ACCT602 – Big Data Technologies
Course Description: Exposes students to the different phases of big data projects: discovery, collection, enrichment, analysis, and decision making. Students learn how to implement Big Data solutions.
Offered: Spring 2023, Fall 2023
ACCT604 – Database Design and Implementation
Course Description: Introduces the fundamentals of modern database concepts. Covers relational database design and implementation techniques, as well as the current relational database standard – the structured query language (SQL). Explore advanced topics such as big data analytics.
Offered: Fall 2023
BINF601 – Introduction to Data Sciences
Course Description: This course introduces topics and fundamental skills needed for application of bioinformatics and data science across life science disciplines using biomedically-relevant examples. Topics will include fundamental computational skills, data preparation, FAIR data practices, basic concepts and applications of Omics and AI/ML in life sciences, and effective communication of results.
Offered: Spring 2024
BINF610 – Applied Machine Learning
Course Description: This course introduces students to basic concepts of machine learning principles and paradigms, and equips them with knowledge of machine learning pragmatics that are involved in a complete learning process from formulating a problem to choosing the appropriate techniques/tools to evaluating the results.
Instructor: Li Liao Offered: Fall 2019, Spring 2022, Spring 2023, Spring 2024
BINF640 – Databases for Bioinformatics
Course Description: Introduces the basic concepts, components, and principles underlying the design, implementation, and use of database management systems. Coupling lectures and a modular, half-semester-long term project, students will develop basic data modeling techniques, database management concepts, and SQL skills to develop a database supporting real-world applications.
Offered: Spring 2024
BINF644 – Bioinformatics
Course Description: This course will introduce the principles of bioinformatics analysis of genes and proteins and provide a practical introduction to a variety of bioinformatics resources and tools. Course consists of lectures, tutorials, hands-on exercises, quizzes, and a term project.
Offered: Spring 2024
BINF667 – Seminar: Big Data in Social, Behavioral, and Health Sciences
Course Description: Empowered by the advances in information technology, Big Data such as social media and
electronic health records, present unprecedented opportunities for social, behavioral, and health
sciences. This emerging field has generated innovative ways of collecting data, developed
cutting edge analytical and statistical techniques, and provided novel approaches of visualizing
and presenting information. The objective of the course is to familiarize students with big data
analysis as a tool for addressing substantive research questions in social, behavioral, and
health sciences.
Instructor: Fang Fang Chen Offered: Spring 2021, Spring 2022, Spring 2023
📎 Syllabus
BINF667-018 – Electronic Health Records (EHR) Data Science
Course Description: Electronic Health Records have become a critical source of information for healthcare providers and researchers, offering vast potential for improving patient care, healthcare management, and medical research. This course is designed to empower students with the knowledge and skills to harness the potential of Electronic Health Records (EHR) data for data science applications within the healthcare domain. Throughout this course, students will acquire essential competencies in ERH data extraction, preprocessing, visualization, exploration, and modeling essentials.
Offered: Spring 2024
BINF685/CISC685 – Modeling and simulation for bioinformatics systems
Course Description: Concepts, techniques, and tools for modeling and simulation of biological systems. Topics include gene regulation, signal transduction, and metabolism, Bayesian networks, Monte Carlo and Gibbs sampling, and optimization
Prerequisites: CISC636 or permission of instructor
Instructor: Li Liao Offered: Fall 2019
BMEG802 – Advanced Biomedical Experiment Design & Analysis
Course Description: Understanding statistical analyses is an essential skill for scientists in academia and industry. Here we will discuss the philosophy of hypothesis testing, simulate datasets via sampling, and perform parametric and nonparametric tests. In additional to traditional tests (mean comparison, regression, ANOVA) we will also introduce some advanced techniques (maximum likelihood, Bayes, bootstrapping, and MCMC).
Instructor: Josh Cashaback Offered: Fall 2021, Fall 2022
📎 Syllabus
BUAD621 – Decision Analytics and Visualization
Course Description: Analytics leverages both the proliferation of data and the advancement of computational tools to bring a new level of sophistication to business decision making. As part of developing an analytic mind and skillset, this course teaches students to properly frame decision problems, represent and understand how to manage uncertainty inherent in those problems, manipulate large data sets using modern software to prescribe recommended actions, and to then compel organizational change through data visualizations.
The free online book is available at http://causact.com and the printed book at http://amazon.com/dp/B08DBYPRD2
Prerequisites: BUAD820
Restrictions: This class is restricted to students in the Online MBA Program. This is an Online course.
