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Master of Science in Data Science

College
College of Computing and Informatics
Department
Computer Science
Level
Masters
Study System
Thesis and Courses
Total Credit Hours
33 Cr.Hrs
Duration
2-4 Years
Intake
Fall & Spring
Location
Sharjah Main Campus
Language
English
Study Mode
Full Time and Part Time

Master of Science in Data Science

Introduction

The MSc in Data Science program will stimulate research activities in the area of computer science, statistics, and their applications. This will enrich both undergraduate and graduate programs in both Colleges (College of Computing and Informatics and College of Science). Faculty of both departments (Computer Science and Mathematics) will be able to contribute and continue performing their teaching, and research in the undergraduate and graduate programs in both departments. In addition, interdisciplinary research projects could be achieved with the other departments in the UoS.

The program will accept students from any related undergraduate program of computing or mathematics. For those students who lack some foundational knowledge, the program offers remedial courses to bridge the lack of background knowledge before embarking on the required courses

 
Program Objectives

Upon the successful completion of the program, student will be able to:
  1. Apply computing models to devise solutions to data science tasks.
  2. Integrate theory and practice of computer science and applied mathematics to analyze and handle large-scale data sets.
  3. Transform information to discover relationships and insights into complex data sets. 
  4.  Communicate data science-related information in various formats to their team and stakeholders.
  5. Create models using proper techniques and methodologies of abstraction that can be automated to solve real-world problems.
  6. Assess ethical use of data in all aspects of the data science profession.
 
 
Special Admission Requirements

To be admitted to the M.Sc. in Data Science Program the following requirements:

  1. Applicants must have a bachelor's degree in any Computer Science (or a closely-related field) from a recognized college or university with an overall undergraduate grade point average of 3.00 (out of 4.0) or higher. Students with a CGPA between 2.5 and 2.99 may be admitted conditionally.
  2. Applicants are required to enroll in prerequisite courses, which they have not taken in their prior studies, as deemed necessary by the Department's "Graduate Studies Committee" and approved by the College and University. These prerequisite courses should be completed within no more than two semesters (Full-Time) and will not be considered as part of the required credit load for the graduate degree.
  3. The undergraduate degree should be in a subject that will qualify students for the graduate specialization of their choice. Otherwise, students may be admitted upon the recommendation of the Department and after their study for required prerequisite courses assigned by the Department.
  4. The graduate admission committee may grant conditional admittance to an applicant whose GPA is between 2.5 t0 2.99 and may require a GPA of at least 3.00 in the last 33 credit hours of their major courses, including courses that are related to their desired specialization. Conditionally accepted applicants must attain a grade point average of 3.00 or higher during their first semester with at least 9 credit hours before being fully admitted into the program.Applicants must provide certified transcripts from the institution where they received their B.Sc. degree, along with course descriptions, and must provide letter(s) of reference.
  5. Applicants must have a bachelor's degree in any Computer Science (or equivalent on the iBT or CBT; or 6 on the academic IELTS for programs taught in English. Students may be admitted conditionally if they obtain 530 or higher on TOEFL provided that they enroll in an English language course and receive a TOFEL score of 550 by the end of their first semester of study. Students who do not meet these two conditions will be dismissed from the program.
Applications will be reviewed and recommended for acceptance by the "Research and Graduate Studies Committee" by the Department and approved by the "Department Council".
 

Full-time candidates for the Master's degree must complete their requirements within a minimum of 3 semesters and a maximum of 8 semesters from the date they are admitted into the program. The admission requirements as cited above are almost similar to all of the other Science Master's programs at the University of Sharjah.

