DATA Courses

Here is information about DATA class enrollment for spring 2025. Classes with no meeting time listed are not shown. Feel free to contact me with any questions/comments/issues. I am happy to add any departments that are missing from these listings, just reach out to ask!

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Data also available for: COMP, AAAD, AMST, ANTH, APPL, ASTR, BCB, BIOC, BIOL, BIOS, BMME, BUSI, CHEM, CLAR, CMPL, COMM, DATA, DRAM, ECON, EDUC, EMES, ENEC, ENGL, ENVR, EPID, EXSS, GEOG, HBEH, INLS, LING, MATH, MEJO, NSCI, PHIL, PHYS, PLAN, PLCY, POLI, PSYC, SOCI, STOR, WGST

Data last updated: 2025-01-23 12:12:38.095167

Class Number	Class	Meeting Time	Instructor	Room	Unreserved Enrollment	Reserved Enrollment	Total Enrollment	Wait List
13265	DATA 110 - 001 Introduction to Data Science	MoWe 3:35PM - 4:25PM	Can Chen	Chapman Hall-Rm 0201	32/70	18/30	50/100	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 3 units.
13266	DATA 110 - 002 Introduction to Data Science	MoWe 1:25PM - 2:15PM	Richard Marks	Murphey Hall-Rm 0116	Seats filled	29/30	99/100	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 3 units.
13267	DATA 110 - 003 Introduction to Data Science	MoWe 1:25PM - 2:15PM	Harlin Lee	Chapman Hall-Rm 0201	68/70	Seats filled	98/100	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 3 units.
13622	DATA 110 - 004 Introduction to Data Science	MoWe 2:30PM - 3:20PM	Chudi Zhong	Global Education, F-Rm 1015	37/70	21/30	58/100	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 3 units.
13307	DATA 110 - 601 Introduction to Data Science	Fr 3:35PM - 4:25PM	Can Chen	Hanes Hall-Rm 0107	26/33	Seats filled	26/33	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
13308	DATA 110 - 602 Introduction to Data Science	Fr 3:35PM - 4:25PM	Can Chen	Global Education, F-Rm 3024	15/33	Seats filled	15/33	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
13309	DATA 110 - 603 Introduction to Data Science	Fr 3:35PM - 4:25PM	Can Chen	Alumni Bldg-Rm 0207	9/34	Seats filled	9/34	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
13314	DATA 110 - 604 Introduction to Data Science	Fr 1:25PM - 2:15PM	Richard Marks	Greenlaw Hall-Rm 0301	32/34	Seats filled	32/34	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
13315	DATA 110 - 605 Introduction to Data Science	Fr 1:25PM - 2:15PM	Richard Marks	Greenlaw Hall-Rm 0305	Seats filled	Seats filled	33/33	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
13316	DATA 110 - 606 Introduction to Data Science	Fr 1:25PM - 2:15PM	Richard Marks	Greenlaw Hall-Rm 0319	Seats filled	Seats filled	34/34	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
13317	DATA 110 - 607 Introduction to Data Science	Fr 1:25PM - 2:15PM	Harlin Lee	Wilson Hall-Rm 0217	32/33	Seats filled	32/33	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
13318	DATA 110 - 608 Introduction to Data Science	Fr 1:25PM - 2:15PM	Harlin Lee	Hamilton Hall-Rm 0452	Seats filled	Seats filled	33/33	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
13319	DATA 110 - 609 Introduction to Data Science	Fr 1:25PM - 2:15PM	Harlin Lee	Genome Sciences Bui-Rm 1374	33/34	Seats filled	33/34	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
14713	DATA 110 - 610 Introduction to Data Science	Fr 2:30PM - 3:20PM	Chudi Zhong	Hamilton Hall-Rm 0452	29/33	Seats filled	29/33	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
14714	DATA 110 - 611 Introduction to Data Science	Fr 2:30PM - 3:20PM	Chudi Zhong	Hanes Hall-Rm 0112	21/33	Seats filled	21/33	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
14715	DATA 110 - 612 Introduction to Data Science	Fr 2:30PM - 3:20PM	Chudi Zhong	Alumni Bldg-Rm 0207	8/34	Seats filled	8/34	0/999
Description: This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. 0 units.
