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 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)

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Location

Hylan Building Room 303 (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.

Prerequisites will be strictly enforced: CSC171, CSC 172 and MATH 161.

Recommended: CSC 242 or CSC262; MATH165.

Location

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

Introduces fundamental principles and techniques from Artificial Intelligence, including heuristic search, automated reasoning, handling uncertainty, and machine learning, to prepare students for advanced AI courses.

Prerequisites: CSC 172 and MTH 150; CSC 173 STRONGLY Recommended. AUDITS NOT ALLOWED.

Location

Wegmans Room 1400 (TR 9:40AM - 10:55AM)

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.

Prerequisites: CSC 172; CSC 173 and CSC 252 recommended.

Note: Course is open only to undergraduate Computer Science and Data Science students during registration week. The restriction will be lifted Monday, November 20th.

Location

Dewey Room 2162 (MW 10:25AM - 11:40AM)

This course explores the relational data model, the theory of database design, the use of databases in applications, and the internals of relational database engines. Topics covered will include the relational model and SQL; relational database design principles based on dependencies and normal forms; database topics from the application-building perspective, including indexes, views, transaction, and integrity constraints; query evaluation and optimization.

Restricted to CSC & DSCC majors only during registration week. Restriction will be lifted Monday, November 20th.

Prerequisites: CSC 173 and CSC 252 (or CSC 261)

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:  DSCC/CSC/STAT 262 or STAT 212 or STAT 213 (or equivalent introductory statistics) background AND DSCC240 (or equivalent data mining course) or permission of instructor.

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: DSC 240/440 (Data Mining) AND an introductory statistics course such as DSCC 262/462, STT212 or STT213 or equivalent. DSC 261/461 (Database Systems) strongly recommended prior but may be taken concurrently. ONLY GRADUATING SENIORS and MS CANDIDATES allowed this semester.

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)