Instructor: Adam Fleischhacker Offered: Fall 2021, Fall 2022
CIEG642 – Advanced Data Analysis
Course Description: The course presents a comprehensive introduction to the principles and practices of emerging advanced data analysis with particular focus to engineering science. The course will attempt to provide insight to advanced statistical techniques and methods of analyzing BIG DATA will be the main focus.
Prerequisites: CIEG315 – Probability and Statistics for Engineers
Instructor: Nii Attoh-Okine Offered: Spring 2021, Spring 2022
📎 Syllabus
CISC367 – Introduction to Data Science
Course Description: Data Science is an increasingly popular, profitable, and critical discipline. In this course, students will learn essential Data Science fundamentals of data manipulation, analytics, visualization, interpretation, and presentation. Taught in a problem-oriented style, with minimal lecture, the course emphasizes practical techniques and programming pragmatics. Contexts will vary widely across the semester, without a single overall theme, and will occasionally give students a chance to explore their own interests in different application areas.
Restrictions: Enrollment is by instructor consent only. Preference is given for honors students, sophomore/juniors not taking many other upper-level CISC courses, students who have completed CISC220, and students who have previously demonstrated a strong work ethic. Interested students should apply through the following google form: https://forms.gle/CU2Jb7aBw2s1aAcQ8
Offered: Spring 2021
📎 Syllabus
CISC436/636 – Computational Biology and Bioinformatics
Course Description: Concepts, methodologies, and tools in bioinformatics. Abstraction of biological problems for computational solutions. Genome sequencing and assembly, bio-sequence analysis and comparison and database search, dynamics programming, hidden Markov models, and phylogenetic trees.
Prerequisites: CISC220 or permission of instructor.
Instructor: Li Liao Offered: Fall 2021, Spring 2022, Spring 2023
CISC467/667-010 – Cloud Computing
Course Description: This course addresses a broad range of topics in cloud computing and distributed systems, including: cloud systems, Internet of Things (and peer-to-peer systems), distributed file systems (and key-value stores), MapReduce, Spark, NoSQL, synchronization, scheduling, distributed agreement, failures and recovery management, and in general system support for Internet-scale computing and distributed data processing and storage. This course highlights key developments in computing system design over the last two decades and illustrates how insight has evolved to implementation.
Instructor: Lena Mashayekhy Offered: Spring 2022
📎 Syllabus
CISC467/667-011 – Computing for Social Good
Course Description: This seminar will explore the broad, ongoing themes around Computing for Social Good, inclusive of advances in Human-Computer Interaction (HCI), the Internet-of-Things, Artificial Intelligence, and the myriad areas that they influence in our modern society. One of our aims will be to differentiate between technology solutions that sound good and those that have a chance for real impact. As a result, we will take a systems perspective—to trace root causes and find the right place(s) to make lasting change.
Prerequisites: CISC 275
Instructor: Matthew Mauriello Offered: Spring 2021, Spring 2022, Spring 2023
CISC474 – Advanced Web Technologies
Course Description: Programming and architecture of web servers and the technologies for implementing high performance, sophisticated web sites for applications like e-commerce. Students learn how to install and set-up a web server, how to write and install programs for a web server, and how to design and implement multi-tier client/server applications with database backends.
Instructors rotate between Matthew Mauriello and Greg Silber.
Prerequisites: CISC275
Instructor: Matthew Mauriello Offered: Fall 2021, Fall 2022, Fall 2023, Fall 2024
CISC481/681 – Artificial Intelligence
Course Description: Programming techniques for problems not amenable to algorithmic solutions. Problem formulation, search strategies, state spaces, applications of logic, knowledge representation, planning and application areas.
Prerequisites: CISC220 and CISC304 or equivalent
Instructor: Rahmat Beheshti Offered: Spring 2021, Fall 2021, Spring 2022, Fall 2022, Spring 2023
CISC482/682 – Introduction to Human-Computer Interaction
Course Description: Research and theoretical methods for the study, design, implementation, and evaluation of effective user-interactive systems, including requirements for gathering, survey design, and rapid prototyping. Hands-on group projects supplement theoretical knowledge
Prerequisites: CISC275
Restrictions: Students who received credit in CISC682 are not eligible to take this course without permission
Instructor: Matthew Mauriello Roghayeh (Leila) Barmaki Offered: Fall 2021, Fall 2022, Fall 2023, Fall 2024
CISC483 – Introduction to Data Mining
Course Description: Concepts, techniques, and algorithms for mining large data sets to discover structural patterns that can be used to make subsequent predictions. Emphasis on practical approaches and empirical evaluation. Use of a workbench of data mining tools, such as the Weka toolkit.
