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 manpower.

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. 

Admission requirements

Admission to the professional master's program in Data Science & Analytics (MSCDSA) is a competitive process. The application must give evidence that the candidate possesses a potential for strong academic performance. We select the best applicants based on the overall undergraduate GPA and transcript, general GRE (quantitative) score, and the recommendation or reference letters.

 

The minimum requirements for applicants to MSCDSA are: 

1. A four-year bachelor's or masters' degree (or equivalent) in Mathematics, Statistics, Computer Science or any related area  in Science and Engineering 
   

2. Minimum Grade-Point Average (GPA): 2.5 on a scale of 4.00 (or equivalent)

3. Grades of at least B (or equivalent) in most Mathematics and Statistics courses

4. IELTS score of 6+ or TOEFL of 70+ (waived for KFUPM graduates and the graduates from English speaking countries.)

5. At least two recommendation letters

6.  The admission process goes beyond meeting the minimum requirements. In particular, the list of courses offered at KFUPM which are equivalent to the required preparatory undergraduate instruction in calculus, linear algebra and programming are required to be eligible for admission.

Degree Plan

Two Years Degree Plan Master of Science (Project-Based) in Data Science & Analytics

Course Descriptions

MATH 503: Mathematics for Data Science            (3-0-3)

Predictive models, sparsity-based techniques, matrix decompositions, randomized SVD, dimensionality reduction with randomized PCA, nonlinear dimensionality reduction, gradient descent methods, stochastic gradient descent methods, linear programming, constrained optimization, Lagrange multipliers, convex and non-convex optimization, backpropagation and automatic differentiation, theoretical developments of algorithms for machine learning models, deep neural networks, graph-based learning. Implementations using numerical and symbolic software.

Prerequisite: Graduate Standing 

MATH 506: Introduction to Data Science (3-0-3)

Overview of Data science and ethical Issues, Statistical inference, Data acquisition and Data cleaning techniques, Exploratory data analysis, Supervised learning, Dimensionality reduction, Regularization, Unsupervised learning, Predictive analytics, Neural networks. 

Prerequisite: Graduate Standing. 

STAT 503 Probability and Statistics for Data Science (3-0-3)

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 502 Machine Learning (3-0-3)

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.

Note: Not to be taken for credit with ICS 485

Prerequisite: Graduate Standing

 

STAT 513: Statistical Modeling                                        (3-0-3)

Linear Regression, Polynomial Regression, Splines, Generalized Linear Models, Generalized additive Model (GAM), Linear mixed model, Partially Mixed Model and anyone of these:- Robust regression, Sparse Regression model, PCA, Discriminant analysis, Some Machine Learning techniques [Decision tree predictive modeling techniques, Neural network modeling techniques, SVM].

Prerequisite: Math 506. 

Note: Not to be taken for credit with STAT 413

ICS 504 Deep Learning (3-0-3)

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 

Note: Not to be taken for credit with ICS 471

 

STAT 523: Forecasting Methods (3-0-3)

Time Series Basics; Autocorrelation; Modeling and forecasting with ARIMA models; Seasonal models; Smoothing and decomposition methods; Model validation; Parameter Estimation; Model diagnostics; Hypothesis testing; Model selection; Post-modeling Estimation; Forecasting with Multivariate GARCH models, Time Series Regression; High dimensional time series forecasting; Forecasting with State Space Models; projects using R software and Stats models.

Prerequisite: STAT 501

ICS 574 Big Data Analytics (3-0-3)

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: Not to be taken for credit with ICS 474

 

MATH 619 Project (0-0-6)

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.

Prerequisite: Graduate Standing