Part-time candidates for the master's degree must complete their requirements within a minimum of 6 semesters and a maximum of 10 semesters from the date they are admitted into the program (This does not include the allowed postponed semesters).  The admission requirements as cited above are almost similar to all other Science Master's programs at the University of Sharjah

 
Program Structure & Requirements

Requirements ​
Compulsory ​ Elective ​ Total ​
Courses Credit Hours Courses Credit Hours Courses Credit Hours
Courses 5 15 3 9 8 24
Thesis 1 9 0 0 1 9
Total Credit Hours 24
9 ​ 33 ​
 

Study Plan
Program Requirements

  1. Compulsory courses (15 credit hours)
  2. Elective Courses (9 credit hours)
  3. Thesis (9 credit hours)

Study Plan: Course List
Course Code
Course Title
اسم المساق
Credit Hours Pre-requisite
  Compulsory Courses ​ ​ ​
1440581 Regression Modelling نماذج الانحدار 3 1440281 or equiv.
1440582 Introduction to Bayesian Data Analysis مقدمة في تحليل بييز للبيانات 3 1440281 or equiv.
1501565 Data Mining
التنقيب عن البيانات 3 (1501263 or 1501567 or equiv.)
1501566 Foundations of Data Science اساسيات علم البيانات 3 (1511263 or 1501567 or equiv.) And 1440281.
1501590 Research Methodology طرائق البحث 3 (1501215 or 1501501) and Graduate Standing
1501696 Thesis in Data Science أطروحة علم البيانات 9 1501590
Elective Courses ​ ​ ​ ​
Mathematics Electives ​ ​ ​ ​
1440583 Graphical data analysis تحليل البيانات الرسومية 3 1440281 or equiv.
1440584 Applied Time Series Analysis المتسلسلات الزمنية التطبيقية 3 1440281 or equiv.
1440586 Design of Experiments
تصميم التجارب 3 1440281 or equiv.
1440589 Nonparametric Inference of Statistics الاستدلال اللامعلمي للإحصاء 3 1440281 or equiv.
1440590 Stochastic Processes العمليات العشوائية 3 1440281 or equiv.
1440593 Topics in Statistics موضوعات في الإحصاء 3 1440281 or equiv.
Data-centric Electives ​ ​ ​ ​
1501668 Big Data and Data Analytics البيانات الضخمة وتحليلات البيانات 3 1501263 or 1501567 or equiv.
1501761 Topics in Data Analytics and Cloud Computing موضوعات في تحليلات البيانات و الحوسبة السحابية 3 Graduate Standing
1501764 Topics in Data Science موضوعات في علم البيانات 3 1501263 or 1501567 or equiv.
Machine Intelligence Electives ​ ​ ​ ​
1501530 Advanced Artificial Intelligence موضوعات في الذكاء الاصطناعي 3 Graduate standing
1501531 Machine Learning التعلم الآلي 3 1440211 + 1501215
1501711 Advanced Programming البرمجة المتقدمة 3 Basic programming course
1501763 Information Retrieval استرجاع المعلومات 3 1501263 or 1501567 or equiv.


Study Plan: Course Distribution
 
First Year             
​Fall Semester
​Spring Semester
Course # ##
Course Title Cr.Hr Course # Course Title Cr.Hr
1511566 Foundations of Data Science 3 1501565 Data Mining 3
1440581 Regression Modelling
3 1501590 Research Methodology 3
  Elective Course 3 1440582 Introduction to Bayesian Data Analysis 3
  Total 9   Total 9
 

Second Year
​Fall Semester
​Spring Semester
Course #
Course Title Cr.Hr Course # Course Title Cr.Hr
  Elective Course
3 1501696 Thesis in DS 6
  Elective Course 3      
1501696 Thesis in DS 3      
  Total 9   Total 6
 


Course Description

1440581 Regression Modelling 3
Prerequisite: 1440281 or equiv

Regression Modelling is a course in applied statistics that studies the use of linear regression techniques for examining relationships between variables. The course emphasizes the principles of statistical modelling through the iterative process of fitting a model, examining the fit to assess imperfections in the model and suggest alternative models, and continuing until a satisfactory model is reached. Both steps in this process require the use of a computer: model fitting uses various numerical algorithms, and model assessment involves extensive use of graphical displays. The R statistical computing package is used as an integral part of the course. ​ ​
1440582 Introduction to Bayesian Data Analysis 3
Prerequisite: 1440281 or equiv