13268	DATA 120 - 001 Ethics of Data Science and Artificial Intelligence	MoWeFr 12:20PM - 1:10PM	Justin Sola	Genome Sciences Bui-Rm G100	78/80	Seats filled	118/120	0/999
Description: In an era of rapid advancements in data science and AI, ethical concerns related to data-intensive technologies are now of utmost importance. This course immerses students in data science ethics, facilitating a comprehensive exploration of the intricate interplay between data and societal values. By nurturing critical thinking grounded in ethical theories, this course provides students with a strong foundation in designing and analyzing data-intensive ecosystems that emphasize values such as fairness, accountability, ethics, and transparency. 3 units.
13269	DATA 120 - 002 Ethics of Data Science and Artificial Intelligence	MoWeFr 4:40PM - 5:30PM	Hsun-ta Hsu	Greenlaw Hall-Rm 0101	69/80	14/20	83/100	0/999
Description: In an era of rapid advancements in data science and AI, ethical concerns related to data-intensive technologies are now of utmost importance. This course immerses students in data science ethics, facilitating a comprehensive exploration of the intricate interplay between data and societal values. By nurturing critical thinking grounded in ethical theories, this course provides students with a strong foundation in designing and analyzing data-intensive ecosystems that emphasize values such as fairness, accountability, ethics, and transparency. 3 units.
13270	DATA 130 - 001 Critical Data Literacy	MoWeFr 10:10AM - 11:00AM	Alex McAvoy	Woollen Gym-Rm 0304	49/50	Seats filled	49/50	0/999
Description: How do you become data literate? Data literacy is the ability to read, write, and communicate data in context, or in other words: perform data analysis, construct a data visualization, and then communicate that data. It is the story that gets told with the data. Data literacy helps us to understand data, learn about different types and scales of data, and understand why this is important in the world today. 3 units.
13272	DATA 140 - 001 Introduction to Data Structures and Management	MoWeFr 12:20PM - 1:10PM	Jack Snoeyink	Greenlaw Hall-Rm 0101	12/70	8/30	20/100	0/999
Description: Data structures provide a means to manage large amounts of data for use in our databases and indexing services. A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose. Data structures make it easy for users to access and work with the data they need in appropriate ways. 3 units.
13273	DATA 140 - 002 Introduction to Data Structures and Management	TuTh 3:30PM - 4:45PM	Huaxiu Yao	Genome Sciences Bui-Rm G200	25/70	29/30	54/100	0/999
Description: Data structures provide a means to manage large amounts of data for use in our databases and indexing services. A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose. Data structures make it easy for users to access and work with the data they need in appropriate ways. 3 units.
16865	DATA 481 - 001 Data Science Practicum	TuTh 12:30PM - 1:45PM	James Marron	Hanes Hall-Rm 0125	25/30	Seats filled	25/30	0/999
Description: Prerequisites, MATH 347 and STOR 455 and STOR 120 or 320; permission of the instructor for students lacking the prerequisites. This course is designed to give undergraduate students hands-on data science experience using real-world research requests. Students will learn and use the Data Science Project Cycle framework to complete a project from an external client. Clients from an array of industries, as well as those from within UNC, will submit project requests that are feasible within a 12-week period. The course will offer an authentic learning experience to develop students' research skills and professional attributes, such as teamwork, communication, and project management, in preparation for workforce entry. 3 units.
15292	DATA 540 - 001 Introduction to Risk Management and Insurance	TuTh 12:30PM - 1:45PM	Rachel Baum	Peabody Hall-Rm 3050	Seats filled	Seats filled	16/16	0/999
Description: Pre- or corequisite, Two or more of the following classes (or permission of the instructor): MATH 231, MATH 232, STOR 151, STOR 155, BIOS 511, BIOS 512, BIOS 600, ECON 400, BIOL/ENEC 562 . Introduces the motivations, objectives, and principles of financial risk management through the lens of insurance, reinsurance and financial institutions. Students will become familiar with key concepts that shape these industries so they can effectively communicate using industry vocabulary, metrics, and tools. Standards governing financial risk management are introduced as are the different types of risks that financial institutions, insurers and reinsurers analyze when conducting business. Students will make use of software and tools to characterize and price risk in various activities, carry out basic quantitative risk assessments, and learn what drives success and failure in financial risk management. 3 units.
