Program Structure 8 courses = 32 credit hours,
practical training = 10 credit hours
Master's thesis = 18 credit hours
ABOUT THE PROGRAM
The College of Applied Sciences at Solomon International University launched a master’s program in data science with the aim of preparing students with academic skills distinguished by their high quality in matters related to data, employing programming in data analysis, and focusing on aspects that give students extensive experience in modern methods related to big data and dealing with it. The program also It works on integrating the cognitive and applied aspects of data science, artificial intelligence, and machine learning, in addition to the work of technology in addressing obstacles in its various forms in the various fields of life.
Objectives
- Merging between theoretical and applied studies in the field of data science
- Providing communities with professional and academic cadres in the field of data science.
- Providing cadres capable of supplying the labor market with expertise capable of providing business with ideal quality.
- Supplying the local, regional and international labor market with specialized cadres to meet the needs of work in this important field.
- Developing sciences and mental and practical abilities that are important for students to enter the field of computing in the business sectors or postgraduate studies and scientific research.
- Effective practice as a specialist in information systems engineering by leading, designing and developing various projects in the field of information systems.
- Communicate effectively with colleagues, as members or leaders of multidisciplinary teams.
- Encouraging undergraduate students to continue postgraduate studies, in order to provide the community with specialized and highly qualified cadres.
- Encouraging students to conduct scientific research according to scientific research methodology.
Program outputs:
Work in a team spirit to direct the development process in software (modern software engineering methodology combined with design thinking and user service design methods) for complex data science.
Gain in-depth professional skills in scalable data collection technology and data analysis methods.
Learn how to use and develop a set of tools and techniques that address data collection, processing, storage, transmission, analysis and visualization in addition to related concepts (data access, data pricing and data privacy).
Enabling the value of innovation among students in the field of data science, as they understand the different aspects (market, users, social aspects, media technology) together through their opinions in multiple disciplines.
The First Stage
- The student studies eight courses, distributed as follows:
√ Six compulsory courses.
√Two elective courses from among the courses offered by the faculty for master’s students.- The study is conducted through research seminars in each course, and the research seminar relies on multiple references and is in accordance with the scientific research methodology and standards.
- The study of each of the eight courses takes four credit hours for a minimum of four weeks, and it may be more than that according to the capabilities of each student, after which the student’s competency and knowledge test is held in the course he finished, then he starts in another course in the same way, and so on.
- Courses studied in the first year, the student has the right to extend the study for a period not exceeding a second year.
- If a specialization track is chosen within the general program, elective courses will have to be studied from the courses designated for the chosen specialization.
The Second Stage
● The student is assigned a virtual course that the faculty chooses from among the courses that the student studied at the bachelor’s level. This is a practical training for the student, with ten credit hours. The student must divide this course into twelve to fourteen abbreviated lectures. The student presents each lecture in the form of a written summary of its topic in Word format, accompanied by a video recording of it in the student’s voice using the Power Point program. Its duration is not less than ten minutes and not more than twenty. Accurate.
The Thrid Stage
Requirements for registering a thesis topic for a master’s degree in Applied Sciences.
- The student must pass the prescribed academic courses with at least 70%.
- The student obtains a TOEFL certificate with a score of at least 450, or its equivalent, or obtains a corresponding certificate in the French language, with the exception of those who obtained a first university degree in one of the two languages, or in one of the two languages.
- The student submits a request to the university administration to register a master’s thesis with a suggested topic in one of the sub-specialized tracks.
- If the initial approval of the subject title is achieved, the Faculty Council shall specify a supervisor to guide the student and follow him up in preparing the plan.
- The research plan includes the importance of the subject and a critical presentation of the previous studies in it, and a specification of the research problem, then defining the methodology of the study and its main hypotheses or the questions that you want to answer, and the division of the study and its sources.
- The student presents his proposed plan in a scientific seminar. The plan discusses a topic and methodology.
- The student amends his plan based on the notes of the professors in the seminar if he is asked to amend it.
- The plan is presented after the seminar to the Faculty Council to take its decision regarding the registration of the subject.
- In the event of approval, the decision of the Faculty Council is presented to the University Council to approve the registration, and the date of registration is calculated from the date of approval by the University Council.
Jury discussion and degree awarding
- The minimum period for preparing a master’s thesis is nine months, starting from the date of approval of the University Council to register the subject, and the maximum is two years, which can be extended for a third exceptional year upon the recommendation of the supervisor and the approval of the Faculty Council, provided that the total period of the student’s enrollment in the degree does not exceed four years.
