DS 1300. Knowledge in a Digital World. (4 Hours)
Examines the impact that information technologies (such as the internet, search engines, blogs, wikis, and smartphones); information processing techniques (such as big data analysis, machine learning, crowdsourcing, and cryptography); and information policies (such as privacy norms and speech restrictions) have on what we know and how much we know, as individuals and as a society. The digital world can enhance our ability to acquire knowledge by providing us with fast and cheap access to huge amounts of information. However, it can also undermine our cognitive abilities and provide us with inaccurate or misleading information. Studies normative frameworks from epistemology and ethics (such as epistemic value theory, the extended mind hypothesis, and moral rights) to evaluate these technologies and policies.
Attribute(s): NUpath Ethical Reasoning, NUpath Societies/Institutions
DS 1990. Elective. (1-4 Hours)
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
DS 2990. Elective. (1-4 Hours)
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
DS 2991. Research in Data Science. (1-4 Hours)
Offers an opportunity to conduct introductory-level research or creative endeavors under faculty supervision. May be repeated three times.
DS 3000. Mathematical Foundations of Artificial Intelligence. (4 Hours)
Introduces methods and concepts from linear algebra and probability that form a basis for modern artificial intelligence. Emphasizes mathematical foundations with written work and computational aspects using programming assignments. Students work with tensors and may be tasked with implementing from scratch algorithms central to numerical linear algebra, introductory machine learning, and artificial intelligence.
Prerequisite(s): CS 2100 with a minimum grade of D- or CIS 201M with a minimum grade of D- or CS 2510 with a minimum grade of D- or DS 2500 with a minimum grade of D-
Attribute(s): NUpath Analyzing/Using Data, NUpath Natural/Designed World
DS 3050. Information and Uncertainty. (4 Hours)
Introduces the foundations of probabilistic inference, information theory, and their uses for drawing conclusions from noisy data. Applications include diagnosing diseases with inconclusive medical tests, locating autonomous vehicles when sensors are imperfect, and how best to make inferences with incomplete or partial information. Central topics include distinguishing deductive and probabilistic inference, philosophical interpretations of probability, fundamental justifications for the rules of probability, and key concepts of information theory. Introduces analytic and mathematical methods of analysis in these cases and contemporary computational (i.e., programming) techniques for implementing and applying theories of information and probabilistic inference.
Attribute(s): NUpath Analyzing/Using Data, NUpath Formal/Quant Reasoning
DS 3500. Advanced Programming with Data. (4 Hours)
Presents a deep dive into the design and implementation of enterprise-grade software systems with an emphasis on software architectures for more complex data-driven applications. Covers extensible architectures that support testing, data provenance, reuse, maintainability, scalability, and robustness. Uses software APIs and libraries for wide-scale adoption and ease of use. Offers students an opportunity to design, implement, and test complex, data-centric pipelines using large, real-world datasets. Explores the features, capabilities, and underlying design of popular data analysis and visualization frameworks.
Prerequisite(s): CS 2100 with a minimum grade of D- or DS 2500 with a minimum grade of D-
DS 3990. Elective. (1-4 Hours)
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
DS 4200. Information Presentation and Visualization. (4 Hours)
Introduces foundational principles, methods, and techniques of visualization to enable creation of effective information representations suitable for exploration and discovery. Covers the design and evaluation process of visualization creation, visual representations of data, relevant principles of human vision and perception, and basic interactivity principles. Studies data types and a wide range of visual data encodings and representations. Draws examples from physics, biology, health science, social science, geography, business, and economics. Emphasizes good programming practices for both static and interactive visualizations. Creates visualizations in Excel and Tableau as well as R, Python, and open web-based authoring libraries. Requires programming in Python, JavaScript, HTML, and CSS. Requires extensive writing including documentation, explanations, and discussions of the findings from the data analyses and the visualizations.
Prerequisite(s): CS 2100 with a minimum grade of D- or CIS 201M with a minimum grade of D- or CS 2510 with a minimum grade of D- or DS 2500 with a minimum grade of D-
Attribute(s): NUpath Analyzing/Using Data, NUpath Writing Intensive
DS 4300. Large-Scale Information Storage and Retrieval. (4 Hours)
Introduces data and information storage approaches for structured and unstructured data. Covers how to build large-scale information storage structures using distributed storage facilities. Explores data quality assurance, storage reliability, and challenges of working with very large data volumes. Studies how to model multidimensional data. Implements distributed databases. Considers multitier storage design, storage area networks, and distributed data stores. Applies algorithms, including graph traversal, hashing, and sorting, to complex data storage systems. Considers complexity theory and hardness of large-scale data storage and retrieval. Requires use of nonrelational, document, key-column, key-value, and graph databases and programming in R, Python, and C++.