15310	DATA 541 - 001 Natural Hazards and Financial Risk	MoWe 10:10AM - 11:25AM	Greg Characklis	Murphey Hall-Rm 0204	9/10	Seats filled	9/10	0/999
Description: Pre- or corequisite, At least 2 of the following courses in mathematics or statistics (or permission of instructor): MATH 231, MATH 232, STOR 151, STOR 155, BIOS 511, BIOS 512, BIOS 600, ECON 400, BIOL/ENEC 562. Some programming experience (e.g., COMP 110, COMP 116, or BIOS 511) helpful, but not required. Society's growing exposure to the financial risks associated with natural hazards (e.g., flood, drought, extreme temperatures) has made it increasingly important to both accurately quantify these risks and develop innovative strategies for managing them. This course provides exposure to the fundamentals of financial risk management with application to natural hazards an emphasis on developing coupled models that consider natural variability, engineered/managed structures and financial/economic factors. Students will learn to (i) model the financial risk posed by extreme events; (ii) understand the merits of various risk management tools; and (iii) develop effective strategies for managing natural hazard-based financial risk. 3 units.
15324	DATA 542 - 001 Insurance: Balancing Risk and Return	TuTh 2:00PM - 3:15PM	Greg Characklis, Harrison Zeff	Greenlaw Hall-Rm 0317	7/8	Seats filled	7/8	0/999
Description: Pre- or corequisite, At least 2 of the following courses in mathematics or statistics (or permission of instructor): MATH 231, MATH 232, STOR 151, STOR 155, BIOS 511, BIOS 512, BIOS 600, ECON 400, BIOL/ENEC 562. Some programming experience (e.g., COMP 110, COMP 116, or BIOS 511) is helpful, but not required. Students will develop a quantitative understanding of concepts underlying actuarial science, including discounted cash flows, net present value and the uncertainties related to liabilities/claims, inflation and interest/discount rates. Asset/premium investment strategies will also be covered, with an introduction to the properties of different asset classes, consideration of uncertainty, and methods by which assets can be assembled into portfolios that balance profitability with the risk. The course will develop students' analytical skills and awareness of the benefits and challenges of quantitative risk analysis, and they will analyze situations in which risk management failed and describe the underlying causes of failure. 1.5 units.
15288	DATA 543 - 001 Risk, Data Science and AI	TuTh 11:00AM - 12:15PM	Youzuo Lin, Harrison Zeff	Gardner Hall-Rm 0001	8/10	Seats filled	8/10	0/999
Description: Pre- or corequisite, At least 2 of the following courses in mathematics or statistics (or permission of instructor): MATH 231, MATH 232, STOR 151, STOR 155, BIOS 511, BIOS 512, BIOS 600, ECON 400, BIOL/ENEC 562. Some programming experience (e.g., COMP 110, COMP 116, or BIOS 511) is helpful, but not required. Students are introduced to advanced techniques in data sciences, machine learning, and artificial intelligence and their application to the management of financial risks. Students will learn to discover, process, and visualize natural hazard and financial data, and will be taught to quantify various financial risks (e.g., natural hazards) and design management strategies to mitigate negative outcomes. Students will learn basic programming methods and apply data analysis and machine learning techniques to model the complex systems that give rise to risk. Structured case studies and in-class assignments will help students build expertise to be used in longer group projects. 3 units.
15319	DATA 710 - 973A Introduction to Applied Data Science	Mo 6:00PM - 7:30PM	Andrea Johnston	TBA	Seats filled	Seats filled	20/20	0/999
Description: The first part of this course introduces various stages of the data life cycle, from defining data requirements to data creation and gathering to data fusion and data preparation to data cleaning and quality control to exploratory analytics, data interpretation, and visualization. We will explore FAIR data principles of curation, metadata, and digital preservation policies. The second part will introduce the concept of relational databases that provide storage and management for structured data. 3 units.
15320	DATA 710 - 973B Introduction to Applied Data Science	We 6:00PM - 7:30PM	Rei Sanchez-Arias	TBA	Seats filled	10/20	10/20	0/999
Description: The first part of this course introduces various stages of the data life cycle, from defining data requirements to data creation and gathering to data fusion and data preparation to data cleaning and quality control to exploratory analytics, data interpretation, and visualization. We will explore FAIR data principles of curation, metadata, and digital preservation policies. The second part will introduce the concept of relational databases that provide storage and management for structured data. 3 units.
