Sign In
 

 MX Programs

Degree Plan for a professional Master of Data Science and Analytics

(Choice A)

Course #TitleLTLBCR
First Semester   
MATH503Mathematics for Data Science303
STAT503Probability and Statistics for Data Science303
 606
Second Semester   
MATH506Fundamentals of Data Science303
STAT513Statistical Modeling303
ICS502Machine Learning303
MATH619Project00IP
   909
Third Semester   
STAT523Forecasting Methods303
ICS504Deep Learning303
ICS574Big Data Analytics303
MATH619Project006
   9015
  Total Credit Hours  30


Degree Plan for a professional Master of Data Science and Analytics

(Choice B)

Course #TitleLTLBCR
First Trimester (Fall)
MATH503Mathematics for Data Science303
MATH506Fundamentals of Data Science303
STAT503Probability and Statistics for Data Science303
ICS502Machine Learning303
 12012
Second Trimester (Spring)
STAT513Statistical Modeling303
STAT523Forecasting Methods303
ICS504Deep Learning303
MATH619Project00IP
   909
Third Trimester (Summer)   
ICS574Big Data Analytics303
MATH619Project006
   309
  Total Credit Hours  30


Course Descriptions for a Professional Master Program in Data Science and Analytics

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

352