First Year First Semester Classes
First Year Second Semester Classes
Second Year First Semester Classes
Course Code | Course Title | Credit | |
---|---|---|---|
DSC | 703 | Statistics with R Application | 3 |
DSC | 705 | Mathematical Application with MATLAB | 3 |
DSC | 707 | Analysis and Design of Algorithms | 3 |
DSC | 709 | Data Modelling | 3 |
DSC | 711 | Advanced Statistics | 3 |
Course Code | Course Title | Credit | |
---|---|---|---|
DSC | 702 | Big Data | 3 |
DSC | 704 | Nonlinear Models for Data Science | 3 |
DSC | 706 | Stochastic Process for Data Science | 3 |
DSC | 708 | Machine Learning | 3 |
DSC | 710 | Database Systems and Data Management | 3 |
Course Code | Course Title | Credit | |
---|---|---|---|
DSC | 711 | Artificial Intelligence | 3 |
DSC | 700 | Capstone Project | 3 |
OR | |||
DSC | 701 | Master’s Thesis | 6 |
This course covers the statistical tools needed to understand empirical research and to plan and execute independent research projects. Topics include statistical inference, regression, dimension reduction, data clustering,
similarity, neighbours and machine learning and evaluation of policies and programs.
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In this course, Descriptive and Inferential Statistics, Measurement Scales, Visualizing data, frequency histograms, Measures for central tendency: mean, median, mode, Measures of variability: range, quartiles, standard deviation,
skewness and variance, Data Visualization, Probability Methods, Discrete Probability Distributions, Continuous Probability Distributions, Distributions of Sample Statistics, Confidence Interval Estimation, Hypothesis Testing,
Simple Linear Regression, Multiple Linear Regression and Analysis of Variance will be taught by using R programming applications.
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The course content includes statistical inference in nonlinear models, nonlinearity in regression, dimension reduction, data clustering, similarity, neighbours and homogeneity/heterogeneity and evaluation of policies and
programs. Students will learn the principles and best practices for how to use big data in order to support fact-based decision-making. Emphasis will be given to applications in various data which has nonlinearity in big data.
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This course focuses on the mathematical methods and models that are required to understand investigate models. Topics may include limits, sequences and series, set theory; univariate and multivariate calculus; matrix algebra and
systems of linear equations; static and dynamic optimization; differential-difference equations and applications in models.
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Topics may include essentials of stochastic integrals and stochastic differential equations. Probability distributions and heavy tails, ordering of risks, aggregate claim amount distributions, risk processes, renewal processes
and random walks, markov chains, continuous Markov models, martingale techniques and Brownian motion, point processes, diffusion models, and applications in various subject related data science.
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This course covers the main approaches to design and analysis of algorithms including important algorithms and data structures, and results in complexity and computability. The main content consists of Search and Sorting, Eid
Al-Adha break, Divide and Conquer Algorithms, Graphs, Project Proposal, Dynamic Programming, Greedy Algorithms, Randomized Algorithms, P and NP, Work with NP Hard Problems, Partial Recursive function, Computations and Unsolvable
Problems.
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This course covers machine learning and statistical pattern recognition. Topics include: supervised learning, unsupervised learning, learning theory; reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing and evaluation of policies and programs.
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This course provides an understanding of the application of software technologies that enables users to make better and faster decisions based on various data. This course covers the statistical tools needed to understand
empirical research and to plan and execute independent research projects. Topics include statistical inference, regression, generalized least squares, instrumental variables, simultaneous equations models, and evaluation of
policies and programs.
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This course covers the degree develops practical workplace competencies that meet current and future challenges through a real world coursework utilizing personalized academic mentoring and tutoring. The coursework focuses on
introduction level of multivariate statistics, factor analysis, principal component analysis, period-grams, state space, frequency domain, Fourier function, functional regression analysis, bootstrapping and asymptotic theory of
tests and estimators.
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