15352	DATA 715 - 973A Advanced Databases for Data Science	Tu 6:00PM - 7:30PM	Adam Lee	TBA	Seats filled	Seats filled	20/20	0/999
Description: Prerequisite, DATA 710. This course will explore intermediate-level design and implementation of database systems, emphasizing scalable, distributed systems. It will deepen students' knowledge of advanced relational database management and discuss current and emerging practices for dealing with big data and large-scale database systems. Concepts include design and implementation of relational databases, exploration of distributed data structures including graph, document, and key-value storage models and scalable and resilient query processing. 3 units.
15353	DATA 715 - 973B Advanced Databases for Data Science	We 6:00PM - 7:30PM	Rafael Salas	TBA	Seats filled	Seats filled	21/21	0/999
Description: Prerequisite, DATA 710. This course will explore intermediate-level design and implementation of database systems, emphasizing scalable, distributed systems. It will deepen students' knowledge of advanced relational database management and discuss current and emerging practices for dealing with big data and large-scale database systems. Concepts include design and implementation of relational databases, exploration of distributed data structures including graph, document, and key-value storage models and scalable and resilient query processing. 3 units.
15331	DATA 720 - 973A Programming Methods for Data Science	Tu 6:00PM - 7:30PM	Michael Herron	TBA	Seats filled	17/18	17/18	0/999
Description: This course will provide students with advanced concepts on the construction and use of data structures and their associated algorithms. Concepts covered in this course will include: abstract data types, lists, stacks, queues, trees, and graphs; sorting, searching, hashing, and an introduction to numerical error control; techniques of algorithm analysis and problem-solving paradigms using relevant programming languages and tools. 3 units.
15332	DATA 720 - 973B Programming Methods for Data Science	Th 6:00PM - 7:30PM	Sabya Sachi Gupta	TBA	Seats filled	7/20	7/20	0/999
Description: This course will provide students with advanced concepts on the construction and use of data structures and their associated algorithms. Concepts covered in this course will include: abstract data types, lists, stacks, queues, trees, and graphs; sorting, searching, hashing, and an introduction to numerical error control; techniques of algorithm analysis and problem-solving paradigms using relevant programming languages and tools. 3 units.
15338	DATA 730 - 973A Statistical Modeling and Inference for Data Science	Mo 7:45PM - 9:15PM	Donna Dueker	TBA	Seats filled	17/20	17/20	0/999
Description: The course will be coding-oriented and cover concepts such as foundations in probability, including basic rules, Bayes' theorem, and basic distributions; sampling and the central limit theorem; bootstrapping, confidence intervals, hypothesis testing, and multiple testing; linear models, basic and multiple regression, inference for regression, regularization; classification, logistic regression, and tree-based methods; and prediction, model interpretation, and model evaluation. 3 units.
15339	DATA 730 - 973B Statistical Modeling and Inference for Data Science	We 7:45PM - 9:15PM	Charles Pepe-Ranney	TBA	Seats filled	Seats filled	20/20	0/999
Description: The course will be coding-oriented and cover concepts such as foundations in probability, including basic rules, Bayes' theorem, and basic distributions; sampling and the central limit theorem; bootstrapping, confidence intervals, hypothesis testing, and multiple testing; linear models, basic and multiple regression, inference for regression, regularization; classification, logistic regression, and tree-based methods; and prediction, model interpretation, and model evaluation. 3 units.
15340	DATA 740 - 973A Governance, Bias, and Ethics in Data Science and Artificial Intelligence	Mo 7:45PM - 9:15PM	Grant Glass	TBA	Seats filled	17/20	17/20	0/999
Description: We will explore the foundational concepts of ethics in data science and AI. This overview will set the stage for a deep understanding of what ethical frameworks mean in practice, providing students the opportunity to create actionable examples. By focusing on a wide variety of case studies throughout a myriad of industries and settings, this class will develop leaders who can effectively integrate and leverage data science solutions while ensuring responsible use of data. 3 units.
15346	DATA 750 - 973A Mathematical Tools for Data Science	Tu 6:00PM - 7:30PM	Joseph Slagel	TBA	Seats filled	Seats filled	21/21	0/999
Description: This course will present the mathematical intuition, theory, and techniques driving the numerical computation methods used for processing and analyzing data in various real-life problems. Topics include dimensionality reduction; linear and non-linear approximation; frequency and wavelet analysis; and a glimpse into the mathematics of deep neural networks, classification, large-scale and high-performance numerical computing, and visualization. 3 units.
