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Building upon prior data acquisition and analysis coursework, students will effectively and flexibly generate advanced statistical models in a digital advertising-specific context. This course will focus on data originating from a variety of digital advertising sources. In addition to technical skill acquisition, students will learn how to interpret results and present them to clients and management.

Department consent required.

Exposes students to current research topics in the field of robotics and provides hands-on experience in solving a grand challenge program.

Surveys computational and mathematical modeling to illuminate biological processes. Students work together to learn to build and analyze models using a variety of numerical tools, tackle meaningful biological problems, and communicate effectively across disciplines. Specific topics: Langevin dynamics of protein folding, agent-based models, finite difference models of organismal growth, stochastic and deterministic cellular automata game of life, models of behavior.

Requisites: Restricted to graduate students only.

Recommended: Prerequisite comfort with mathematics and/or programming experience, and more advanced understanding (upper undergraduate level) of any relevant discipline.

Creating autonomous systems that interact with humans requires the synthesis of insights from a variety of disciplines. This course aims to provide students with the algorithms, models, and frameworks that form the building blocks required for developing intelligent autonomous systems that perform useful tasks while interacting with, coordinating with, co-existing with, or otherwise assisting humans. Previously offered as a special topics course.

Examines modern techniques for analyzing and modeling the structure and dynamics of complex networks. Focuses on statistical algorithms and methods, and emphasizes model interpretability and understanding the processes that generate real data. Applications are drawn from computational biology and computational social science. No biological or social science training is required.

Introduces students to the field of human-robot interaction (HRI). Covers HRI theory, principles, methodologies, and applications with links to robotics, artificial intelligence, human factors, human-computer interaction, design, cognitive psychology, education and other domains. Coursework includes readings from state-of-the-art in HRI research, team exercises and problem-solving sessions, and implementation and evaluation of a human-robot interaction systems for specific applications. Same as ATLS 5402.

Introduces core concepts in cybersecurity including confidentiality, integrity, authentication, risk management, and adversarial thinking. The concepts will be applied to both traditional information technology (IT) systems and cyber physical systems (CPS). At the conclusion of the course, students should have a solid foundation in cybersecurity and hands-on experience. Degree credit not granted for this course and CYBR 5300.

Explores the principles and emergent properties of collective dynamics through computational modeling and theory. Focuses on multi-agent systems using insights from biology, like the self-assemblage of cells and insect colony behavior. Topics include designing swarm intelligence, networked agents, cellular computing and self-assembly, optimization, synchronization, and evolutionary computation. Uses cross-discipline research developments to practice applied techniques. Biology background is not required.

Recommended: Prerequisite CSCI 2270 and basic knowledge of programming.

Introduces computing systems, software, and methods used to solve large-scale problems in science and engineering. Students use high-performance workstations and a supercomputer. This is the first course in a two-semester sequence.

Explores algorithms that can extract information about the world from images or sequences of images. Topics covered include: imaging models and camera calibration, early vision (filters, edges, texture, stereo, optical flow), mid-level vision (segmentation, tracking), vision-based control and object recognition.

Recommended Prerequisite: Probability, multivariate calculus, and linear algebra

Introduces a set of modeling techniques that have become a mainstay of modern artificial intelligence, cognitive science and machine learning research. These models provide essential tools for interpreting the statistical structure of large data sets and for explaining how intelligent agents analyze the vast amount of experience that accumulates through interactions with an unfamiliar environment.

Recommended prerequisite: undergraduate course in probability and statistics.

Explores the field of natural language processing as it is concerned with the theoretical and practical issues that arise in getting computers to perform useful and interesting tasks with natural language. Covers the problems of understanding complex language phenomena and building practical programs.

Introduces students to techniques for applying machine learning in the development of customizable human-computer interfaces. Students will learn to process a wide variety of input data (e.g. video and accelerometer streams), using different machine learning algorithms to detect semantically meaningful events that can afford the construction of new interactive systems. They will complete substantial projections within the domains of assistive or creative technologies. Does not fulfill Breadth Requirement for CSEN graduate students. Same as CSCI 4889, ATLS 4889 and ATLS 5880.