Prerequisite(s): (CS 3100 with a minimum grade of D- or CIS 310M with a minimum grade of D- or CS 3500 with a minimum grade of D- or DS 3500 with a minimum grade of D- ); CS 3200 with a minimum grade of D-
Attribute(s): NUpath Analyzing/Using Data
DS 4400. Machine Learning. (4 Hours)
Introduces supervised and unsupervised predictions, modeling, and essential machine learning concepts. Uses tools and libraries to analyze datasets, build predictive models, and evaluate the fit of the models. Covers common learning algorithms such as regression; classification (including support vector machines and logistic regression); dimensionality reduction (including principal-component analysis); clustering, gradient descent, regularization techniques, multiclass data, and algorithms; boosting; and decision trees. Studies computational aspects of probability, statistics, and linear algebra that support algorithms, including sampling theory and computational learning. Applies concepts to common problem domains.
Prerequisite(s): DS 3000 with a minimum grade of D- or CIS 250M with a minimum grade of D- or (MATH 2331 with a minimum grade of D- ; MATH 3081 with a minimum grade of D- )
Attribute(s): NUpath Analyzing/Using Data, NUpath Capstone Experience, NUpath Writing Intensive
DS 4420. Advanced Machine Learning. (4 Hours)
Covers advanced supervised and unsupervised machine learning concepts. Focuses on mathematical and computational foundations of learning algorithms. Topics may include kernel methods and models appropriate for structured data, including time-series and other sequences. Covers parameter estimation techniques (including Bayesian learning); generative models; and sampling strategies like Markov Chain Monte Carlo methods. May include mathematical proofs and empirical analysis as methods to assess the validity and performance of algorithms. Applies concepts to common problem domains.
Prerequisite(s): DS 4400 with a minimum grade of D-
Attribute(s): NUpath Analyzing/Using Data, NUpath Capstone Experience, NUpath Writing Intensive
DS 4440. Modern Neural Networks. (4 Hours)
Presents a hands-on introduction to modern neural network (deep learning) methods and tools. Covers fundamentals of neural networks, including stochastic gradient descent and backpropagation. Introduces standard and new architectures from simple feedforward networks to generative recurrent and state-of-the-art transformer architectures. Emphasizes using these technologies in practice, via modern toolkits. Reviews applications of these models to various types of data, including images and text.
Prerequisite(s): DS 4400 with a minimum grade of D-
Attribute(s): NUpath Analyzing/Using Data
DS 4535. Professional Practicum Capstone. (4 Hours)
Offers students an opportunity to expand skills in real-world machine learning, artificial intelligence, and data science applications and team collaboration within an experiential learning structure. Involves students in industry-specific projects designed to integrate into an industry partner’s intellectual property portfolio. Projects focus on machine learning and artificial intelligence and also include written components that align with the partner’s standards and reference relevant prior work. Students engage in a structured process of milestones and feedback cycles with peers; instructors; and, as appropriate, industry partners to refine project outcomes.
Attribute(s): NUpath Capstone Experience, NUpath Integration Experience, NUpath Writing Intensive
DS 4970. Junior/Senior Honors Project 1. (4 Hours)
Focuses on in-depth project in which a student conducts research or produces a product related to the student’s major field. Combined with Junior/Senior Project 2 or college-defined equivalent for 8 credit honors in the discipline project.
DS 4971. Junior/Senior Honors Project 2. (4 Hours)
Focuses on second semester of in-depth project in which a student conducts research or produces a product related to the student’s major field.
Prerequisite(s): DS 4970 with a minimum grade of D-
DS 4973. Topics in Data Science. (4 Hours)
Offers a lecture course in data science on a topic not regularly taught in a formal course. Topics may vary from offering to offering. May be repeated up to four times.
Prerequisite(s): CS 3000 with a minimum grade of D- ; (CS 3100 with a minimum grade of D- or CIS 310M with a minimum grade of D- or CS 3500 with a minimum grade of D- or DS 3500 with a minimum grade of D- )
DS 4990. Elective. (1-4 Hours)
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
DS 4991. Research. (4 Hours)
Offers an opportunity to conduct research under faculty supervision.
Attribute(s): NUpath Integration Experience
DS 4992. Directed Study. (1-4 Hours)
Offers independent work under the direction of members of the department on a chosen topic. May be repeated without limit.
