Data Science MS - Curriculum

This is the first of a two course applied data science seminar series. Students will be introduced to the data science masters program along with potential projects which they will develop over the course of this series in con-junction with the applied data science directed studies. Students will select a project along with an advisor and sponsor, develop a written proposal for their work, and investigate and write a related work survey to refine this proposal with their findings. Students will begin preliminary design and implementation of their project. Work will be presented in class for peer review with an emphasis on developing data science communication skills. This course will keep students up to date with the broad range of data science applications. (Prerequisites: SWEN-601 and DSCI-633 and STAT-614 or equivalent courses.) Lecture 3 (Fall).

A foundations course in data science, emphasizing both concepts and techniques. The course provides an overview of data analysis tasks and the associated challenges, spanning data preprocessing, model building, model evaluation, and visualization. The major areas of machine learning, such as unsupervised, semi-supervised and supervised learning are covered by data analysis techniques including classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of assignments utilizing practical datasets from diverse application domains, which are designed to reinforce the concepts and techniques covered in lectures. A substantial project related to one or more data sets culminates the course. (This course is restricted to DATASCI-MS, INFOST-MS, SOFTENG-MS, COMPSCI-MS, or COMPIS-PHD Major students.) Lecture 3 (Fall, Spring).

This course focuses on the software engineering challenges of building scalable and highly available big data software systems. Software design and development methodologies and available technologies addressing the major software aspects of a big data system including software architectures, application design patterns, different types of data models and data management, and deployment architectures will be covered in this course. (Prerequisites: SWEN-601 and DSCI-633 or equivalent courses.) Lecture 3 (Spring).

An introduction to the theory and practice of designing and implementing database systems. Current software environments are used to explore effective database design and implementation concepts and strategies. Topics include conceptual data modeling, methodologies, logical/physical database design, normalization, relational algebra, schema creation and data manipulation, and transaction design. Database design and implementation projects are required. Lec/Lab 4 (Fall, Spring).

Statistical tools for modern data analysis can be used across a range of industries to help you guide organizational, societal and scientific advances. This course is designed to provide an introduction to the tools and techniques to accomplish this. Topics covered will include continuous and discrete distributions, descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one-way ANOVA and Chi-square tests. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall).

This is a programming based course to enhance individual, technical engineering knowledge and skills as preparation for masters level graduate work in computing. Students will be introduced to programming language syntax, object oriented concepts, data structures and foundational algorithms. An emphasis will be placed on obtaining practical programming skills, through regular programming assignments and practicum. (Corequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall).

This is the second of a three course applied data science seminar series. Students will design an implementation plan and preliminary documentation for their selected applied data science project, along with an in class presentation of this work. At the end of the semester students will present preliminary demos of their project and write a preliminary project report. Writing and presentations will be peer reviewed to further enhance data science communication skills. This course will keep students up to date with the broad range of data science applications. (Prerequisite: DSCI-601 or equivalent course.) Lecture 3 (Spring).

A foundations course in data science, emphasizing both concepts and techniques. The course provides an overview of data analysis tasks and the associated challenges, spanning data preprocessing, model building, model evaluation, and visualization. The major areas of machine learning, such as unsupervised, semi-supervised and supervised learning are covered by data analysis techniques including classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of assignments utilizing practical datasets from diverse application domains, which are designed to reinforce the concepts and techniques covered in lectures. A substantial project related to one or more data sets culminates the course. (This course is restricted to DATASCI-MS, INFOST-MS, SOFTENG-MS, COMPSCI-MS, or COMPIS-PHD Major students.) Lecture 3 (Fall, Spring).

An introduction to the theory and practice of designing and implementing database systems. Current software environments are used to explore effective database design and implementation concepts and strategies. Topics include conceptual data modeling, methodologies, logical/physical database design, normalization, relational algebra, schema creation and data manipulation, and transaction design. Database design and implementation projects are required. Lec/Lab 4 (Fall, Spring).

Statistical tools for modern data analysis can be used across a range of industries to help you guide organizational, societal and scientific advances. This course is designed to provide an introduction to the tools and techniques to accomplish this. Topics covered will include continuous and discrete distributions, descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one-way ANOVA and Chi-square tests. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall).

This is a programming based course to enhance individual, technical engineering knowledge and skills as preparation for masters level graduate work in computing. Students will be introduced to programming language syntax, object oriented concepts, data structures and foundational algorithms. An emphasis will be placed on obtaining practical programming skills, through regular programming assignments and practicum. (Corequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall).

This course focuses on the software engineering challenges of building scalable and highly available big data software systems. Software design and development methodologies and available technologies addressing the major software aspects of a big data system including software architectures, application design patterns, different types of data models and data management, and deployment architectures will be covered in this course. (Prerequisites: SWEN-601 and DSCI-633 or equivalent courses.) Lecture 3 (Spring).

This non-class-based experience provides the student with an individual opportunity to explore a project-based or a research-based project that advances knowledge in an area of data science. The student selects a problem, conducts background research, develops the system or devises a research approach, analyses the results, and builds a professional document and presentation that disseminates the project. The report must include a literature review. The final report structure is to be determined by the capstone advisor. Ind Study (Fall, Spring, Summer).

An introduction to the theory and practice of designing and implementing database systems. Current software environments are used to explore effective database design and implementation concepts and strategies. Topics include conceptual data modeling, methodologies, logical/physical database design, normalization, relational algebra, schema creation and data manipulation, and transaction design. Database design and implementation projects are required. Lec/Lab 4 (Fall, Spring).

This is a programming based course to enhance individual, technical engineering knowledge and skills as preparation for masters level graduate work in computing. Students will be introduced to programming language syntax, object oriented concepts, data structures and foundational algorithms. An emphasis will be placed on obtaining practical programming skills, through regular programming assignments and practicum. (Corequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall).

This course focuses on the software engineering challenges of building scalable and highly available big data software systems. Software design and development methodologies and available technologies addressing the major software aspects of a big data system including software architectures, application design patterns, different types of data models and data management, and deployment architectures will be covered in this course. (Prerequisites: SWEN-601 and DSCI-633 or equivalent courses.) Lecture 3 (Spring).

This non-class-based experience provides the student with an individual opportunity to explore a project-based or a research-based project that advances knowledge in an area of data science. The student selects a problem, conducts background research, develops the system or devises a research approach, analyses the results, and builds a professional document and presentation that disseminates the project. The report must include a literature review. The final report structure is to be determined by the capstone advisor. Ind Study (Fall, Spring, Summer).