Data Science MS

Data Science, MS degree, typical course sequence (on-campus program)

Course Sem. Cr. Hrs.
First Year
DSCI-601
Applied Data Science I
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).
3
DSCI-633
Foundations of Data Science and Analytics
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. Major families of data analysis techniques covered include classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of programming assignments which will involve implementation of specific techniques on practical datasets from diverse application domains, reinforcing the concepts and techniques covered in lectures. Lecture 3 (Fall, Spring).
3
DSCI-644
Software Engineering for Data Science
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: DSCI-623 or equivalent course.) Lecture 3 (Spring).
3
ISTE-608
Database Design And Implementation
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. (Prerequisite: ISTE-200 or equivalent course.) Lec/Lab 4 (Fall).
3
STAT-614
Applied Statistics
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).
3
SWEN-601
Software Construction
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. (Prerequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall).
3
 
Electives
3
Second Year
DSCI-602
Applied Data Science II
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. (Prerequisites: DSCI-601 or equivalent course. Co-requisites: DSCI-681 or equivalent course.) Lecture 3 (Spring).
3
 
Electives
6
Total Semester Credit Hours
30

 

Data Science, MS degree, typical course sequence (online program)

Course Sem. Cr. Hrs.
First Year
DSCI-633
Foundations of Data Science and Analytics
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. Major families of data analysis techniques covered include classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of programming assignments which will involve implementation of specific techniques on practical datasets from diverse application domains, reinforcing the concepts and techniques covered in lectures. Lecture 3 (Fall, Spring).
3
ISTE-608
Database And Implementation
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. (Prerequisite: ISTE-200 or equivalent course.) Lec/Lab 4 (Fall).
3
STAT-614
Applied Statistics
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).
3
SWEN-601
Database Design And Implementation
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. (Prerequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall).
3
 
Elective
3
Second Year
DSCI-644
Software Engineering for Data Science
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: DSCI-623 or equivalent course.) Lecture 3 (Spring).
3
DSCI-799
Graduate Capstone
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).
3
 
Electives
9
Total Semester Credit Hours
30

 

Data Science, MS degree, typical course sequence (online + edX program)

Course Sem. Cr. Hrs.
First Year
ISTE-608
Database Design And Implementation
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. (Prerequisite: ISTE-200 or equivalent course.) Lec/Lab 4 (Fall).
3
SWEN-601
Software Construction
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. (Prerequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall).
3
 
edX Micromasters
9
 
Elective
3
Second Year
DSCI-644
Software Engineering for Data Science
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: DSCI-623 or equivalent course.) Lecture 3 (Spring).
3
DSCI-799
Graduate Capstone
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).
3
 
Electives
6
Total Semester Credit Hours
30