MX in Data Science Program Description

The data continue to shape our today and tomorrow at an increasing pace. Every industry, government, social and health organization is developing smart and sophisticated analytical tools to draw meaningful insight from data to help their business and the society. As a result, the demand for Data Scientists is growing by the day. In the face of such an increasing demand, educational institutions are the first ones to embrace the challenge by offering distinctive programs to supply the skilled work force.

The Professional Master Program in Data Science and Analytics at KFUPM aims to prepare its graduates for careers in Data Science by offering an immersive multidisciplinary program. This program combines topics from Mathematics and Statistics with the tools from Computer Science. The program covers topics ranging from mathematical foundations for data science, statistical analysis of data including time-series analysis, big-data analytics, and machine learning including deep learning. The program will also give a strong hands-on tools experience to the students letting them develop advanced skills with the most recent software and toolboxes used in Data Science. 

Degree Plan for a professional Master of Data Science and Analytics

Course Descriptions for a Professional Master Program in Data Science 

MATH 503: Mathematics for Data Science

Selected topics from linear algebra, multivariate calculus, and optimization for Data Science with an emphasis on the implementation using numerical and symbolic software, toolboxes, and libraries for data science like NumPy, SciPy, Pandas, SymPy. Topics include data transformation using linear algebra, vector spaces, linear transformations, matrix representations, matrix decompositions (eigenvectors, LU, QR, SVD, Cholesky); multivariate calculus for continuous, convex, and non-convex optimization methods; time series construction and visualization, Fourier transformations for time series conversion.
Prerequisite: Graduate Standing

MATH 506: Fundamentals of Data Science
All aspects of the data science pipeline using the software, toolboxes, and libraries like NumPy, SciPy, Pandas, Matplotlib, Seaborn: data acquisition, cleaning, handling missing data, EDA, visualization, feature engineering, modeling, model evaluation, bias-variance tradeoff, sampling, training, testing, experimenting with a classical model.
Prerequisite: Graduate Standing

STAT 503: Probability and Statistics for Data Science
Selected topics from Probability theory, Statistical Inference, and Information Theory for Data Science with an emphasis on the implementation using statistical software, toolboxes, and libraries like R, NumPy, SciPy, Pandas, and Statsmodels. Topics include Probability; Conditional Probability; Bayes' Theorem; Random variables; Discrete and Continuous Distributions; Central Limit Theorem; Point Estimation MLE and MAP; Confidence Interval Estimation; Hypothesis Testing; Non-parametric Statistics; Synthetic Data; Entropy, Mutual Information; Information Gain.
Prerequisite: Graduate Standing

ICS 574: Big Data Analytics
Introduction and foundation of big data and big-data analytics. Sources of big data. Smart clouds. Hadoop file system and Apache Spark. Storage management for big data. Machine learning and visualization with big data. Applications of big data. Big data and security, privacy, societal impacts.
Prerequisite: Graduate Standing
Note: Cannot be taken for credit with ICS-474.

ICS 502: Machine Learning
Introduction to machine learning; supervised learning (linear regression, logistic regression, classification, support vector machines, kernel methods, decision tree, Bayesian methods, ensemble learning, neural networks); unsupervised learning (clustering, EM, mixture models, kernel methods, dimensionality reduction); learning theory (bias/variance tradeoffs); and reinforcement learning and adaptive control.
Prerequisite: Graduate Standing
Note: Cannot be taken for credit with ICS-485.

STAT 513: Statistical Modeling
Statistical tools for learning from the data by doing statistical analysis on the data with an emphasis on the implementation using various software, toolboxes, and libraries like R, Scikit-Learn, and Statsmodels. Topics include Simple and Multiple Linear Regression, Polynomial Regression, Splines, Generalized Additive Models; Hierarchical and Mixed Effects Models; Bayesian Modeling; Logistic Regression, Generalized Linear Models, Discriminant Analysis; Model Selection.
Prerequisite: STAT 503
Note: Cannot be taken for credit with STAT 413.

ICS 504: Deep Learning

Deep Learning models and their applications in real world.  Foundations of deep learning networks training and optimization. Deep learning models for spatial and temporal data processing. Analysis of prominent deep learning models such as Convolutional Neural Networks (CNNs), Recurrent and Recursive Networks, Long-Short Term Memory (LSTM), Residuals Networks, and Generative Adversarial Networks (GANs). One-Shot Learning and Deep Reinforcement Learning.

Prerequisite: ICS 502 or Consent of Instructor

Note: Cannot be taken for credit with ICS-471

STAT 523: Forecasting Methods
Time Series Basics; Autocorrelation; Modeling and forecasting with MA, AR, ARMA, ARIMA models; Seasonal and non-seasonal models; Model validation; Parameter selection; Smoothing and decomposition methods; Advanced forecasting methods, Multivariate models, State Space Models, Arch and Garch Models; projects using various software, toolboxes, and libraries like R, Scikit-Learn, and Statsmodels.
Prerequisite: STAT 503
Note: Cannot be taken for credit with ISE 487.

MATH 619: Project
A graduate student will arrange with a faculty member to conduct an industrial research project related to the Data Science field. Subsequently the students shall acquire skills and gain experiences in developing and running actual industry-based project. This project culminates in the writing of a technical report, and an oral technical presentation in front of a board of professors and industry experts.
Prerequisites: Graduate Standing