DS 4996. Experiential Education Directed Study. (1-4 Hours)
Draws upon the student’s approved experiential activity and integrates it with study in the academic major. Restricted to those students who are using it to fulfill their experiential education requirement. May be repeated without limit.
Attribute(s): NUpath Integration Experience
DS 4997. Data Science Thesis. (4 Hours)
Offers students an opportunity to prepare an undergraduate thesis under faculty supervision.
DS 4998. Data Science Thesis Continuation. (4 Hours)
Focuses on student continuing to prepare an undergraduate thesis under faculty supervision.
DS 5010. Introduction to Programming for Data Science. (4 Hours)
Offers an introductory course on fundamentals of programming and data structures. Covers lists, arrays, trees, hash tables, etc.; program design, programming practices, testing, debugging, maintainability, data collection techniques, and data cleaning and preprocessing. Includes a class project, where students use the concepts covered to collect data from the web, clean and preprocess the data, and make it ready for analysis.
DS 5020. Introduction to Linear Algebra and Probability for Data Science. (4 Hours)
Offers an introductory course on the basics of statistics, probability, and linear algebra. Covers random variables, frequency distributions, measures of central tendency, measures of dispersion, moments of a distribution, discrete and continuous probability distributions, chain rule, Bayes’ rule, correlation theory, basic sampling, matrix operations, trace of a matrix, norms, linear independence and ranks, inverse of a matrix, orthogonal matrices, range and null-space of a matrix, the determinant of a matrix, positive semidefinite matrices, eigenvalues, and eigenvectors.
DS 5110. Essentials of Data Science. (4 Hours)
Introduces students to the core tasks in data science, including data collection, storage, tidying, transformation, processing, management, and modeling for the purpose of extracting knowledge from raw observations. Programming is a cross-cutting aspect of the course. Offers students an opportunity to gain experience with data science tasks and tools through short assignments. Includes a term project based on real-world data.
DS 5220. Supervised Machine Learning and Learning Theory. (4 Hours)
Introduces supervised machine learning, which is the study and design of algorithms that enable computers/machines to learn from experience or data, given examples of data with a known outcome of interest. Offers a broad view of models and algorithms for supervised decision making. Discusses the methodological foundations behind the models and the algorithms, as well as issues of practical implementation and use, and techniques for assessing the performance. Includes a term project involving programming and/or work with real-world data sets. Requires proficiency in a programming language such as Python, R, or MATLAB.
Attribute(s): NUpath Capstone Experience, NUpath Writing Intensive
DS 5230. Unsupervised Machine Learning and Data Mining. (4 Hours)
Introduces unsupervised machine learning and data mining, which is the process of discovering and summarizing patterns from large amounts of data, without examples of data with a known outcome of interest. Offers a broad view of models and algorithms for unsupervised data exploration. Discusses the methodological foundations behind the models and the algorithms, as well as issues of practical implementation and use, and techniques for assessing the performance. Includes a term project involving programming and/or work with real-life data sets. Requires proficiency in a programming language such as Python, R, or MATLAB.
Attribute(s): NUpath Capstone Experience, NUpath Writing Intensive
DS 5500. Data Science Capstone. (4 Hours)
Offers students a capstone opportunity to practice data science skills learned in previous courses and to build a portfolio. Students practice visualization, data wrangling, and machine learning skills by applying them to semester-long term projects on real-world data. Students may either propose their own projects or choose from a selection of industry options. Emphasizes the overall data science process, including identification of the scientific problem, selection of appropriate machine learning methods, and visualization and communication of results. Lectures may include additional topics, including visualization, communication, and data science ethics.
Prerequisite(s): (CS 5800 with a minimum grade of C- or EECE 7205 with a minimum grade of C- ); (CS 6140 with a minimum grade of C- or DS 5220 with a minimum grade of C- or EECE 5644 with a minimum grade of C- ); (CS 6220 with a minimum grade of C- or DS 5230 with a minimum grade of C- or IE 7275 with a minimum grade of C- ); DS 5110 with a minimum grade of C-
DS 5983. Topics in Data Science. (4 Hours)
Offers special topics in data science based on the interest and expertise of the faculty member conducting the course. May be repeated once.
DS 6962. Elective. (1-4 Hours)
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
DS 7990. Thesis. (4 Hours)
Offers selected work with the agreement of a project supervisor. May be repeated once.
DS 7995. Project. (1-4 Hours)
Offers students an opportunity to participate in a direct data science project under the supervision of a faculty member. May be repeated once for a total of 8 credits.
DS 8982. Readings. (1-8 Hours)
Offers selected readings under the supervision of a faculty member. May be repeated without limit.