The Bayesian approach to statistics assigns probability distributions to both the data and unknown parameters in the problem.  This way, we can incorporate prior knowledge on the unknown parameters before observing any data.  Statistical inference is summarized by the posterior distribution of the parameters after data collection, and posterior predictions for new observations.  The Bayesian approach to statistics is very flexible because we can describe the probability distribution of any function of the unknown parameters in the model.  Modern advances in computing have allowed many complicated models, which are difficult to analyze using 'classical' (frequentist) methods, to be readily analyzed using Bayesian methodology. The aim of this course is to equip students with the skills to perform and interpret Bayesian statistical analyses.  The first part of the course is devoted to describing the fundamentals of Bayesian inference by examining some simple Bayesian models.  More complicated models will then be explored, including linear regression and hierarchical models in a Bayesian framework.  Bayesian computational methods, especially Markov Chain Monte Carlo methods will progressively be introduced as motivated by the models discussed.   Emphasis will also be placed on model checking and evaluation. ​ ​
1501565 Data Mining 3
Prerequisite: Introduction to Database Management Systems (1501263), or 1501567 or equivalent.

Data mining has become one of the most interesting and rapidly growing fields. Data mining techniques are used to uncover hidden information, such as patterns, in databases and perform predictions. The data to be mined may be complex data including multimedia, spatial, and temporal. Topic include data processing, association rules, clustering, and classification. This course is designed to provide graduate students with a solid understanding of data mining concepts and tools. ​ ​
1501566 Foundations of Data Science 3
Prerequisite: Introduction to Database Management Systems (1501263) or 1501567 or equivalent, and Introduction to Probability and Statistics (1440281)

Data science is an interdisciplinary field that provides tools to extract insights from data in various forms, either structured or unstructured. Data science course provides the theories, strategies, and tools to understand and apply the following topics: data preparation, data cleaning & integration, data analysis, classification, clustering, text analysis, and visualization. ​ ​
1501590 Research Methodology 3
Prerequisite: 1501215-Data Structures or equivalent, and Graduate Standing

This course introduces graduate students to the practice of research. The course preliminary introduces students to concepts of research methods in data science, data resources, data collection, and literature review. The course ensure that students learn how to select a research topic, devise research questions, and plan the research. Additionally, the students will gain practical knowledge on technical writing, such as writing a thesis proposal, a survey paper, and technical review of research papers. Students will also gain the skills, and practice, of technical presentations of scientific research papers and proposal. Lab sessions will be conducted to train students on a Latex editor for writing technical reports. ​ ​
1440583 Graphical data analysis 3
Prerequisite: 1440281 or equiv

This course introduces the principles of data representation, summarization and presentation with particular emphasis on the use of graphics. The course will use the R Language in a modern computing environment. ​ ​
1440584 Applied Time Series Analysis 3
Prerequisite: 1440281 or equiv

This course considers statistical techniques to evaluate processes occurring through time. It introduces students to time series methods and the applications of these methods to different types of data in various contexts (such as actuarial studies, climatology, economics, finance, geography, meteorology, political science, risk management, and sociology). Time series modelling techniques will be considered with reference to their use in forecasting where suitable. While linear models will be examined in some detail, extensions to non-linear models will also be considered. The topics will include: deterministic models; linear time series models, stationary models, homogeneous non-stationary models; the Box-Jenkins approach; intervention models; non-linear models; time-series regression; time-series smoothing; case studies. Statistical software R will be used throughout this course. Heavy emphasis will be given to fundamental concepts and applied work. Since this is a course on applying time series techniques, different examples will be considered whenever appropriate. ​ ​
1440586 Design of Experiments 3
Prerequisite: 1440281 or equiv

This course covers the statistical design of experiments for systematically examining how a system functions. Topics covered will include: introduction to experiments, completely randomized designs, blocking designs, factorial designs with two levels, fractional designs with two levels and response surface designs. ​ ​
1440589 Nonparametric Inference of Statistics 3
Prerequisite: 1440281 or equiv

This course is an introduction to nonparametric function estimation. Topics include kernels, local polynomials, Fourier series, spline methods, wavelets, automated smoothing methods, cross-validation, large sample distributional properties of estimators, lack-of-fit tests, semiparametric models, recent advances in function Estimation. ​ ​
1440590 Stochastic Processes 3
Prerequisite: 1440281 or equiv