Requires prereqs: (CSCI 3022 or APPM 4570 or APPM 3570 or APPM 4520 or CVEN 3227 or MATH 3510 or MATH 4510 or ECEN 3810 or ECON 3818 or MCEN 4120) & (CSCI 3002 or CSCI 3202 or CSCI 4448) all min grade C-.

Restricted to grad students in the ATLAS program.

Introduces modern approaches to machine learning using neural networks. Neural nets, popular in the early 1990s, have undergone a resurgence due to significant advances in computing power and the availability of very large data sets. Now rechristened 'deep learning,'听the field has produced state-of-the-art results in a range of artificial intelligence problems, including vision, speech, and natural language processing.

This course studies state-of-the-art practice and research on efficient and effective systems and algorithms design for managing and exploring massive amounts of digital data in various application domains. The course takes an integrated approach that studies all three aspects of big data analytics: systems, algorithms, and applications. Specifically, this course covers big data systems for MapReduce, NoSQL, stream processing, deep learning, mobile/wearable/IoT sensing, as well as the practical use of indexing, sketching, recommendation, graph, and deep learning algorithms. Domain-specific data management and analysis, such as those in online social networks, scientific discovery, business intelligence, health informatics, urban computing, are also covered.

Covers research topics of current interest in computer science that do not fall into a standard subarea.

Repeatable: Repeatable for up to 8.00 total credit hours. Allows multiple enrollment in term.

Requisites: Restricted to graduate students only

Students conduct agreed-upon research, critical review, business proposal, or project and present their work to the capstone committee for evaluation.

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Available only through approval of graduate advisor and faculty. Subjects arranged to fit the needs of the particular student. May be repeated for up to 6 total credits.

Examines a special topic in Data Science. May be repeated up to 9 total credit hours.

This class provides a structure for DS graduate students to receive academic credit for internships with industry partners that have an academic component to them suitable for graduate-level work. Participation in the program will consist of an internship agreement between a student and an industry partner who will employ the student in a role that supports the academic goals of the internship. Instructor participation will include facilitation of mid-term and final assessments of student performance as well as support for any academic-related issues that may arise during the internship period.

Explores advanced topics in geospatial databases, spatial analytics and geoprocessing in a Geographic Information System (GIS). Emphasizes how geographic concepts are linked to methodological frameworks for recording, transforming, storing/retrieving, analyzing, and processing geographic data as well as various forms of uncertainty. Exercises demonstrate the application of GIS-based methods to real world scenarios in interdisciplinary settings. Same as .

Requisites: Restricted to Grad students only.

Focuses on the use and development of advanced models for human and environmental applications in a geospatial environment integrating raster and vector data models. Covers terrain and hydrologic modeling, geostatistical modeling, dasymetric modeling, as well as multi-criteria modeling. Group projects critically design, implement and test spatial models to develop independent skill sets in a chosen problem setting. Same as .

Requisites: Restricted to Grad students only.

Recommended Prerequisites and Corequisites: or or working knowledge of GIS software or instructor consent required.

Focuses on the extension of geographic information systems (GIS) through programming as well as on the development of algorithms for spatial analysis and information extraction in vector and raster data using open source tools. Covers concepts, principles and techniques of programming and solving spatial problems in natural and social science settings. Group projects will foster skillsets in implementing solutions to complex spatial problems.

Recommended prerequisite: . Same as .

Introduce students to major unanswered questions in Earth science and to the analytical tools, including data management, analysis and visualization, necessary to explore 'big data' from a suite of sensors. Aligns with Earth Lab, a new initiative of the to use our expertise in space-based observation to address our world's most pressing problems.

Introduces students to foundational computing and statistical concepts for analyzing humanities data. This course discusses the influence of digitization and data on humanist inquiry and exposes students to techniques for working with data in different areas of the humanities, including literature, history, and art. The course emphasizes technical practices involved in humanist data analysis. Comfort with programming is strongly encouraged.

Explores the ethical and legal complexities of information and communication technology. By combining real-world inquiry with creative speculation, students will probe everyday ethical dilemmas they face as digital consumers, creators and coders, as well as relevant policy. Explores themes such as privacy, intellectual property, social justice, free speech, artificial intelligence, social media, and ethical lessons from science fiction.

