Professional Master in Data Science & Analytics

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. 

Admission requirements

Admission to the professional master's program in Data Science & Analytics (MxDSA) 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 MxDSA 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. Minimum GRE score in quantitative section: 156

5. IELTS score of 6+ or TOEFL of 70+ (waived for KFUPM graduates)

6. Two recommendation or reference letters

7. Required preparatory courses include undergraduate courses in calculus, linear algebra, probability and statistics, differential equations, numerical methods, and programming. (See details below for each program) 

The admission process goes beyond meeting the minimum requirements.

The list of courses, offered at KFUPM, which are equivalent to the required preparatory undergraduate instruction in calculus, linear algebra, probability and statistics, differential equations, numerical methods, and programming are given below:

• Math  101, Math  102, Math  201, Math  208 or 225, ICS 104, and any one of Stat 201, 212, 214, 319
  

Degree Plan

Two Years Degree Plan of MX-Data Science & Analytics

Course Descriptions

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

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.

Note: Not to be taken for credit with ICS 471

Prerequisite: ICS 502 or Consent of Instructor

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.

Note: Not to be taken for credit with ICS 474

Prerequisite: Graduate Standing

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

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 (3-0-3)

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

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

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

STAT 513 Statistical Modeling (3-0-3)

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.

Note: Not to be taken for credit with STAT 413

Prerequisite: STAT 503

STAT 523 Forecasting Methods (3-0-3)

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.

Note: Not to be taken for credit with ISE 487

Prerequisite: STAT 503