Prerequisites: CISC 220 and MATH 205 or MATH 350
Instructor: Roghayeh (Leila) Barmaki Rahmat Beheshti Offered: Spring 2021, Fall 2021, Spring 2022, Fall 2022, Spring 2023
📎 Syllabus
CISC484/684 – Introduction to Machine Learning
Course Description: Development of methods to learn to solve a task using examples. Explore different machine learning algorithms/techniques and discuss their strengths and weaknesses and situations they are or are not suited for.
Prerequisites: Basic background in probability and statistics
Restrictions: Credit cannot be received for both CISC484 and CISC684
Instructor: Xi Peng Offered: Spring 2021, Fall 2021, Spring 2022, Fall 2022, Spring 2023
CISC637 – Database Systems
Course Description: Physical and logical organization of databases. Data retrieval languages, relational database languages, security and integrity, concurrency, distributed databases.
Prerequisites: CISC220
Restrictions: Students who received credit in CISC437 are not eligible to take this course without permission.
Offered: Fall 2021, Spring 2022, Spring 2023
CISC689 – Artificial Intelligence: Introduction to Network Science
Course Description: Network science is one of the most important disciplines in the research areas such as data mining, physical complex systems, epidemiology, communications, electrical circuits, social science, and bioinformatics.
Most of these areas have a common formal basis when the studied system contains a set of individual objects or components connected together in some way. In this course we will cover basic topics related to this common basis. They will include models and properties of networks (small-world, scale-free, degree distributions, etc.), measures of importance of network elements (centrality, clustering coefficients, etc.), robustness, optimization and visualization. Practical work will include analysis of real-world networks.
Prerequisites: CISC681There is an AI course prerequisite for this course which was added by mistake. Please contact me to approve the prerequisite override.
Instructor: Ilya Safro
Offered: Fall 2020, Spring 2021
📎 Syllabus
CISC849 – Advanced Topics in Computer Applications: Game Theory for Distributed Systems
Course Description: Game theory provides a set of effective tools to understand complicated interactions among many decision-makers. In this course, we will survey recent research at the interface between game theory and distributed systems, including Clouds, IoTs, CPS, and Federated Learning. Students will be introduced to key concepts of game theory and its applications. We will also identify open research challenges and directions.
Instructor: Lena Mashayekhy Offered: Fall 2021
📎 Syllabus
CISC849 – Advanced Topics in Computer Applications: Intro to Educational Data Mining
Course Description: In this course, we learn about learning! This course covers core research methods in Educational Data Mining (EDM). Students will learn how to execute these methods in standard software packages, and the limitations of existing implementations of these methods. Equally importantly, students will learn when and why to use these methods.
Discussion of how EDM differs from more traditional statistical and psychometric approaches will be a key part of this course; in particular, we will study how many of the same statistical and mathematical approaches are used in different ways in these research communities.
Restrictions: Requires permission of instructor.
Instructor: Roghayeh (Leila) Barmaki
Offered: Spring 2022
📎 Syllabus
CISC867/ELEG867-015 – Seminar: Elective Course: Computing and data science for soft materials innovation & discovery
Course Description: The course is the intersection of high-performance computing, data science, and soft material design.
The idea of this semester-long “hackathon” course will be to address scientific problems from multiple company/national labs. We are hoping to have 5-7 industry/national lab problems. Each scientific problem will be tackled by an interdisciplinary group of 3-4 students with disciplinary expertise in soft materials or data science and/or high-performance computing.
Prerequisites: See syllabus
Instructor: Arthi Jayaraman Austin Brockmeier
Offered: Spring 2022, Spring 2023
📎 Syllabus
CISC889 – Advanced Topics in AI: Deep Learning
Course Description: This course will cover advanced deep-learning topics. Out of many possible topics, the course will cover generative adversarial networks, latent variable models (VAEs), graph neural networks, deep reinforcement learning, and foundation models (transformer and generative methods). The class will include lecture sessions (covering the theory and applications), followed by paper reading sessions (covering papers picked by the students). Students would have a great flexibility and are encouraged to choose reading materials and project topics that directly fits to their graduate or personal research interests.
Instructor: Rahmat Beheshti Offered: Spring 2023, Spring 2024
CISC889-011 – Human-Centered AI
Course Description: This course will cover the ever-increasing implications of AI. It will go beyond the classic “how to develop AI systems as utility-maximizers” theme and will study other important considerations in designing such systems. Some of the covered topics include interpretability, reproducibility, bias, corrigibility, privacy, ethics, automation, literacy, sustainability, democracy, and singularity. The course will start by discussing these topics at a higher level, and will then continue on both theoretical (e.g., discussing ideas) and technical (e.g., covering algorithmic solutions) tracks by focusing on some of the topics. As such, the students will choose either a theoretical or technical topic for class presentation or project.
