A data model (or datamodel) is an However, it was Terry Halpin's PhD thesis that created the formal foundation on which Object-Role Modeling is based. Bill Kent, Such an information model is an integration of a model of the facility with 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 Master of Philosophy (MPhil) and Doctor of Philosophy (PhD) Programs in Data Science and Analytics aim to facilitate close integration of statistical analytics, logical reasoning, and computational intelligence in the study of data processing and analytics
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Master of Philosophy in Data Science and Analytics Doctor of Philosophy in Data Science and Analytics. In the digital era, following advancements made in innovative technologies, data handling is growing at an unprecedented pace. The data-driven world opens tremendous possibilities and opportunities for companies and businesses for all industries as they can make use of the data information to create values for their business.
As a disruptive consequence of the digital revolution, data science and analytics has become an emerging and cross-disciplinary field that requires knowledge and skills in many areas such as computer science, statistics and mathematics. The Master of Philosophy MPhil and Doctor of Philosophy PhD Programs in Data Science and Analytics aim to facilitate close integration of statistical analytics, logical reasoning, data integration phd thesis, and computational intelligence in the study of data processing and analytics.
The programs will provide rigorous research training that prepares students to become knowledgeable researchers who are conversant in applying logic, mathematics, algorithms and computing power in the process of examining and analyzing data in academia or industry so as to derive valuable insights for making better decisions. The MPhil Program aims to expose students to issues involved in the development of scientific, educational and commercial applications of data science and analytics.
A graduate of the MPhil program should demonstrate a good working knowledge of issues in the discipline. He or she should data integration phd thesis capable of synthesizing and creating new knowledge, making contribution to the field, data integration phd thesis.
The PhD Program aims to develop the skills needed for students to identify theoretical research issues related to practical applications, formulate and undertake research that addresses issues identified, and independently find a data science and analytics related solution.
A PhD graduate is expected to demonstrate mastery of knowledge in the discipline and to synthesize and create new knowledge, making original and substantial scientific contribution to the discipline.
MPhil: 15 credits PhD: 21 credits. Students who have taken equivalent courses at HKUST or other recognized universities may be granted credit transfer on a case-by-case basis, up to a maximum of 3 credits for MPhil students, and 6 credits for PhD students. All students are required to complete either IIMP or IIMP Students may complete the remaining courses as part of the credit requirements, as requested by the Program Planning Committee.
Students are data integration phd thesis to complete at least one Hub core course 2 credits from the Information Hub and at least one Hub core course 2 credits from other Hubs. MPhil: minimum 9 credits of coursework PhD: minimum 15 credits of coursework. Under this requirement, each student is required to take one required course and other electives to form an individualized curriculum relevant to the cross-disciplinary thesis research. Only one Independent Study course may be used to satisfy the course requirements, data integration phd thesis.
To ensure that students will take appropriate courses to equip them with needed domain knowledge, data integration phd thesis, each student has a Program Planning Committee to approve the courses to be taken soonest after program commencement and no later than the end of the first year.
Depending on the approved curriculum, individual students may be required to complete additional credits beyond the minimal credit requirements. To meet individual needs, students will be taking courses in different areas, which may include but not limited to courses and areas listed below. Individual students may be required to take foundation data integration phd thesis to strengthen their academic background and research capacity in related areas, which will be specified by the Program Planning Committee.
The credits earned cannot be counted toward the credit requirements. All full-time RPg students are required to complete PDEV The course is composed of a hour training offered by the Center for Education Innovation CEIand session s of instructional delivery to be assigned by the respective departments. Upon satisfactory completion of the training conducted by CEI, MPhil students are required to give at least one minute session of instructional delivery in front of a group of students for one term.
PhD students are required to give at least one such session each in two different terms. The instructional delivery will be formally assessed. Students are required to complete PDEV The 1 credit earned from PDEV cannot be counted toward the credit requirements.
PhD students who are HKUST MPhil graduates and have completed PDEV or other professional development courses offered by the University before may be exempted from taking PDEVsubject to prior approval of the Program Planning Committee.
Students are required to complete INFH The 1 credit earned from INFH cannot be counted toward the credit requirements. PhD students who are HKUST MPhil graduates and have completed INFH or other professional development courses offered by the University before may be exempted from data integration phd thesis INFHsubject to prior approval of the Program Planning Committee. Full-time RPg students are required to take an English Language Proficiency Assessment ELPA Speaking Test administered by the Center for Language Education before the start of their first term of study.
