MSc In Computational Data Science

The educational objectives of the M.Sc. in Computational Data Science program are to produce graduates who will be able to:

• Advance professionally and be recognized as leaders in their chosen areas within the broad field of computational data science.
• Apply their technical expertise to address the critical needs of society in a creative, ethical, and innovative manner.
• Further develop their knowledge and skills through graduate education or professional schools.

Overall Structure

The M.Sc. in Computational Data Science (CODS) program consists of a minimum of 33 credit hours. The required program credits are distributed as follows: 12 credits of Program Core courses, 9 credits of Program Elective courses, 3 credits for a Graduate Project, and 9 credits of CODS Master’s Thesis work. A student may organize the selection of the elective courses and the master’s thesis topic to follow an optional concentration in either “Computational Systems” or “Data Analytics” within the broad field of Computational Data Science. In such cases, the optional concentration will only be noted on the student’s academic record (transcript). The table below presents a summary of the M.Sc. in CODS degree program structure and requirements. All the M.Sc. in CODS program courses, with the exception of the Seminar in Research Methods and the Master’s Thesis, have a credit rating of three credits each.

Program requirements

Students seeking the degree of MSc in Computational Data Science must successfully complete a minimum of 33 credit hours as specified in the program requirements detailed below, with a minimum CGPA of 3.0. Course selection should be made in consultation with the student’s Main Advisor. All courses have a credit rating of three credits each, except the ENGR 695 Seminar in Research Methods and the Master’s Thesis.

Program core

The M.Sc. in CODS degree program core requires a minimum of 12 credits, consisting of the four courses, which are 3 credits each, and the seminar in research methods course which has zero credit rating. The core courses are listed below.

• CODS 608 Distributed Systems and Could Computing
• CODS 610 Model Estimation
• CODS 620 Advanced Statistical Inference
• CODS 622 Data Science with Machine Learning
• ENGR 695 Seminar in Research Methods (0 credits)

Program electives (minimum 9 credits)

Students must complete a minimum of 9 credits of electives from the list of courses given below. Students can also select one elective course (3 credits) from the MSc in Computer Science or the MSc in Electrical and Computer Engineering programs at KU subject to the approval of their research advisor and the Associate Dean for Graduate Studies.

• CODS 612 Computational Methods and Optimization in Finance
• CODS 623 Health Data Science
• CODS 624 Space-Time Data Science
• CODS 626 Financial Derivatives and Risk Management
• CODS 630 Advanced Computer Networks
• CODS 631 Blockchain Fundamentals and Applications
• CODS 634 Artificial Intelligence
• CODS 635 Deep Learning System Design
• CODS 636 Introduction to High-Performance Computing
• CODS 637 Parallel Programming
• CODS 640 Financial Cyber Security
• CODS 641 Natural Language Processing & Information Retrieval
• CODS 642 Database Systems Concepts and Design
• CODS 643 Mobile and Pervasive Computing
• CODS 644 Data Science for Business Applications
• CODS 645 Financial Machine Learning
• CODS 650 Data Processing and Visualization
• CODS 694 Selected Topics in Computational Data Science

CODS Graduate Project

• CODS 697 Computational Data Science Graduate Project (3 Cr)

Master’s thesis

CODS 699 Master’s Thesis (minimum 9 credits)

A student must complete a master’s thesis that involves creative research-oriented work within the broad field of Computational Data Science, under the direct supervision of the main advisor, who must be a full-time faculty in either the Electrical Engineering and Computer Science Department or the Mathematics Department, and at least one other full-time faculty who acts as co-advisor. The research findings must be documented in a formal thesis and defended successfully in a viva voce examination. Furthermore, the research should lead to publishable quality scholarly articles.

Concentrations

A student may opt to have one of the optional concentrations within the M.Sc. in CODS program. To do so, the student must complete a minimum of 9 credits from the elective courses specified for the particular concentration and conduct research for her/his thesis within the domain of that concentration. The concentration will only be noted on the student’s academic record (transcript). The following concentrations are currently available under the M.Sc. in CODS program:

• Computational Systems
• Data Analytics

Computational Systems Concentration

The requirements for a student who would like to pursue the Computational Systems Concentration are as follows:

The student must take at least 9 credits from the courses below:

• CODS 630 Advanced Computer Networks
• CODS 631 Blockchain Fundamentals and Applications
• CODS 635 Deep Learning System Design
• CODS 636 Introduction to High-Performance Computing
• CODS 637 Parallel Programming
• CODS 642 Database Systems Concepts and Design
• CODS 643 Mobile and Pervasive Computing

The student’s thesis must be in the general domain of Computational Systems.

Data Analytics Concentration

The requirements for a student who would like to pursue the Data Analytics Concentration are as follows.

The student must take at least 9 credits from the courses below:

• CODS 623 Health Data Science
• CODS 624 Space-Time Data Science
• CODS 635 Deep Learning System Design
• CODS 640 Financial Cyber Security
• CODS 641 Natural Language Processing & Information Retrieval
• CODS 644 Data Science for Business Applications
• CODS 645 Financial Machine Learning
• CODS 650 Data Processing and Visualization

The student’s thesis must be in the general domain of Data Analytics.

Students graduating with the M.Sc. in Computational Data Science will be able to:

• Identify, formulate, and solve computational data science problems through knowledge and understanding of advanced computing and mathematical concepts.
• Critically evaluate emerging data analysis technologies and assess how they can be applied to different types and amounts of data.
• Design and program complex computer software for various computational data science applications using state-of-the-art tools and techniques.
• Conduct research and document and defend the research results.
• Function in teams and communicate effectively.
• Conduct themselves in a professional and ethical manner

Additional learning outcomes for the optional concentrations:

• Computational Systems
• Demonstrate proficiency in analysis and design of major aspects of computational systems for data science applications.
  • Demonstrate proficiency in analysis and design of major aspects of computational systems for data science applications.
• Data Analytics
• Apply advanced data analytic techniques to a range of application domains.
  • Apply advanced data analytic techniques to a range of application domains.

A master’s degree in Computational Data Science from Khalifa University helps open many career opportunities for future success. The program combines computer science algorithms and statistical techniques to analyze and understand the information hidden in big data, such as those generated by financial, and health services, to optimize and grow the various economic sectors, devise strategies and enable leaders to make data-driven decisions. The Computational Data Science program equips graduates with the knowledge and skills to join data and technology intensive industries, including but not limited to investments and finance, telecommunications, national security, healthcare, energy, manufacturing, and utilities. The MSc in CODS program at Khalifa University offers students an excellent opportunity for interdisciplinary education, which will help them fulfil the requirement of these career paths. Graduates also go through rigorous training and research experience to enable them to pursue their studies at PhD level.