Prerequisites: CISC681 or instructor’s permission
Instructor: Rahmat Beheshti Offered: Spring 2021
COMM306 – Digital Technology and Politics
Course Description: Focuses on how technology influences and is used in political campaigns, public policy debates, governance, and public opinion, as well as how politics shapes technological development, processes, and policy. Special attention given to synergy among political actors, media, non-governmental organizations and citizens in shaping, consuming, and producing communication technology and policy.
NOTE: Counts as a second-writing requirement
Instructor: Lindsay Hoffman
Offered: Spring 2020, Spring 2023
CPEG/ELEG652 – Principles of Parallel Computer Architectures
Course Description: Provides an introduction to the principles of parallel computer architecture. Begins at a level that assumes experience in introductory undergraduate courses such as digital system design, computer architecture, and microprocessor based systems.
Instructor: Rudolf Eigenmann Offered: Spring 2021, Spring 2022, Spring 2023
📎 Syllabus
CPEG467/667 – Computational & Data-Intensive Research Platforms & Applications
Course Description: An introductory course for students conducting computational and data-intensive research in all disciplines, providing an overview of relevant computer systems hardware, software, and applications.
Prerequisites: This course is available to members of computational and data-intensive (CDI) research teams of all sciences. There are no formal prerequisites. Attendees should be involved in CDI research, have substantial experience in developing application code, and be proficient in a programming languages. If you are strongly interested in taking this course but do not have such background, contact the instructor.
Instructor: Rudolf Eigenmann Offered: Fall 2019, Fall 2020, Fall 2021
📎 Syllabus
CPEG657 – Search and Data Mining
Course Description: With the increasing amount of textual information, it is important to develop effective search engines, such as Google’s engine, to help users manage and exploit the information. This course is designed to give you a broad view of information retrieval. And, you will build critical skills through hands-on experience solving real-world problems in information retrieval and text mining.
Prerequisites: Students should come with GOOD programming skills.
Instructor: Hui Fang Offered: Spring 2019, Spring 2020, Spring 2021, Spring 2022
CRJU467-011 – Data Science for Criminal Justice
Course Description: This course provides an introduction to crime analysis. Specific skills that students will learn include mapping, handling text data, making plots, completing simple statistical tests and models, and developing interactive web applications for criminal justice contexts. An emphasis is placed on manipulating data to provide creative and valuable evidence in criminal justice policy debates. Throughout this course, students will learn how to use R, a free software program.
Instructor: Ellen Donnelly Offered: Fall 2021
ECON306 – Introduction to Econometrics
Course Description: This course provides an introduction to econometrics, with a focus on application and practice. The main objective of this course is to teach students to use and interpret a basic set of quantitative methods frequently employed in empirical analysis of economic phenomena. Applications of these methods will be emphasized throughout the course. Course topics include a review of estimation and hypothesis testing, linear regression and several extensions to linear regression.
Prerequisites: C- or better in ECON 101, ECON 103; and STAT 200, STAT471 or MATH 450.
Offered: Spring 2024, Fall 2024
ECON422 – Econometric Methods and Models I
Course Description: This course includes advanced topics in econometrics and policy evaluation. The course focuses on causal reasoning: evaluating the causal effects of policies or other economic variables on outcomes of interest. It will demonstrate how causal reasoning and econometrics are applied in microeconomics research. Econometric techniques will be put into practice through data analysis using statistical software.
Prerequisites: Must earn a C- or better in ECON 300 or ECON 301; and ECON 306 or MATH 202.
Restrictions: Students who received credit in ECON 415 are not eligible to take this course without permission.
Offered: Spring 2024, Fall 2024
ECON423 – Econometric Methods and Models II
Course Description: Class discussion and research in advanced economic statistics and applied econometrics.
Prerequisites: ECON 422
Offered: Fall 2024
ECON622 – Applied Econometrics I
Course Description: Applies and modifies statistical techniques to economic data; presents the essentials of econometric theory.
Prerequisites: MATH 202 or STAT471 or equivalent.
Restrictions: Cannot be taken for credit for MS or PhD degree in Economics.
Offered: Fall 2024
ECON822 – Econometric Theory I
Course Description: Statistical basis for econometric analysis, which includes general linear model, discrete and continuous distributions, methods of estimation, properties of estimators, nested and non-nested hypothesis testing, asymptotic theory. All topics will be illustrated using the General Linear Model.
