This course provides a hands-on introduction to widely-used tools for data science. Topics include computational hardware and Linux; languages and packages for statistical analysis and visualization; parallel computing and Spark; libraries for machine learning and deep learning; databases including NoSQL; and cloud services.

PREREQUISITES: CSC 161, CSC 171 or some equivalent introductory programming experience strongly recommended.

Location

Bausch & Lomb Room 109 (MW 9:00AM - 10:15AM)

Data intensive applications (DIA) are an important part of many valuable services that we rely on in our day to day lives.  These applications in most cases are built by performing data engineering and data science at scale.  Scale in this case implies data volume and compute capacity far outside of what is available on a single machine and its narrow connection to the internet.  This course will focus on how to develop data intensive applications at scale in the Cloud. The course will be structured with lecture content and programming labs using Python and SQL on Databricks Unified Analytics Platform. Grading will be based on programming homework and a final project that demonstrates clear understanding of how to orchestrate the complete DIA pipeline to deliver business value in a commercial transportation application.

PREREQUSITE: DSCC 201/401 or instructor permission

Location

Gavett Hall Room 202 (MW 4:50PM - 6:05PM)

This course introduces students to the methods involved in turning real objects into virtual ones using cutting edge digital imaging technology and image rendering techniques. Focusing on manuscripts, paintings, maps, and 3D artifacts, students will learn the basics of multispectral imaging, photogrammetry, and Reflectance Transformation Imaging, and spectral image processing using ENVI and Photoshop. These skills will be applied to data from the ongoing research of the Lazarus Project as well as to local cultural heritage collections.

Location

Rettner Hall Room 307 (TR 11:05AM - 12:20PM)

Fundamental concepts and techniques of data mining, including data attributes, data visualization, data pre-processing, mining frequent patterns, association and correlation, classification methods, and cluster analysis. Advanced topics include outlier detection, stream mining, and social media data mining. CSC 440, a graduate-level course, requires additional readings and a course project.

Location

Dewey Room 1101 (TR 4:50PM - 6:05PM)

This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.

Location

Dewey Room 2162 (MW 3:25PM - 4:40PM)

This course explores the internals of data engines. Topics covered will include the relational model; relational database design principles based on dependencies and normal forms; query execution; transactions; recovery; query optimization; parallel query processing; NoSQL. 

Location

Gavett Hall Room 312 (MW 9:00AM - 10:15AM)

The course provides an introduction to modern machine learning concepts, techniques, and algorithms. Topics discussed include regression, clustering and classification, kernels, support vector machines, feature selection, goodness of fit, neural networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets. Students will be expected to work with Python programming environment to complete the assignments. 

PRE-REQUISITES: 

1) DSCC/CSC/TCS 462 or STAT 212 or STAT 213 or equivalent introductory statistics background.

2) Introductory programming in Python or equivalent background in another programming language.

3) Knowledge of data mining/machine learning.

Location

Wegmans Room 1400 (MW 2:00PM - 3:15PM)

The capstone/practicum provides an experience for data science majors/MS candidates to apply the core knowledge and skills attained during their program to a tangible data science focused project. Students will work in small teams on a project that applies data science methods to the analysis of a real-world problem. The instructor will guide each team in developing a topic that makes use of the knowledge the team members gained through their application area courses. The identified projects or problems and data sets will cover a range of application areas and reflect real-world needs from industry, medicine and government. Each student will be required to write a paper about their project, which satisfies one upper-level writing requirement for majors and Plan B for master's.

PREREQUISITES: DSCC 240/440 (Data Mining) AND an introductory statistics course such as DSCC 462 or equivalent; DSCC 261/461 (Database Systems) strongly recommended prior but may be taken concurrently. FOR GRADUATING MS Candidate ONLY.

PERMISSION REQUEST: To seek instructor permission/eligibility, follow directions on UR Student. https://tech.rochester.edu/wp-content/uploads/QRC-Requesting-Permission-to-Register_UofR-_0200227_cmf.pdf

Location

Bausch & Lomb Room 106 (MW 10:25AM - 11:40AM)

The course will involve readings from current literature on machine learning and artificial intelligence applications for medical data analytics.

Meetings every other week and presentation at the end of the semester.

To register for Independent Study, contact program advisor before registering.

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must have GEPA Approval of Graduate Practical Research Internship

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Description of Research:

Using Deep Learning-based approaches to classify lung ultrasounds into Normal and Diseased.

Goal 1: A Baseline model using U-Net will be developed to perform frame classification. A new Clip classification method will be developed using output of Frame classifier.

Goal 2: Independently, a new (sequential or similar) model will also be developed that takes in frames from clips, and classifies each clip as normal or abnormal . Some data labeling code development may be involved in this task.

For Goal 1 and 2, Student will propose and lead implementation of new classifier.

Evaluation of grade will be based on the following factors:

1.            Regular attendance at weekly and bi-weekly meetings as scheduled. Student will prepare slides to document progress and present at the meeting. The slides should display professionalism (good formatting, clear explanation etc).

2.            Literature survey of ultrasound methods for lung disease detection presented

3.            Code repository diligently maintained and documented via Github. The repo can be shared across team members for collaboration.

4.            End of semester report documenting student’s individual contribution, results and outcomes.

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Please see advisor before enrolling.

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The science of networks is an emerging discipline of great importance that combines graph theory, probability and statistics, and facets of engineering and the social sciences. This course will provide students with the mathematical tools and computational training to understand large-scale networks in the current era of Big Data. It will introduce basic network models and structural descriptors, network dynamics and prediction of processes evolving on graphs, modern algorithms for topology inference, community and anomaly detection, as well as fundamentals of social network analysis. All concepts and theories will be illustrated with numerous applications and case studies from technological, social, biological, and information networks. Prerequisites;

Some mathematical maturity, comfortable with linear algebra, probability, and analysis (e.g., MTH164-165). Exposure to programming and Matlab useful, but not required.

Location

Computer Studies Room 601 (MW 3:25PM - 4:40PM)