Data Science
Courses
DSCI 501 Quantitative Foundations of Data Science 3.0 Credits
Linear algebra, calculus, probability and statistical methods are essential foundation areas required for an effective understanding and application of data science. In this course, students will get a gentle introduction to these important areas of quantitative reasoning. Along with introducing basics of linear algebra, calculus, probability, and statistical methods, this course will also introduce their computational application through the Python programming language. Concepts will be demonstrated using various python packages.
Repeat Status: Not repeatable for credit
DSCI 511 Data Acquisition and Pre-Processing 3.0 Credits
Introduces the breadth of data science through a project lifecycle perspective. Covers early-stage data-life cycle activities in depth for the development and dissemination of data sets. Provides technical experience with data harvesting, acquisition, pre-processing, and curation. Concludes with an open-ended term project where students explore data availability, scale, variability, and reliability.
Repeat Status: Not repeatable for credit
Prerequisites: CS 570 [Min Grade: C] (Can be taken Concurrently)
DSCI 521 Data Analysis and Interpretation 3.0 Credits
Introduces methods for data analysis and their quantitative foundations in application to pre-processed data. Covers reproducibility and interpretation for project life cycle activities, including data exploration, hypothesis generation and testing, pattern recognition, and task automation. Provides experience with analysis methods for data science from a variety of quantitative disciplines. Concludes with an open-ended term project focused on the application of data exploration and analysis methods with interpretation via statistical, algorithmic, and mathematical reasoning.
Repeat Status: Not repeatable for credit
Prerequisites: DSCI 501 [Min Grade: C] (Can be taken Concurrently)
DSCI 591 Data Science Capstone I 3.0 Credits
Explores data science in practice as an open-ended team activity. Initiates an in-depth multi-term capstone study applying computing and informatics knowledge in a data science project. Teams work to develop a significant product with advisors from industry and/or academia. Explores data science-related issues and challenges involved in the application domain of the team’s choice. Applies a development process structure for project planning, specification, design, implementation, evaluation, and documentation. This course should be taken towards the end of a student's program.
Repeat Status: Not repeatable for credit
DSCI 592 Data Science Capstone II 3.0 Credits
Explores data science in practice as an open-ended team activity. Completes an in-depth multi-term capstone study applying computing and informatics knowledge in a data science project. Teams work to develop a significant product with advisors from industry and/or academia. Explores data science-related issues and challenges involved in the application domain of the team’s choice. Applies a development process structure for project planning, specification, design, implementation, evaluation, and documentation. This course should be taken toward the end of the student's program.
Repeat Status: Not repeatable for credit
Prerequisites: DSCI 591 [Min Grade: C]
DSCI 631 Applied Machine Learning for Data Science 3.0 Credits
Introduces relevant topics in the life cycle of machine learning: extracting and engineering features, tuning parameters, comparing algorithms, interpreting results, and analyzing errors. Students will be exposed to various representative algorithms in the concept level and learn their trade-offs. Students will gain hands-on experiences with assignments and a term project. Students will be prepared to attack new problems using various machine learning methods and be able to compare the performance of different algorithms for the term project.
Repeat Status: Not repeatable for credit
Prerequisites: DSCI 521 [Min Grade: C] (Can be taken Concurrently)
DSCI 632 Applied Cloud Computing 3.0 Credits
The course will cover different technologies in cloud computing. This course focuses on the frameworks and algorithms used in the distributed processing of massive datasets. It will explore both batch and streaming data processing and examine the theory behind their algorithmic approaches. Students will gain practical experience by using cloud computing to solve real world problems.
Repeat Status: Not repeatable for credit
Prerequisites: DSCI 521 [Min Grade: C] (Can be taken Concurrently)
DSCI 641 Recommender Systems for Data Science 3.0 Credits
Recommender systems help users to discover new products and services. The goal is generating meaningful recommendation to a collection of users with items or products that might interest them. Recommender systems are encountered on multiple domains such as e-commerce, content and media distribution, social media, and more. The course will cover fundamental and practical aspects of Recommender systems focusing on the data science approach. The course includes topics and concepts for recommender systems: collaborative filtering, content-based recommendation, knowledge-based recommendation, hybrid recommendation, attack-resistance recommendation, and evaluation of the recommender systems. Students will gain hands-on experiences with assignments and a term project.
Repeat Status: Not repeatable for credit
Prerequisites: DSCI 521 [Min Grade: C]
DSCI 691 Natural Language Processing with Deep Learning 3.0 Credits
Natural Language Processing (NLP) is one of the most important technologies of the information age and is a critical component to AI. Recently, deep learning approaches have overtaken the domain. This course explores the basis of these neural models with a heavy emphasis on research.
Repeat Status: Not repeatable for credit
Prerequisites: DSCI 521 [Min Grade: C], CS 613 [Min Grade: C], CS 615 [Min Grade: C] (Can be taken Concurrently)
DSCI T780 Special Topics in Data Science 3.0 Credits
This course is a special topics numbering intended to afford a curricular place for special and developing topics in data science at a graduate level.
Repeat Status: Can be repeated multiple times for credit