Prerequisites: MATH202 or STAT471 or equivalent.
Offered: Fall 2024
ECON823 – Econometric Theory II
Course Description: Extensions of the General linear model, including heteroskedasticity, autocorrelation, identification and estimation of simultaneous equations, and error in measurement.
Prerequisites: ECON 822.
Offered: Spring 2024
ECON824 – Econometrics of Cross-Section and Panel Data
Course Description: Econometric techniques used in applied microeconomic analysis. Cross-section techniques, limited dependent variables, panel data analysis. Additional topics may also be covered.
Prerequisites: ECON 822 and ECON 823.
Offered: Fall 2024
EDUC865 – Measurement Theory
Course Description: Pending
Prerequisites: An introductory quantitative methods course and some familiarity with logistic regression are recommended. Students interested in the course who do not have these prerequisites should contact Dr. Student.
Instructor: Sanford Student Offered: Spring 2024
EDUC867 – Survey Design for the Social Sciences
Course Description: Surveys are ubiquitous across the social sciences in both research and applied settings. This course will cover the process of developing a high-quality survey–that is, a survey that measures what it is intended to measure. Topics will include definition of what the survey is intended to measure, development of questions aligned to that target, piloting the survey, and analyzing responses. Methods covered will include construct mapping, reviewing prior literature, item writing, cognitive interviews/think-alouds, reliability, classical test theory scoring and analysis, and dimensionality analysis (exploratory and confirmatory factor analysis). Suitable for master’s and doctoral students across the social sciences seeking the skills to develop, adapt or apply high-quality survey instruments in their own research and practice.
Prerequisites: None: We will be using R for some analyses during the second half of the course, assuming no prior use of R.
Instructor: Sanford Student Offered: Fall 2023
EDUC873 – Multilevel Models in Education
Course Description: Introduction to multilevel modeling. Considers the analysis of growth and change as a special case of multilevel modeling.
Offered in Fall of odd-numbered years.
Prerequisites: EDUC812 and EDUC856, or equivalent.
Instructor: Henry May Offered: Fall 2018, Fall 2021, Fall 2023
ELEG305 – Signals and Systems
Course Description: Introduction to signals and systems, with an emphasis on time and frequency characterization of linear, time-invariant systems. Covers discrete and continuous time systems, sampling, and Fourier, Laplace, and Z transforms. Application examples include medical imaging, radar, audio and image processing, virus delivery protocols, and biological networks.
Prerequisites: A minimum grade of C- or better in MATH242 and a C- or better in ELEG 205
Instructor: Austin Brockmeier Offered: Spring 2020, Spring 2021, Spring 2022, Spring 2023
📎 Syllabus
ELEG405/ELEG605 – Engineering Machine Learning Systems
Course Description: Engineering Machine Learning Systems are applied in an array of real-world applications. This course focuses on their conceptualization, estimation, computational implementation, and optimization. Topics supervised and unsupervised learning, linear and logistic regression, dimensionality reduction, regularization, neural networks, convolution neural networks, decision trees, and select additional deep learning topics.
Prerequisites: Basic probability theory, discrete math, simple calculus, linear algebra (preferred), and programming experience, particularly in Python, or equivalents.
Instructor: Kenneth Barner Offered: Fall 2022, Fall 2023
📎 Syllabus
ELEG467/667 – Matrix and Tensor Methods for Signal Processing, Machine Learning and Data Science
Course Description: Matrix and tensor methods underlie much of modern signal processing, machine learning, and data science. This course provides an overview of powerful techniques and fundamental theory for matrix and tensor methods in these contexts. The goal is for students to become fluent in applying techniques based on matrix and tensor methods (through hands-on experience deriving and implementing algorithms to solve challenging problems in signal processing, machine learning, and data science) and to learn how to use these techniques wisely (by looking “under the hood” at the underlying theory).
Potential topics include: matrix decomposition (eigendecomposition, singular value decomposition, and their geometric interpretation),
low-rank approximation and dimensionality reduction (fundamental subspaces, subspace learning, subspace classification, signals + noise and random matrix theory), tensor decomposition (e.g., canonical polyadic, Tucker), generalized matrix and tensor decompositions (e.g., nonnegative decompositions, Poisson decompositions, etc.).
Topics will be illustrated through a variety of examples coming from signal processing, machine learning, and data science (e.g., linear least-squares regression, classification, Procrustes alignment, matrix completion, recommender systems, PageRank, etc).
