Degree Structure
College
Computing and Informatics
Department
Computer Science
Level
Graduate Masters
Study System
Courses and Theses
Total Credit Hours
33 Cr. Hrs.
Duration
2-4 Years
Intake
Fall and Spring
Language
English
Study Mode
Full Time and Part Time
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Degree Overview
Program Structure & Requirements
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
What You Will Learn
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 in 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.
University Requirements
College Requirements
Degree Requirements
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. | ||
| 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. | ||
| 1501564 | Foundations of Data Science | 3 |
| Prerequisite: | Introduction to Database Management Systems (1501263) or 1501567 or equivalent | - |
| Prerequisite: | 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 ensures 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. | ||
| 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. Topics will include: deterministic models; linear time series models, stationary models, and others. | ||
| 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 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, and recent advances in function estimation. | ||
| 1440590 | Stochastic Processes | 3 |
| Prerequisite: | 1440281 or equiv | - |
| This course develops and analyzes probability models that capture the salient features of systems under study to predict the short and long term effects that randomness will have on the systems under consideration. The course strikes a balance between the mathematics and applications of stochastic processes. | ||
| 1440593 | Topics in Statistics | 3 |
| Prerequisite: | 1440281 or equiv | - |
| This course covers selected topics in statistical methods related to statistical analysis. It gives a brief review of linear regression models, generalized and linear mixed models, matrix algebra, multivariate random variables, and their distribution. | ||
| 1501668 | Big Data & Data Analytics | 3 |
| Prerequisite: | Introduction to Database Management Systems (1501263) or 1501567 or equivalent | - |
| Big data provides the fundamentals, technologies, and tools to understand and apply Big Data analytics. Topics include: Big Data types, technologies, analytical tools, numerical, textual, image and stream analysis, and applications of spatial data and remote sensing. | ||
| 1501661 | Topics in Data Analytics and Cloud Computing | 3 |
| Prerequisite: | Graduate Standing | - |
| Cloud Computing enables big data processing at a large scale, allowing 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, such as the architecture of cloud data centers and resource management and scheduling. | ||
| 1501664 | 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 an in-depth perspective of a particular research problem in their chosen field of specialization of Data Science. | ||
| 1501530 | Advanced Artificial Intelligence | 3 |
| Prerequisite: | Graduate standing | - |
| This course covers fundamental and advanced concepts in artificial intelligence, such as intelligent agents, informed and uninformed search, adversarial search, constraint satisfaction problems, Bayesian networks, decision networks, and advanced topics like machine learning and reinforcement learning. | ||
| 1501531 | Machine Learning | 3 |
| Prerequisite: | (1440211-Linear Algebra + 1501215-Data Structures) or equivalent | - |
| This course provides a broad introduction to machine learning, including regression, classification, clustering, and algorithms like decision trees, support vector machines, artificial neural networks, and others. | ||
| 1501511 | Advanced Programming | 3 |
| Prerequisite: | Basic programming course | - |
| This course familiarizes students with advanced methods in Python programming, including object-oriented programming, parallel programming, data structures, and algorithms. It applies Python to fields such as AI and Data Science. | ||
| 1501663 | 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 an in-depth perspective of a particular research problem in their chosen field of specialization of Data Science. | ||
Career Path
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