**Degree Plan for a professional Master of Data Science and Analytics**

**(Choice A)**

Course # | | Title | LT | LB | CR |

First Semester | | | | | |

MATH | 503 | Mathematics for Data Science | 3 | 0 | 3 |

STAT | 503 | Probability and Statistics for Data Science | 3 | 0 | 3 |

| | 6 | 0 | 6 | |

Second Semester | | | | | |

MATH | 506 | Fundamentals of Data Science | 3 | 0 | 3 |

STAT | 513 | Statistical Modeling | 3 | 0 | 3 |

ICS | 502 | Machine Learning | 3 | 0 | 3 |

MATH | 619 | Project | 0 | 0 | IP |

9 | 0 | 9 | |||

Third Semester | | | | | |

STAT | 523 | Forecasting Methods | 3 | 0 | 3 |

ICS | 504 | Deep Learning | 3 | 0 | 3 |

ICS | 574 | Big Data Analytics | 3 | 0 | 3 |

MATH | 619 | Project | 0 | 0 | 6 |

9 | 0 | 15 | |||

Total Credit Hours | | | 30 |

**Degree Plan for a professional Master of Data Science and Analytics**

**(Choice B)**

Course # | | Title | LT | LB | CR |

First Trimester (Fall) | | | | | |

MATH | 503 | Mathematics for Data Science | 3 | 0 | 3 |

MATH | 506 | Fundamentals of Data Science | 3 | 0 | 3 |

STAT | 503 | Probability and Statistics for Data Science | 3 | 0 | 3 |

ICS | 502 | Machine Learning | 3 | 0 | 3 |

| | 12 | 0 | 12 | |

Second Trimester (Spring) | | | | | |

STAT | 513 | Statistical Modeling | 3 | 0 | 3 |

STAT | 523 | Forecasting Methods | 3 | 0 | 3 |

ICS | 504 | Deep Learning | 3 | 0 | 3 |

MATH | 619 | Project | 0 | 0 | IP |

9 | 0 | 9 | |||

Third Trimester (Summer) | | | | | |

ICS | 574 | Big Data Analytics | 3 | 0 | 3 |

MATH | 619 | Project | 0 | 0 | 6 |

3 | 0 | 9 | |||

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.

**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*