ELEG 467 vs ELEG 667: ELEG 467/667 is a single course offered at both the undergraduate level (ELEG 467) and the graduate level (ELEG 667). The courses are the same except that students enrolled in ELEG 667 will have additional assignments and assessments.
Prerequisites: Knowledge of undergraduate-level linear algebra, vector calculus, and probability. Exposure to signal processing, machine learning, computational data science, or other areas involving numerical computing is helpful but not required.
Instructor: David Hong Offered: Fall 2024
ELEG491 – Ethics and Impacts of Engineering
Course Description: This course will analyze a range of social, ethical, and legal issues that arise because of the power and pervasiveness of computers and information technology, including privacy, security, free speech, intellectual property, Big Data, cyberwar, autonomous systems, emerging technologies, and professional ethical codes.
Instructor: Thomas M Powers Offered: Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023
📎 Syllabus
ELEG601 – Convex Optimzation
Course Description: Optimization algorithms underlie numerous modern methods in signal processing, machine learning, and data science. This course provides an introduction to techniques and theory from convex optimization. The goal is for students to become fluent in using convex optimization techniques (through hands-on experience computing numerical solutions to challenging optimization problems) and to learn how to use these tools wisely (by understanding underlying theory).
Topics include:
Convex sets and functions
Convex optimization problems (including linear, quadratic, and semidefinite programs)
Algorithms for unconstrained and constrained convex optimization (including descent methods, Newton’s method, and interior-point methods)
Disciplined convex programming (with software tools)
Duality and optimality conditions
If time permits: accelerated methods, stochastic methods, nonconvex optimization, …
Topics will be illustrated through examples coming from a variety of applications, e.g., coming from signal processing, machine learning, and data science.
Prerequisites: Undergraduate-level linear algebra and probability. Exposure to signal processing, machine learning, computational data science, or other areas involving numerical computing is helpful but not required.
Instructor: David Hong Offered: Fall 2023
ELEG631 – Digital Signal Processing
Course Description: Theory of discrete-time signals and systems with emphasis on the frequency domain description of digital filtering and discrete spectrum analysis, fast Fourier transform, z-transform, digital filter design, relationship to analog signal processing.
Instructor: Xiang-Gen Xia Offered: Fall 2021, Fall 2022
ELEG668 – Detection and Estimation
Course Description: This course covers the fundamentals of detection and estimation theory for statistical signal processing. Topics include hypothesis testing theory, signal detection theory for both deterministic and random signals, signal estimation theory with various optimal estimators for both deterministic and random parameters, and their properties and performance analysis.
Prerequisites: ELEG305, ELEG306, ELEG310 or equivalent courses.
Restrictions: Knowlegde of material covered in ELEG305, ELEG306, ELEG310 or equivalent courses.
Instructor: Xiang-Gen Xia Offered: Fall 2021
ELEG815/FSAN815 – Statistical Learning
Course Description: Introduction to the mathematics of data analysis. Bayes estimation, linear regression and classification methods. The singular value decomposition and the pseudo-inverse. Statistical models for inference and prediction in finance, marketing, and engineering applications. Regularization methods and principles of sparsity priors are applied. Streaming solutions. High dimensional problems. Concepts reinforced in R programming experiments.
Prerequisites: Undergraduate probability and linear algebra
Restrictions: First course in linear algebra. First course in probability and statistics. Basic programming skills.
Instructor: Gonzalo Arce Offered: Fall 2019, Fall 2020, Fall 2021, Fall 2022
📎 Syllabus
ELEG817/CISC817/FSAN817 – Large scale machine learning
Course Description: Introduction to the analysis and processing of massive and/or high-dimensional data. Large-scale machine learning problems can involve growth in the number of data points, features, target variables, or related prediction tasks. Approaches to address these cases rely on concepts from optimization theory, statistics, and artificial neural networks. Computational and statistical scaling from both theoretical and practical perspectives are covered.
Prerequisites: A previous graduate-level machine learning or a statistical estimation course.
Instructor: Austin Brockmeier Offered: Fall 2019, Fall 2020, Fall 2021, Fall 2022, Fall 2023
📎 Syllabus
ENWC417/617 – Quantitative Ecology
Course Description: This course introduces students to the field of quantitative ecology, which involves various approaches to analyzing ecological datasets and testing ecological hypotheses. By the end of the course you should be proficient at using the statistical programming software R, be able to plot and analyze ecological datasets and conduct specific ecological analyses including distance sampling analyses and occupancy modeling.