Students whose ELPA Speaking Test score is below Level 4, or who failed to take the test in data integration phd thesis first term of study, are required to take LANG until they pass the course by attaining at least Level 4 in the ELPA Speaking Test before graduation. The 1 credit earned from LANG cannot be counted toward the credit requirements.
Students are required to take one of the above three courses. The credit earned cannot be counted toward the credit requirements. Students can be exempted from taking this course with the approval of the Program Planning Committee. Students are required to complete DSAA and DSAA in two terms. PhD students are required to pass a qualifying examination to obtain PhD candidacy following established policy. To qualify for admission, applicants must meet all of the following requirements.
Admission is selective and meeting these minimum requirements does not guarantee admission. Applicants have to fulfill English Language requirements with one of the following proficiency attainments:. TOEFL-Revised paper-delivered test: 60 total scores for Reading, Listening and Writing sections. IELTS Academic Module : Overall score: 6. they obtained the bachelor's degree or equivalent from an institution where the medium of instruction was English.
More about HKUST. University News Academic Departments A-Z Life HKUST Library. Programs and Course Catalog, data integration phd thesis. Academic Regulations.
Office of the Postgraduate Studies. Connect with Us. Master of Philosophy and Doctor of Philosophy Programs in Data Science and Analytics. Award Data integration phd thesis. Data Science and Analytics Thrust Area Information Hub. Program Director: Prof Lei CHEN, Chair Professor of Computer Science and Engineering. LEARNING OUTCOMES. On successful completion of the MPhil program, graduates data integration phd thesis be able to: Demonstrate critical thinking and analytical skills essential for solving real data science problems; Apply a range of qualitative and quantitative research methods for data science and analytics; and Translate and transform advanced research techniques effectively into data science practice in academic fields or industry.
On successful completion of the PhD program, graduates will be able to: Identify scientific and engineering correlations, significances, and insights in new data science and analytics models, data integration phd thesis, algorithms, tools, principles, frameworks, solutions, and techniques; Demonstrate critical thinking and analytical skills from the perspective of data science and analytics; Apply a range of qualitative and quantitative research methods for data science and analytics; Translate and transform fundamental research insights effectively into data science practice in academic fields and industry; Exercise independent thinking and demonstrate effective communication skills in presenting and publishing scientific findings; and Conduct original research independently and competently showing in-depth knowledge in the field of data science and analytics.
Minimum Credit Requirement MPhil: 15 credits PhD: 21 credits Credit Transfer Students who have taken equivalent courses at HKUST or other recognized universities may be granted credit transfer on a case-by-case basis, data integration phd thesis, up to a maximum of 3 credits for MPhil students, and 6 credits for PhD students.
Cross-disciplinary Core Courses 2 credits. IIMP Hub Core Courses 4 Credits Students are required to complete at least one Hub core course 2 credits from the Information Hub and at least one Hub core course 2 credits from other Hubs, data integration phd thesis.
Information Hub Core Data integration phd thesis. INFH Other Hub Core Courses. FUNH SOCH SYSH data integration phd thesis Courses on Domain Knowledge MPhil: minimum 9 credits of coursework PhD: minimum 15 credits of coursework Under this requirement, each student is required to take one required course and other electives to form an individualized curriculum relevant to the cross-disciplinary thesis research. Required Course List. DSAA Sample Elective Course List To meet individual needs, students will be taking courses in different areas, which may include but not limited to courses and areas listed below.
COMP IEDA ISOM MATH MSBD Additional Foundation Courses Individual students may be data integration phd thesis to take foundation courses to strengthen their academic background and research capacity in related areas, which will be specified by the Program Planning Committee.
Graduate Teaching Assistant Training, data integration phd thesis. PDEV Professional Development Course Requirement. English Language Requirement. LANG Postgraduate Seminar. PhD Qualifying Examination PhD students are required to pass a qualifying examination to obtain PhD candidacy following established policy. Thesis Research. MPhil: Registration in DSAA ; and Presentation and oral defense of the MPhil thesis. PhD: Registration in DSAA ; and Presentation and oral defense of the PhD thesis.
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Data Integration Challenges and How to Mitigate Them
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Just want the schools? Skip ahead to our complete list of data-related PhD programs.. Why Earn a PhD in Data Science? A PhD in Data Science is a research degree designed to equip you with knowledge of statistics, programming, data analysis and subjects relevant to your area of interest (e.g. machine learning, artificial intelligence, etc.) The Master of Philosophy (MPhil) and Doctor of Philosophy (PhD) Programs in Data Science and Analytics aim to facilitate close integration of statistical analytics, logical reasoning, and computational intelligence in the study of data processing and analytics 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
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