15347	DATA 760 - 973A Visualization and Communication in Data Science	Th 6:00PM - 7:30PM	Vincent Stuntebeck	TBA	Seats filled	18/20	18/20	0/999
Description: Prerequisite, DATA 710. This course will provide students with a foundational understanding of visual perceptional and data visualization design practices, provide instruction on using visualization for tasks such as exploratory analysis and storytelling to support both data-driven discovery and communication. The class will focus hands-on experiences with commonly used data science tools and technologies. 3 units.
16928	DATA 766 - 001 Leading Research Teams	TuTh 12:30PM - 1:45PM	James Marron	Hanes Hall-Rm 0125	6/8	Seats filled	6/8	0/999
Description: Prerequisites, STOR 664, STOR 665 and STOR 765, or Instructor's Permission. Graduate students will lead groups of four to five undergraduate students to complete a project for an external client. Clients from an array of industries, as well as those from within UNC, will submit project requests that are feasible within a 12-week semester. The course will offer graduate students an authentic learning experience to develop management skills and professional attributes, such as teamwork, communication, and project management, in preparation for workforce entry. 3 units.
15349	DATA 780 - 973A Machine Learning	Mo 6:00PM - 7:30PM	Rei Sanchez-Arias	TBA	Seats filled	Seats filled	22/22	0/999
Description: Prerequisites, DATA 720 and DATA 730. This course will be an introductory course to machine learning (ML). The course will cover core principles of artificial intelligence for statistical inference and pattern analysis. Topics will include probability distributions; graphical models; optimization, maximum likelihood estimation, and regression; classification; cross validation; generalization and overfitting; neural networks; nonparametric estimators; clustering; autoencoders; generative models; and kernel methods. Applications in tabular, image, and textual data for supervised and unsupervised learning tasks also will be covered. 3 units.
15012	DATA 785 - 973A Deep Learning	Mo 6:00PM - 7:30PM	Jonathan Schlosser	TBA	Seats filled	15/20	15/20	0/999
Description: Prerequisites, DATA 720, DATA 730, and DATA 780. Deep learning fundamentals and applications with emphasis on their broad applicability to problems across a range of disciplines. Topics include regularization, optimization, convolutional networks, sequence modeling, generative learning, instance-based learning, and deep reinforcement learning. Students will complete several substantive programming assignments in PyTorch and Keras. 3 units.
12678	DATA 890 - 001 Special Topics in Data Science	TuTh 12:30PM - 1:45PM	To be Announced	TBA	0/15	Seats filled	0/15	0/999
Description: The course goal is to expose graduate students in any UNC department to a broad range of topics in the theory and applications of data science. Students will learn about current and emerging methods and techniques in data science to advance individual research efforts and facilitate inter-disciplinary collaboration. Open to graduate students only and by permission only. 3 units.
12679	DATA 890 - 002 Special Topics in Data Science	TuTh 12:30PM - 1:45PM	To be Announced	Hanes Hall-Rm 0130	0/15	Seats filled	0/15	0/999
Description: The course goal is to expose graduate students in any UNC department to a broad range of topics in the theory and applications of data science. Students will learn about current and emerging methods and techniques in data science to advance individual research efforts and facilitate inter-disciplinary collaboration. Open to graduate students only and by permission only. 3 units.
16562	DATA 890 - 003 Special Topics in Data Science	Mo 9:30AM - 12:00PM	To be Announced	ITS Manning-Rm 1101	0/3	Seats filled	0/3	0/999
Description: The course goal is to expose graduate students in any UNC department to a broad range of topics in the theory and applications of data science. Students will learn about current and emerging methods and techniques in data science to advance individual research efforts and facilitate inter-disciplinary collaboration. Open to graduate students only and by permission only. 3 units.
16645	DATA 890 - 004 Special Topics in Data Science	TuTh 11:00AM - 12:15PM	Weitong Zhang	ITS Manning-Rm 1101	18/25	Seats filled	18/25	0/999
Description: The course goal is to expose graduate students in any UNC department to a broad range of topics in the theory and applications of data science. Students will learn about current and emerging methods and techniques in data science to advance individual research efforts and facilitate inter-disciplinary collaboration. Open to graduate students only and by permission only. 3 units.
15354	DATA 992 - 973A Master's (Non-Thesis)	We 6:00PM - 7:30PM	Richard Marks	TBA	Seats filled	3/20	3/20	0/999
Description: Prerequisite, DATA 780, DATA 760, DATA 740, DATA 750, DATA 715, DATA 710. Team based project in final term of program 3 units.