DATA Courses

Here is information about DATA class enrollment for fall 2024. 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 last updated: 2024-09-04 10:56:29.690695

Class Number	Class	Meeting Time	Instructor	Room	Unreserved Enrollment	Reserved Enrollment	Total Enrollment	Wait List
14493	DATA 110 - 001 Introduction to Data Science	MoWe 3:35PM - 4:25PM	Harlin Lee, Amartya Banerjee, Debabrata Mandal	Gardner Hall-Rm 0105	74/75	Seats filled	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.
14518	DATA 110 - 002 Introduction to Data Science	MoWe 9:05AM - 9:55AM	Richard Marks, Amartya Banerjee, Debabrata Mandal	Chapman Hall-Rm 0211	Seats filled	Seats filled	100/100	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.
14494	DATA 110 - 601 Introduction to Data Science	Fr 3:35PM - 4:25PM	Harlin Lee, Amartya Banerjee, Debabrata Mandal	Murphey Hall-Rm 0204	Seats filled	Seats filled	30/30	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.
14495	DATA 110 - 602 Introduction to Data Science	Fr 3:35PM - 4:25PM	Harlin Lee	Murphey Hall-Rm 0115	Seats filled	Seats filled	31/31	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.
14796	DATA 110 - 603 Introduction to Data Science	Fr 3:35PM - 4:25PM	Harlin Lee	Murphey Hall-Rm 0105	38/39	Seats filled	38/39	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.
14520	DATA 110 - 604 Introduction to Data Science	Fr 9:05AM - 9:55AM	Richard Marks	Gardner Hall-Rm 0308	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.
14519	DATA 110 - 605 Introduction to Data Science	Fr 9:05AM - 9:55AM	Richard Marks	Gardner Hall-Rm 0210	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.
14789	DATA 110 - 606 Introduction to Data Science	Fr 9:05AM - 9:55AM	Richard Marks	Phillips Hall-Rm 0206	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.
14766	DATA 120 - 001 Ethics of Data Science and Artificial Intelligence	MoWeFr 12:20PM - 1:10PM	Neil Gaikwad, Levi Harris, Malavika Mampally, Yifei Zhang	Carroll Hall-Rm 0111	183/190	Seats filled	203/210	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.
14496	DATA 130 - 001 Critical Data Literacy	MoWeFr 9:05AM - 9:55AM	Alex McAvoy, Chuxiangbo Wang	Phillips Hall-Rm 0247	38/41	Seats filled	43/46	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.
14499	DATA 140 - 001 Introduction to Data Structures and Management	TuTh 12:30PM - 1:45PM	Youzuo Lin, Titus Spielvogel, Scott Merrill	Greenlaw Hall-Rm 0101	Seats filled	Seats filled	100/100	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.
14500	DATA 150 - 001 Communication for Data Scientists	TuTh 11:00AM - 12:15PM	Anita Crescenzi, Jacob Horner	Dey Hall-Rm 0305	Seats filled	Seats filled	50/50	0/999
Description: The ability to collect and analyze data has changed virtually every field, yet data scientists often lack the ability to present their findings in effective formats. This class uses storytelling to help you connect with your audience and present your data in compelling and understandable ways so stakeholders can make the right decisions with data. Through hands-on exercises, you'll learn the advantages and disadvantages of oral, visual, and written formats. 3 units.
17570	DATA 710 - 970 Introduction to Applied Data Science	Mo 6:00PM - 7:30PM	Fola Omofoye	TBA	Seats filled	Seats filled	20/20
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.
17580	DATA 710 - 973 Introduction to Applied Data Science	Tu 6:00PM - 7:30PM	Andrea Johnston	TBA	16/20	Seats filled	16/20
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.
17581	DATA 710 - 974 Introduction to Applied Data Science	We 6:00PM - 7:30PM	To be Announced	TBA	13/20	Seats filled	13/20
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.
17583	DATA 715 - 970 Advanced Databases for Data Science	Tu 6:00PM - 7:30PM	Adam Lee	TBA	9/20	Seats filled	9/20
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.
17574	DATA 715 - 973 Advanced Databases for Data Science	We 7:45PM - 9:15PM	Rafael Salas	TBA	Seats filled	11/20	11/20
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.
17572	DATA 720 - 970 Programming Methods for Data Science	Mo 6:00PM - 7:30PM	Sabya Sachi Gupta	TBA	17/20	Seats filled	17/20
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.
17582	DATA 720 - 973 Programming Methods for Data Science	Tu 6:00PM - 7:30PM	Michael Herron	TBA	Seats filled	Seats filled	20/20
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.
17586	DATA 730 - 970 Statistical Modeling and Inference for Data Science	We 7:45PM - 9:15PM	Charles Pepe-Ranney	TBA	17/20	Seats filled	17/20
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.
17573	DATA 730 - 973 Statistical Modeling and Inference for Data Science	Mo 7:45PM - 9:15PM	Donna Dueker	TBA	16/20	Seats filled	16/20
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.
17589	DATA 740 - 970 Governance, Bias, and Ethics in Data Science and Artificial Intelligence	We 7:45PM - 9:15PM	Grant Glass	TBA	7/20	Seats filled	7/20
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.
17584	DATA 750 - 970 Mathematical Tools for Data Science	We 6:00PM - 7:30PM	To be Announced	TBA	16/20	Seats filled	16/20
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.
17577	DATA 760 - 970 Visualization and Communication in Data Science	Th 7:45PM - 9:15PM	Vincent Stuntebeck	TBA	6/20	Seats filled	6/20
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.
17575	DATA 780 - 970 Machine Learning	Mo 6:00PM - 7:30PM	Jonathan Schlosser	TBA	10/20	Seats filled	10/20
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.
17576	DATA 780 - 973 Machine Learning	Tu 6:00PM - 7:30PM	Rei Sanchez-Arias	TBA	8/20	Seats filled	8/20
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.
14867	DATA 890 - 003 Special Topics in Data Science	TuTh 9:30AM - 10:45AM	Can Chen	ITS Manning-Rm 5106	15/16	Seats filled	15/16	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.