Data Science

Name	Title	Credits	School
DTSC 501	Fundamental Tools for Data Science	3	College of Eng & Comp Sciences
This is a prerequisite course for students in the Master's program in Data Science who do not have a computer science background. This course covers various fundamental skills necessary for data science. Topics covered in this course include the Python programming language, relational databases and the SQL language, computer science basics, and command line interfaces. Classroom Hours- Laboratory and/or Studio Hours- Course Credits: 3-0-3
DTSC 502	Fundamental Probability and Statistics for Data Science	3	College of Eng & Comp Sciences
This is a prerequisite course for the Master’s program in Data Science who do not have probability and statistics background. This course covers basic concepts in probability theory and illustrates its applications to computer science. The course covers probability spaces, random variables, distributions and density functions, expectations, sampling, limit theorems, statistical inference and hypothesis testing, as well as additional topics such as large deviations, client-server system and Markov chains, as they apply to computing.
DTSC 610	Programming for Data Science	3	College of Eng & Comp Sciences
This course will introduce basic programming concepts (i.e. in Python and R), and techniques including data structures (vector, matrix, list, data frame, factor), basic and common operations/concepts (indexing, vectorization, split, subset), data input and output, control structures and functions. Other topics will include string operations (stringr package) and data manipulation techniques (dplyr, reshape2 packages). The course will also explore data mining, such as probability basics/data exploration, clustering, regression, classification, graphics and debugging.
DTSC 615	Optimization Methods for Data Science	3	College of Eng & Comp Sciences
Basic concepts in optimization are introduced. Linear optimization (linear and integer programming) will be introduced including solution methods like simplex and the sensitivity analysis with applications to transportation, network optimization and task assignments. Unconstrained and constrained non-linear optimization will be studied and solution methods using tools like Matlab/Excel will be discussed. Extensions to game theory and computational methods to solve static, dynamic games will be provided. Decision theory algorithms and statistical data analysis tools (Z-test, t-test, F-test, Bayesian algorithms and Neyman Pearson methods) will be studied. Linear and non-linear regression techniques will be explored. Prerequisite Course(s): Corequisites: DTSC 635
DTSC 620	Statistics for Data Science	3	College of Eng & Comp Sciences
This course presents a range of methods in descriptive statistics, frequentist statistics, Bayesian statistics, hypothesis testing, and regression analysis. Topics includes point estimation, confidence interval estimation, nonparametric model estimation, parametric model estimation, Bayesian parametric models, Bayesian estimators, parametric testing, nonparametric testing, simple and multiple linear regression models, logistic regression model.
DTSC 630	Data Visualization	3	College of Eng & Comp Sciences
This course is designed to provide an introduction to the fundamental principles of designing and building effective data visualizations. Students will learn about data visualization principles rooted in graphic design, psychology and cognitive science, and how to the use these principles in conjunction with state-of-the-art technology to create effective visualizations for any domain. Students who have taken this course will not only understand the current state-of-the-art in data visualization but they will be capable of extending it.
DTSC 635	Probability and Stochastic Processes	3	College of Eng & Comp Sciences
This course starts with a review of the elements of probability theory such as: axioms of probability, conditional and independent probabilities, random variables, distribution functions, functions of random variables, statistical averages, and some well-known random variables such as Bernoulli, geometry, binomial, Pascal, Gaussian, and Poisson. The course introduces more advanced topics such as stochastic processes, stationary processes, correlations, statistical signal processing, and well-known processes such as Brownian motion, Poisson, Gaussian, and Markov. Prerequisite: Undergraduate level knowledge of probability theory.
DTSC 662	Special Topic in Data Science	3	College of Eng & Comp Sciences
This course is designed to offer advanced topics related to data science. The specific topics of the course will be determined by the interest of both the students and the instructor, and approved by department chair.
DTSC 701	Introduction to Big Data	3	College of Eng & Comp Sciences
This course provides an overview of big data applications ranging from data acquisition, storage, management, transfer, to analytics, with focus on the state-of-the-art technologies, tools, and platforms that constitute big-data computing solutions. Real-life big data applications and workflows are introduced as well as use cases to illustrate the development, deployment, and execution of a wide spectrum of emerging big-data solutions. Prerequisite Course(s): Prerequisite: DTSC 610
DTSC 710	Machine Learning	3	College of Eng & Comp Sciences
In this course, students will learn important machine learning (ML) and data mining concepts and algorithms. Emphasis is on basic ideas and intuitions behind ML methods and their applications in activity recognition, and anomaly detection. This course will cover core ML topics such as classification, clustering, feature selection, Bayesian networks, and feature extraction. Classroom teaching will be augmented with experiments performed on machine learning systems. Student understanding and progress will be measured through quizzes, exams, homework, project assii.mments, proposals, term-paper reports, and presentations. Prerequisite Course(s): Prerequisite: DTSC 615
DTSC 740	Deep Learning	3	College of Eng & Comp Sciences
This course presents a range of topics from basic neural networks, convolutional and recurrent network structures, deep unsupervised and reinforcement learning, and applications to problem domains like speech recognition and computervision. Classroom Hours- Laboratory and/or Studio Hours- Course Credits: 3-0-3 Prerequisite Course(s): Prerequisites: DTSC 620, DTSC 710
DTSC 760	Biometrics	3	College of Eng & Comp Sciences
Biometrics has emerged as an important tool for user identification and authentication in security-critical applications, both the physical and virtual world. At its core, biometrics is an application of machine learning and anomaly detection. This course introduces biometrics concepts by building on machine learning and anomaly detection, and shows how state-of-the-art machine learning techniques are currently applied to biometric authentication. The course covers core biometric topics, and discusses the innovations made in the past decade. The course also concentrates on emerging biometric applications and their privacy, security, and usability, implications in a networked society. Prerequisite Course(s): Prerequisite: DTSC 710
DTSC 870	Project I	3	College of Eng & Comp Sciences
In this course students carry out independent research in a significant technical area of data science. The student is to investigate a technical area, research it, advance it in some way if possible, and report on the learning and advancements made. A written report is required that summarizes the findings and any advancements made to the technology.
DTSC 890	Master's Thesis I	3	College of Eng & Comp Sciences
This is the first of a two course sequence. The master's thesis provides an opportunity for students to generate new knowledge in a specific topic that falls within the field of Data Science. This course requires the student to explore an original and appropriately phrased research question, to present creative thoughts and initiatives, and demonstrate ability to carry out and document a comprehensive paper in the chosen research area with a good deal of individual responsibility. In consultation with the thesis advisor, the student develops and presents a written thesis proposal on an original research question. The preliminary draft of the thesis document is prepared and presented to the thesis advisor by the end of this course.
DTSC 891	Master's Thesis II	3	College of Eng & Comp Sciences
This is the second of a two course sequence for master's thesis. The student must give an oral presentation of the thesis project in front of a committee consisting of the student's thesis advisor and other members. The student will complete and present a master's thesis by the end of this course that culminates in a publication-quality paper and is archived in the NYIT library. Prerequisite Course(s): Prerequisite: DTSC 890