Explores the space of personalized information access applications known as recommender systems. This class will introduce students to a range of approaches for building recommender systems including collaborative, content-based, knowledge-based, and hybrid methods. Students will also explore a variety of applications for recommendation including consumer products, music, social media, and online advertising. The course will also examine controversies surrounding recommendation, including Pariser鈥檚 鈥渇ilter bubble鈥�, and questions of algorithmic bias. Proficiency in Python programming required.听

Introduces theories and methods for analyzing relational data in social, information, and other complex networks. Students will understand the processes and theories explaining network structure and dynamics as well as develop skills analyzing and visualizing real-world network data. No math or statistics training required, but the course will assume familiarity with Python.听

Explore public websites, databases, and bioinformatic tools that can be used for analysis of genomic data. These include NCBI Resources, genome databases, gene expression databases, tools for nucleotide and algorithms analyses and protein databases. Students develop a mini-grant proposal that is required to incorporate use of some of the tools covered.

Recommended Prerequisites: and

Introduces advanced statistical techniques important for analyzing data rising in biomedical research, including physiology. StatistIcal reasoning will be emphasized through problem solving and applications using statistical software packages.

Requisites: Restricted to Integrative Physiology (IPHY) or Integrative Physiology Concurrent Degree (C-IPHY) graduate students only.

Recommended: Prerequisite .

Application and use appropriate study designs and methods to address research questions/hypotheses in transdisciplinary integrative physiology, circadian, and sleep research. Highlights gold standard and cutting-edge research techniques, and issues of scientific rigor and reproducibility.

Gives an introduction, with proofs, to the algebra and number theory used in coding and cryptography. Basic problems of coding and cryptography are discussed; prepares students for the more advanced ECEN 5682. Same as MATH 4440.

Covers the concepts and tools to design and manage business processes. Emphasizes modeling and analysis, information technology support for process activities, and management of process flows. Graphical simulation software is used to create dynamic models of business processes and predict the effect of changes. Prepares students for strong management or consulting career path in business processes.

Maximum enrollment of 5 Data Science students per semester.

Focuses on formulating decision problems as mathematical models and employing computational tools to solve them. Microsoft Excel is used as the main modeling platform but the course will also cover advanced tools, such as modeling languages. Optimization modeling will be illustrated in problems associated with operations, marketing, management, and finance. Integrates topics from decision analysis and operations management as they relate to modeling management decisions.

Maximum enrollment of 5 Data Science students per semester.

Explores both the functional and technical environment for the creation, storage, and use of the most prevalent source and type of data for business analysis, ERP, and related structured data. Students will learn how to access and leverage information via SQL for analysis, aggregation to visualization, create dashboards, and be source for business intelligence.听

Maximum enrollment of 5 Data Science students per semester.

Provides an introduction to methods in the field of statistical learning. Topics include a review of multiple regression, assessing model accuracy, classification, resampling methods, model selection and regularization, nonlinear regression, tree-based methods, support vector machines and unsupervised learning. Involves hands-on data analysis using the R programming language.

Requisites: with a grade of C- or higher AND (MS-DS major OR Department Consent)

Educates and trains students to become effective interdisciplinary collaborators by developing the communication and collaboration skills necessary to apply technical statistics and data science skills to help domain experts answer research questions. Topics include structuring effective meetings and projects; communicating statistics to non-statisticians; using peer feedback, self-reflection and video analysis to improve collaboration skills; creating reproducible statistical workflows; working ethically.
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Requisites: Requires a prerequisite course of or (minimum grade C-). Restricted to graduate students only.

Maximum enrollment of 5 Data Science students per semester.

Other departments across campus also offer courses covering data science topics and applications.

If you are interested in taking a course for elective MS-DS degree credit and it is not currently included in the list above, email the听MS-DS Graduate Advisor at听datascience@colorado.edu听with the following information, showing the course has a significant data science component:

• Course name
• Course number
• Course description
• Course syllabus (if available)
• Number of credits

Requests will be reviewed on a case-by-case basis, and approval is not guaranteed. All courses taken towards the degree must be at the 5000 level or higher and meet the grade standards indicated in the handbook.