs in the department are:. Stressing excellence in these core values helps to ensure that our graduates are valued and contributing members of our global society and that they will carry on the tradition of industrial and academic leadership established by our alumni.
Our goal is to graduate students who have fundamental technical knowledge of their profession and the requisite technical breadth and communications skills to become leaders in creating the new techniques and technologies which will advance their fields. To achieve this goal, the department offers a wide range of technical specialties consistent with the breadth of computer science and data science, including recent developments in the fields. Because of the rapid pace of advancement in these fields, our degree programs emphasize a broad and foundational science and technology background that equips students for future developments.
Our programs include a wide range of electives and our students can also develop individualized programs that can combine computer and data thesis data warehousing phd with other disciplines, thesis data warehousing phd. At Case Western, we thrive to provide outstanding educational experiences for both our undergraduate and graduate students, while performing cutting edge research in:.
Erman Ayday, PhD Georgia Institute of Technology Assistant Professor Cryptography, network security, trust and reputation management, big data analytics. Vipin Chaudhary, PhD The University of Texas at Austin Kevin J. Kranzusch Professor and Chair High performance computing, machine learning, computational and data science, computer aided diagnosis and interventions, and quantum computing, thesis data warehousing phd.
Harold S. Connamacher, PhD University of Toronto Robert J. Herbold Associate Professor in Transformative Teaching Constraint satisfaction problems, graph theory, random structures, and algorithms.
Mehmet Koyuturk, PhD Purdue University Professor Bioinformatics and computational biology, computational modeling and algorithm development for systems biology, integration, mining and analysis of biological data, algorithms for distributed systems. Michael Lewicki, PhD California Institute of Technology Professor Computational perception and scene analysis, visual representation and processing, auditory representation and analysis. Jing Li, PhD University of California, Riverside Leonard Case Jr.
Professor Computational biology and bioinformatics, data mining and machine learning, data science and analytics, algorithms. Vincenzo Liberatore, PhD Rutgers University Associate Professor Distributed systems, Internet computing, randomized algorithms. Orhan Ozguner, PhD Case Western Reserve University Assistant Professor Algorithms, data science, data structure, programming.
Andy Podgurski, PhD University of Massachusetts, Amherst Professor Software engineering methodology and tools, especially use of data mining, machine learning, and program analysis techniques in software testing, fault detection and localization, reliable engineering and software security, electronic medical records, privacy.
Michael Rabinovich, PhD University of Washington Professor Computer networks, thesis data warehousing phd, distributed systems, Internet security and performance. Soumya Ray, PhD University of Wisconsin, Madison Associate Professor Artificial intelligence, machine learning, reinforcement learning, thesis data warehousing phd, automated planning, applications to interdisciplinary problems including medicine and bioinformatics.
An Wang, PhD George Mason University Assistant Professor Thesis data warehousing phd and network security. Yinghui Wu, PhD University of Edinburgh UK Assistant Professor Data science. Xusheng Xiao, PhD North Carolina State University Assistant Professor Software engineering, computer security.
Shuai Xu, PhD Florida International University Assistant Professor Algorithms and theory. Yanfang Fanny Ye, PhD Xiamen University Theodore L. and Dana J.
Schroeder Associate Professor Cybersecurity, data mining, machine learning, health intelligence. Cenk Cavusoglu, PhD University of California, thesis data warehousing phd, Berkeley Nord Professor of Engineering. Michael Fu, PhD Timothy E, thesis data warehousing phd. and Allison L. Schroeder Assistant Professor CSE-ECSE. Aziz Nazha, PhD Assistant Professor SOM, CCF-Ctr of Clinical Artificial Intelligence. Satya Sahoo, PhD Associate Professor SOM-Dept.
Gultekin Ozsoyoglu, PhD University of Alberta, Canada Emeritus Professor Graph databases and data mining problems in metabolic networks, metabolomics, and systems biology, bioinformatics, web data mining. Meral Ozsoyoglu, PhD University of Alberta, Canada Emeritus Professor Database systems, database query languages and optimization, data models, index structures, bioinformatics, medical informatics.
These programs provide students with a strong background in the fundamentals of mathematics and science. Students can use their technical and open electives to pursue concentrations in software engineering, algorithms, artificial intelligence, databases, thesis data warehousing phd, data mining, bioinformatics, security, computer systems, and computer networks. In addition to an excellent technical education, all students in the department are exposed to societal issues, ethics, professionalism, and have the opportunity to develop leadership and creativity skills.
The Data Science and Analytics BS program provides our students with a broad foundation in the field and with the instruction, skills, and experience needed to understand and handle large amounts of data to derive actionable information.
The degree program has a unique focus on real-world data and real-world applications. This major is one of the first undergraduate programs nationwide with a curriculum that includes mathematical modeling, computation, data analytics, visual analytics and project-based applications — all elements of the future emerging field of data science.
Graduates from the Data Science and Analytics Bachelor of Science program will be prepared to: 1. Analyze real-world problems and create data-driven solutions based on the fundamentals of data science and computing.
Work effectively, thesis data warehousing phd, and ethically. Assume positions of leadership in industry, academia, public service, and entrepreneurship. Successfully progress in advanced degree programs in data science, computing, and related fields. As preparation for achieving the above educational objectives, the Bachelor of Science degree program in Data Science and Analytics is designed so that students attain the ability to: 1.
Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions. Communicate effectively in a variety of professional contexts. Recognize professional responsibilities and thesis data warehousing phd informed judgments in computing practice based on legal and ethical principles. The major requires completion The major requires completion of the university general education requirements and the engineering general education requirements as modified for the Bachelor of Science in Data Science and Analytics degree, and the following courses:.
Two of STATSTATSTATand STAT 3 units each. Core courses provide our students with a strong background in foundations and analytics.
Each student must supplement their competence in foundational technical areas by taking at least three additional courses, totaling at least nine credit hours from the following list. The following list is organized in topical areas for informational purposes only; foundation courses may come from the same or from different areas.
Data science graduates are expected to be knowledgeable in a wide range of areas of applications of the data science profession. The breadth requirement is satisfied by choosing at least two courses totaling at least six credit hours from the following list. Two more courses from the core, foundations, and applications lists for at least six credit hours.
The combination of core, foundations, and application courses with technical and open electives makes it possible to achieve a minor in fields as different as Economics and Biology. Interested students should contact their advisors. The following is a suggested program of study. Current students should always consult their advisors and their individual graduation requirement plans as tracked in SIS.
Engineering General Education Requirement. An undergraduate minor in applied data science is administered in the Materials Science and Engineering Department. The Bachelor of Science degree program in computer science is designed to give a student a strong background in the fundamentals of mathematics and computer science.
A graduate of this program should be able to use these fundamentals to analyze and evaluate software systems and the underlying abstractions upon which they are based. A graduate should also be able to design and implement software systems that are state-of-the-art solutions to a variety of computing problems; this includes problems that are sufficiently complex to require the evaluation of design alternatives and engineering trade-offs. In addition to these program-specific objectives, all students in the Case School of Engineering are exposed to societal issues, thesis data warehousing phd, and are provided opportunities to develop leadership skills.
The mission of the Bachelor of Science degree program in computer science is to graduate students who have fundamental technical knowledge of their profession and the requisite technical breadth and communications skills to become leaders in creating the new techniques and technologies which will advance the field of computer science and its application to other disciplines.
As preparation for achieving the above educational objectives, the Bachelor of Science degree program in computer science is designed so that students attain the ability to:. Core and breadth courses provide our students with the flexibility to work across many disciplines and prepare them for a variety of professions. Our curriculum is designed to teach fundamental skills and knowledge needed by all CS graduates while providing the greatest flexibility in selecting topics.
Students are also required to develop depth in at least one of the thesis data warehousing phd technical areas: software engineering; algorithms and theory; computer systems, networks, and security; databases and thesis data warehousing phd mining; bioinformatics; or artificial intelligence. Each student is required to complete a total of 20 computer science and computer science related courses, totaling at least 63 credits.
The remaining courses needed to fulfill the 20 course requirement may come from the computer science breadth courses, courses of any computer science depth area, and up to 6 of the 20 courses may come from the list of approved technical electives with at most two group 2 courses.
Some courses appear in more than one list. The same course may be used to satisfy multiple requirements of the core, computer science breadth and depth requirements, but courses may not be double counted for the purpose of achieving 20 separate computer science courses and 63 credits.
BS students are required to complete at least 5 of the thesis data warehousing phd following computer science breadth courses. Students pursuing the BS degree must demonstrate competence in the principles and practices of secure computing by completing one of the following courses as part of their 20 computer science or computer science related courses.
This course may be double counted as a computer science depth course, as appropriate. There is no secure computing requirement for students pursuing the BA degree. Students pursuing the BS degree must demonstrate a depth of competence in one of the technical areas listed below. To complete the depth requirement, students must complete at least four courses in one of the depth areas, including all thesis data warehousing phd courses.
Recommended general background courses are listed following each area where applicable. Recommended preparation: MATH Introduction to Probability.
Recommended breadth and preparation: STAT Data Analysis and Linear Models or PQHS Statistical Methods ISYBB A Survey of Bioinformatics: Technologies in BioinformaticsSYBB B Survey of Bioinformatics: Data Integration in BioinformaticsSYBB C Survey of Bioinformatics: Translational Bioinformatics, BIOL Genes, Evolution and Ecology.
Recommended breadth and preparation: MATH Introduction to Probabilityand either ECSE Convex Optimization for Engineering or CSDS Advanced Algorithms.
PhD Thesis Defense: Michael Everett
, time: 46:16Computer Science | Università di Genova
Programme overview Summary Our Master of Science is organized in two tracks: Data Science & Engineering, centered on the management, analysis, and visualization of massive amounts of digital data for artificial intelligence, visual computing, data intensive computing, or business analytics; Software Security & Engineering, devoted to the development of high-quality, Trending thesis topics in cloud computing; Data aggregation as a thesis topics in Big Data; Research topics in Software Engineering; Data Warehousing. Data Warehousing is the process of analyzing data for business purposes. Data warehouse store integrated data from multiple sources at a single place which can later be retrieved for making reports The PhD student must write a formal thesis proposal and defend it in an oral presentation to his or her Dissertation Advisory Committee. Normally this is done within a year of advancing to candidacy. data warehousing, machine learning, statistics, algorithms, data visualization, and high-performance computing. This course is an introduction
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