Many systems evolve over time with an inherent amount of randomness. The purpose of this course is to develop and analyze probability models that capture the salient features of the system under study to predict the short and long term effects that this randomness will have on the systems under consideration. The study of probability models for stochastic processes involves a broad range of mathematical and computational tools. This course will strike a balance between the mathematics and the applications. ​ ​
1440593 Topics in Statistics 3
Prerequisite: 1440281 or equiv

This course cover selected topics in statistical methods related to statistical analysis. It gives a brief review of linear regression models, generalized and linear mixed models. Some matrix algebra that helps to understand the quadratic forms and multivariate random variables and their distribution. In addition, some topics form the analysis of variance will be covered. The topics covered are typically not included in other statistical courses at the master's level. The course material may change (and likely will) each semester depending on Students' preferences. ​ ​
1501668 Big data & Data Analytics 3
Prerequisite: : Introduction to Database Management Systems (1501263) or 1501567 or equivalent

Big data is becoming one of the most important technology that enables organizations to store, manage, and manipulate vast amounts of data efficiently to gain business insights. Big data course provides the fundamentals, technologies, and tools to understand and apply the following the Big Data analytics. Topics covered are: Big Data types, technologies, analytical tools, numerical, textual, image and stream analysis, and applications of spatial data and remote sensing. ​ ​
1501761 Topics in Data Analytics and Cloud Computing 3
Prerequisite: Graduate Standing

Cloud Computing enables big data processing at a large scale, by allowing a ubiquitous access to a large number of shared remote servers, often, over the internet. This course presents advanced research topics in cloud systems, data processing frameworks, and networking. Example topics include the architecture of cloud data centers, state-of-the-art data processing frameworks, Cloud workload characteristics, and resource management and scheduling. ​ ​
1501764 Topics in Data Science 3
Prerequisite: 1501263 or 1501567 or equiv

The student has to undertake and complete a research topic in Data Science under the supervision of a faculty member. The thesis work should provide the student with in-depth perspective of a particular research problem in his chosen field of specialization of Data Science.  It is anticipated that the student be able to carry out this theoretical research and required practical work fairly independently under the direction of the supervisor.  The student is required to submit a final thesis documenting the research, experiments, findings and defend the work in front of a committee. ​ ​
1501530 Advanced Artificial Intelligence 3
Prerequisite: Graduate standing

This course covers fundamental and advanced concepts and techniques in the field of artificial intelligence. The main topic list includes intelligent agents, uninformed and informed search, adversarial search, constraint satisfaction problem, and uncertain knowledge and reasoning, which covers Bayesian Networks, and Decision Networks. In addition, advanced topics will include machine learning, reinforcement learning, natural language processing (or vision/robotics), and deep learning. ​ ​
1501531 Machine Learning 3
Prerequisite: (1440211-Linear Algebra + 1501215-Data Structures) or equivalent

This course provides a broad introduction to machine learning. Main topics include Regression, classification, and clustering. Detailed subjects are simple and multiple, Ridge, kernel feature, feature selection & Lasso; Linear classifiers & logistic regression; decision trees and ensemble learning, support vector machines, and artificial neural networks. Besides, best practices in machine learning such as overfitting/regularization and bias/variance theory shall be covered. Students will learn how to identify and implement appropriate machine learning algorithms for a variety of problems. ​ ​
1501511 Advanced Programming 3
Prerequisite: Basic programming course

This course aims to familiarize students with advanced methods in programming using Python, including object-oriented programming, parallel programming, data structures, algorithms, and applications of Python programming to AI and Data Science. The course covers the following major modules: (I) Object-Oriented Programming, (II) Data Structures and Algorithms, (III) Parallel Programming, (IV) Applications and Computing Tools: Features, Libraries, agents and controllers, and Regular Expressions applied to NLP tasks. ​ ​
1501763 Information Retrieval 3
Prerequisite: : Introduction to Database Systems (1501263) or 1501567 or equiv

The student has to undertake and complete a research topic in Data Science under the supervision of a faculty member. The thesis work should provide the student with in-depth perspective of a particular research problem in his chosen field of specialization of Data Science.  It is anticipated that the student be able to carry out this theoretical research and required practical work fairly independently under the direction of the supervisor. The student is required to submit a final thesis documenting the research, experiments, findings and defend the work in front of a committee. ​ ​
 




Special Admission Requirements
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