- The supervisor submits a semi-annual report that includes what has been accomplished, and what is required in the remaining period.
- After the student completes the thesis and the supervisor reviews it, the supervisor submits to the university administration a report stating that it is valid for discussion, including an evaluation of the student’s performance during the thesis preparation period of 140 degrees, along with submitting a full copy of the thesis signed by him, and a letter with the names of the jury proposed by the professors of the specialty, for presentation to the Council the faculty.
- It is required that before the student’s discussion, at least fifteen days have passed from the date of approval of the jury committee from the faculty.
- The jury committee formed to discuss the thesis is six months, which may be renewed for a similar period based on a report from the supervisor and the approval of the Faculty Council.
- The period of validity of the committee formed to discuss the thesis is six months. It may be renewed for a similar period based on a report from the supervisor and the approval of the Faculty Council.
- Each member of the jury writes a detailed scientific report on the validity of the thesis for discussion, and evaluates the thesis out of 100 degrees, and the average of the three degrees is taken.
- The student may not be discussed unless he obtains at least 70% of the supervisor’s evaluation of his performance and the jury members’ evaluation of the thesis in the individual reports.
- Submit a post-dissertation group report signed by all members of the jury evaluating the thesis discussion out of 100.
- The thesis is approved after common discussion by the jury with one of the grades shown in the following table:
Definition Point Grade Excellent 4.00 A+ Excellent 3.75 A Very Good 3.50 B+ Very Good 3.00 B Good 2.50 C+ Average 2.00 C Pass on probation 1.50 D+ Pass on probation 1.00 D Fail 0.00 F
- The following grades are not taken into account for the semester or cumulative GPA.
Thesis or project in progress : DP Incomplete : I In progress : IP Registration has been suspended : L The grade has not been decided : NGR Did not take the final exam. : NP Transferred course. : T Withdraw from the course. : W The course covers two semesters, the degree is given at the end of the spring or summer semester. : YR There are no credit hours. : NC One credit hour taken as a private student. The credit hours and the degree do not count towards the degree : ND Re-submitted course, only the last grade is used in calculating the GPA. : R Credit taken as a special student. Credit hours and grade counted towards a degree. : S Credits
Before the 2016-2017 fall semester 1 credit point is equivalent to 1 semester lecture hour. In the 2016 – 2017 fall semester the University introduced the ECTS – European Credit Transfer System.Academic Calendar
International Suleiman University calendar is based on the semester system. Each semester has a duration of 15 weeks including the week of the final exam. The summer semester is 10 weeks long, including the final exam.
Core Courses for General Track
Scientific Research Methodology
Course code: MAS101
Course name: Scientific Research Methodology
Credit hours: 4.00
Scientific research methodology
The course includes an introduction to scientific research (definition of scientific research, its purposes, objectives, importance, and characteristics), characteristics of the researcher, methods of obtaining knowledge. Classifications of scientific research methods, the reasons for differences in the classifications of scientific research methods, the descriptive (historical) approach, the experimental approach, the case study approach – the chapters of scientific research, the steps of scientific research and how to formulate them (title, introduction, problem, hypotheses, questions, objectives, importance, Study limits, terminology, study procedures, data collection tools, questionnaire, observation, interview, samples (types, selection), statistical methods used in research, documentation of sources and references (various methods), recommendations and proposals in scientific research.
Statistical Data Analysis
Course code: MADS101
Course name: Statistical Data Analysis
Credit hours: 4.00
This course aims to review the foundational statistical knowledge and identify the methods used in analyzing simple and complex systems with a focus on understanding different statistical principles (from redundancy to Bayesian theory) and the practical ability to apply these examples to data drawn from various systems.
Data Management and Presentation
Course code: MADS102
Course Name: Data Management and Presentation
Credit hours: 4.00
This course aims to develop the student’s understanding of managing large and huge data sets, understanding the critical role of data quality, and the skills needed to present data in an understandable and effective way.
Cloud Computing and Big Data Analysis
Course code: MADS103
Course name: Cloud Computing and Big Data Analysis
Credit hours: 4.00
The course aims to develop a deep understanding of the concept of cloud computing and the issues associated with cloud architecture management starting from business structuring aspects to software engineering approach, design and development, critical analysis and problem-solving skills in cloud systems projects.
Machine Learning
Course code: MADS104
Course name: Machine Learning
Credit hours: 4.00
This course provides a broad introduction to machine learning. Major topics include regression, classification, and clustering. Detailed topics Simple and Multiple, Ridge feature, kernel, feature selection and Lasso; Linear classifiers and logistic regression. Decision trees and ensemble learning, support vector machines, and artificial neural networks. In addition, machine learning best practices such as overfitting/regulation and bias/variance theory should be covered. Students will learn how to define and implement machine learning algorithms suitable for a variety of problems.