Instructor: Vincenzo Ellis Offered: Fall 2021, Fall 2022
EPID603 – Biostatistics for Health Sciences I
Course Description: An introductory statistics course for graduate students in the College of Health
Sciences with applications for clinical and population health. The course is taught using statistical
software.
Offered: Fall 2023
EPID604 – Introduction to Epidemiologic Data Analysis in SAS
Course Description: Overview of basic analytical epidemiologic methods using SAS Statistical Software.
The course covers working with SAS Statistical Software to import, modify, merge, analyze, store, and
document the steps of data analysis from the collection of data through the dissemination of results.
Offered: Fall 2023
EPID605 – Epidemiology Methods I
Course Description: Introduction to epidemiological concepts and methods including descriptive data, measures of association, and study design.
Instructor: Jennifer Horney Offered: Fall 2021, Fall 2022, Fall 2023
EPID610 – Epidemiology Methods II
Course Description: Continued introduction to epidemiological concepts and methods. Emphasis on
calculation and interpretation of crude and adjusted data, measures of association, and study design.
Offered: Spring 2023
EPID613 – Biostatistics for Health Sciences II
Course Description: An intermediate statistics course for graduate students in the College of Health
Sciences with applications for population health. The course covers research designs, analysis of
confounding, and logistic regression. The course is taught using SAS statistical software.
Prerequisites: EPID 603
Offered: Spring 2023
EPID614 – Biostatistics for Health Sciences III
Course Description: An intermediate statistics course for graduate students in the College of Health
Sciences with applications to clinical health. The course covers research designs, ANOVA, linear
regression, and multiple regression. The course is taught using SPSS software.
Prerequisites: STAT 656, EPID 613.
Offered: Fall 2023
EPID621 – Methods in Field Epidemiology
Course Description: An introduction to methods used by field epidemiologists. Emphasis on conducting outbreak investigations from start to finish, study design, questionnaire development, interviewing techniques, data analysis, and communications of findings appropriate to various audiences.
Instructor: Jennifer Horney Offered: Spring 2022
EPID631 – Analyzing Epidemiologic Data Using R
Course Description: Analyze epidemiologic data using R, software, organize, data, make plots ranging from the basic (boxplots, histograms, scatterplots), to the more advanced (beanplots, volcano, and Manhattan plots), and present results from your data in a compelling way.
Offered: Fall 2024
FINC/FSAN841 – Financial Services Markets
Course Description: Focuses on the economic roles and interactions of the units in each sector of the financial services industry. Overview of the current and changing layout of institutions, products, and practices.
Offered: Fall 2024
FINC/FSAN842 – Financial Services Risk Analytics
Course Description: Develops the theoretical and practical foundations of resource allocation across time and risky assets, and of credit risk and systemic considerations. Topics include allocation of resources across time; allocation of resources across risks and credit risk and systemic considerations.
Instructor: Paul Laux Offered: Spring 2023, Spring 2025
FINC430 – Fintech and Data Science for Finance
Course Description: Overview of the fintech industry, considering both company stories and strategies. Develop foundation skills in a selection of computing and data science topics that are useful in fintech, including R, Jupyter Notebooks, and machine learning. Apply these skills in some analytical projects relating to fintech. Grading will be based on cases, projects, homework, and presentations.
Prerequisites: MISY 262, FINC 311, FINC 312, FINC 314
Restrictions: Discovery Learning Experience
Offered: Spring 2023, Fall 2023, Spring 2024, Fall 2024
FSAN820 – Foundation of Optimization
Course Description: Concept of optimization, convex set, convex function, unconstrained optimization, convex optimization problems, including least-squares, linear, and quadratic optimization, duality theory, sensitivity analysis. Modeling of more advanced optimization techniques including integer programming, geometric and semidefinite programming, and convex relaxations.
Prerequisites: First course on linear algebra and calculus.
Instructor: Bintong Chen Offered: Spring 2023, Spring 2025
FSAN830 – Business Process Management Innov
Course Description: Employ a data-driven approach to designing, managing, and improving the business processes that execute a firm’s strategy. Exploring the linkage between strategy and business process design and quickly moves into identifying key process metrics which have greatest leverage on improving performance at both the process and firm-wide levels. Planning and controlling for variability in business processes is discussed with applications drawn from diverse settings. Become proficient in improving processes based on leveraging data and learning to deploy resources and information to achieve consistently good outcomes.
Instructor: Adam Fleischhacker Offered: Spring 2023, Spring 2025
GEOG/MAST481/681 – Remote Sensing of Environment
Course Description: Introduces important technology of remote sensing to further our understanding of Earth s environment. Gain an in-depth look at the principles, techniques and applications of remote sensing. Basic skills in computer processing of digital satellite images using ENVI and ArcGIS software are provided.
