Master of Science in Data Science

Year 1, Fall semester

9 credit hours

• 1501564Foundations of Data Science3 credits

  Prerequisite: Introduction to Database Management Systems (1501263) or 1501567 or equivalent; 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.

• 1440581Regression Modelling3 credits

  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.

• Elective Course3 credits

Year 1, Spring semester

9 credit hours

• 1501565Data Mining3 credits

  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.

• 1501591Research Methodology3 credits

  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.

• 1440582Introduction to Bayesian Data Analysis3 credits

  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.

Year 2, Fall semester

9 credit hours

• Elective Course3 credits
• Elective Course3 credits
• 1501696Thesis in Data Science3 credits

  Prerequisite: 1501591 (Research Methodology)

  This course enables students to conduct independent research on a contemporary topic in data science under the guidance of a faculty supervisor. Students identify a research problem, review relevant literature, develop and implement appropriate methodologies, analyze and interpret results, and present their findings in a formal thesis. The course develops students' research, analytical thinking, problem-solving, and technical communication skills while preparing them to address real-world data science challenges.

Year 2, Spring semester

6 credit hours (programme total: 33)

• 1501696Thesis in Data Science6 credits

  Prerequisite: 1501591 (Research Methodology)

Mathematics Electives

Three elective courses (9 credit hours) are taken from the Mathematics, Data-centric and Machine Intelligence groups.

• 1440583Graphical Data Analysis3 credits

  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.

• 1440584Applied Time Series Analysis3 credits

  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.

• 1440586Design of Experiments3 credits

  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.

• 1440589Nonparametric Inference of Statistics3 credits

  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.

• 1440590Stochastic Processes3 credits

  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.

• 1440593Topics in Statistics3 credits

  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.

Data-centric Electives

• 1501668Big Data & Data Analytics3 credits

  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.

• 1501661Topics in Data Analytics and Cloud Computing3 credits

  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.

• 1501664Topics in Data Science3 credits

  Prerequisite: 1501263 or 1501567 or equiv

Machine Intelligence Electives

• 1501530Advanced Artificial Intelligence3 credits

  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.

• 1501531Machine Learning3 credits

  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.

• 1501511Advanced Programming3 credits

  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.

• 1501663Information Retrieval3 credits

  Prerequisite: Introduction to Database Systems (1501263) or 1501567 or equiv