Data Analysis and Mining
Course code: MADS106
Course name: Data Analysis and Mining
Credit hours: 4.00
This course aims to complete the knowledge of foundational statistics and machine learning by reviewing the algorithms used in data mining and analysis to find valuable knowledge elements in the field of decision-making, for example extracting association rules to discover interesting relationships between variables in large databases. The role of machine learning algorithms in the chain of operations used in data mining is also defined. This course also allows students to learn about the methods of evaluating these algorithms and applying them in various areas of data analysis and exploration, such as textual and numerical data of various types.
Elective Courses for General Track
Development Computing
Course code: MADS151
Course name: Development Computing
Credit hours: 4.00
This course introduces the main concepts, technologies and applications in the field of evolutionary computing. Topics covered include Components of Evolutionary Algorithms, Genetic Algorithms, Evolution Strategies, Genetic Programming and Learning Classification Systems, Handling of Constraints, Multiobjective Cases, Optimization of Nonstatic Functions and Noise, Coevolutionary Systems, Reactive Evolutionary Algorithms, Evolutionary Computational Theory, Hybridization Using other techniques: Memetic algorithms, ant colony optimization.
Computer Vision and Image Processing
Course code: MADS152
Course name: Computer Vision and Image Processing
Credit hours: 4.00
An introduction to basic and advanced concepts and techniques in computer vision and image processing. After completing this course, students will be able to apply a variety of computer technologies to design efficient algorithms for real-world applications, such as optical character recognition, face detection and recognition, motion estimation, human tracking, and gesture recognition. Topics covered include image filters, edge detection, feature extraction, object detection, object recognition, tracking and motion analysis, gesture recognition, composing images and camera models, and stereoscopic vision. The course will cover deep learning concepts with an introduction to the various architectures and their applications.
Mathematical Methods and Computational Science
Course code: MADS153
Course: Mathematical Methods and Computational Science
Credit hours: 4.00
This course aims to introduce students to the principles of mathematical modeling and how to use them in computational science and data analysis. The topics covered will be applied in various complex fields (from financial to biological) that can benefit from modeling and forecasting techniques.
Information Retrieval and Web Search
Course code: MADS154
Course name: Information Retrieval and Web Search
Credit hours: 4.00
The aim of this course is to make the student familiar with aspects of information management, which affect the field of electronic commerce. These aspects include traditional databases, access to text documents and multimedia information, as well as important emerging topics of the semantic web, blogging, microblogging and social networking.
Advanced Topics in Data Science and Technology
Course code: MADS155
Course Name: Advanced Topics in Data Science and Technology
Credit hours: 4.00
This course aims to present and explain the developments in the field of data science and related fields, both in terms of models and methods, or in terms of techniques and tools for processing, analyzing and displaying data and big data of all kinds, whether structured or unstructured. Deep learning, textual data analysis, health data science, business administration, information security and advanced recommendation systems can be mentioned, but not limited to.
Research Contemporary Issues in Data Science
Course code: MADS156
Course Name: Research Contemporary Issues in Data Science
Credit hours: 4.00
The research project aims to enhance the knowledge gained by the student by addressing the solution to the problem of data analysis. The research project should be based on scientific articles and follow the scientific method to enable the student to have the ability to think based on logical argument, and the ability to objective and rigorous discussion. The project should also include, as far as possible, a comparison of an element of preliminary research conducted to explore specific aspects of the problem with the results of this research and contradictory to relevant theoretical models.
Applied Human Interaction with the Computer
Course code: MAIA157
Course Name: Applied Human Interaction with the Computer
Credit hours: 4.00
This course aims to introduce students to the basics of HCI with their application in the design and development of the new user interface using the latest interaction mechanisms. The course covers concepts, methods, and techniques in planning, prototyping, and evaluating user interfaces for interactive systems. Topics include design principles, usability principles, engineering, user-centered problem-solving, device interaction, and GUI design (2D and 3D interfaces). For application, the course introduces development concepts for console-based user interface design for desktop, mobile, and virtual reality. In addition, the course also introduces the design and development of a natural, console-free user interface design and development for the above systems.
Faculty of Applied Sciences
Degree: Master's DegreeProgram code: MA109ASStudy method: Distance LearningCredit hour: 144How long it takes: Full time: 3 years Part time: 6 years Limit time: 13 years