Data Science, M.S.
Curriculum
Major Requirements
| Fundamental Courses | Credits: | |
| DTSC 610 | Programming for Data Science | 3 |
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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. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 2-2-3 |
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| DTSC 615 | Optimization Methods for Data Science | 3 |
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Prerequisite: Corequisites: DTSC 635 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. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| DTSC 620 | Statistics for Data Science | 3 |
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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. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| DTSC 701 | Introduction to Big Data | 3 |
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Prerequisite: Prerequisite: DTSC 610 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. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| DTSC 710 | Machine Learning | 3 |
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Prerequisite: Prerequisite: DTSC 615 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. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| Total: 15 Credits | ||
| Students must choose either Thesis Track or Non-Thesis/Project Track (below) | ||
| Thesis Track | Credits: | |
| DTSC 890 | MS Thesis I | 3 |
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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. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| DTSC 891 | MS Thesis II | 3 |
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Prerequisite: Prerequisite: DTSC 890 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. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| ELECTIVES | Consult with program chair/program advisor on any electives. | 9 |
| Please view all course descriptions: http://www.nyit.edu/courses | ||
| Total: 15 Credits | ||
| Non-Thesis/Project Track | Credits: | |
| DTSC 870 | MS Project I | 3 |
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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. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| ELECTIVES | Consult with program chair/program advisor on any electives. | 12 |
| Please view all course descriptions: http://www.nyit.edu/courses | ||
| Total: 15 Credits | ||
| Total Required Credits = 30 |