Instructor: Pinki Mondal Offered: Fall 2021
GEOG/PHYS/SPPA167 – Foundations of Data Science for Everyone
Course Description: This course will teach the fundamentals and the basics of Machine Learning and AI, how to extract information from data, good practices for handling and visualizing small- and big-data, familiarize you with statistical analysis, error analysis, and bias. The course is taught online and will be organized in a modular fashion, with labs and projects assigned to students for group work. The class will be taught simultaneously at Lincoln University and the University of Delaware, and it’s designed to be accessible even if you have no or minimal math or statistic background.
Prerequisites: Some coding experience is recommended but not required.
Instructor: Federica Bianco Offered: Fall 2023
GEOG271 – Introduction to Geographic Data Analysis
Course Description: A survey of computational, statistical, and graphical techniques used in scientific data analysis with particular emphasis on the special nature of geographic and spatial data. Includes scripting language programming.
Offered: Fall 2023
GEOG367/UAPP367/ELEG367/CISC367/PHYS367 – Geospatial Data Science
Course Description: Introduces principles, techniques, and applications of data science in geospatial contexts. Focuses on geospatial and statistical concepts and algorithms for processing, analyzing, and visualizing spatial data; hypothesis testing; and model building. Examines impacts and ethics of geospatial data.
Instructor: Gregory Dobler Jing Gao Austin Brockmeier Offered: Spring 2023
GEOG372 – Introduction to GIS
Course Description: Fundamental geographic concepts and principles necessary to effectively use GIS to examine geographic problems. Hands-on training is provided in the use of professional GIS software in the context of collecting, managing, processing, analysis and presenting geographic data. Emphases is placed on the nature of spatial data, modeling techniques, spatial analysis and cartographic design.
Instructor: Pinki Mondal Offered: Spring 2020, Fall 2020, Spring 2021, Fall 2021, Spring 2022, Fall 2022, Spring 2023
📎 Syllabus
GEOG405 – Computer Programming for Environmental Research
Course Description: Introduction to Python programming and machine learning. Topics include the basics of Python programming and machine learning algorithms, such as linear and logistic regression, decision trees, neural networks and selected topics in deep learning. Case studies include their applications in environmental systems analysis and modeling.
Prerequisites: STAT200
Instructor: Yao Hu Offered: Fall 2024
GEOG472/686 – Cartography: Art & Science of Mapping Data
Course Description: A picture is worth a thousand words. A map is worth much more. This course provides a broad introduction to cartography, surveying the science, art, and ethics of making and using maps. Students will learn to design appealing maps that effectively communicate geospatial information.
Instructor: Jing Gao Offered: Spring 2020, Spring 2021, Spring 2022
📎 Syllabus
GEOG480/680 – Know Your Satellites
Course Description: Learn about earth observing satellites; work with free satellite data on Google Earth Engine (GEE) platform; real world environmental science applications; collaborate on a paper or class project; develop geospatial data science skills
Instructor: Pinki Mondal Offered: Fall 2019, Fall 2021
GEOG604 – GIS in Environmental Research
Course Description: Explores the application of GIS to environmental problems. Reviews current research in the field, and implements relevant techniques for analysis of a variety of environmental problems.
Instructor: Kyle Davis Offered: Spring 2020, Fall 2021, Spring 2023
GEOG605010 – Computer Programing for Environmental Research
Course Description: Introduction to Python programming and machine learning. Topics include the basics of Python programming and machine learning algorithms, such as linear and logistic regression, decision trees, neural networks and selected topics in deep learning. Case studies include their applications in environmental systems analysis and modeling.
Instructor: Yao Hu Offered: Fall 2024
GEOG667 – Spatial Data Analysis & Modeling
Course Description: Spatial Data Analysis and Modeling are crown jewels of an analyst’s toolbox for geospatial data science investigations. As spatiotemporal data become increasingly available about more and more aspects of our lives and the environments we interact with, spatial data analysis and modeling hold a powerful key to discover unseen patterns, extract meaningful insights, and unlock new solutions for all kinds of applications. This course focuses on advanced quantitative data-driven analysis and modeling methods for describing, identifying, understanding, modeling, and predicting spatiotemporal phenomena. Students will learn to choose, evaluate, and apply spatial data analysis and modeling methods to conduct meaningful and ethical geospatial research in academia or industry.
Prerequisites: Incoming students should already be familiar with (1) quantitative data representations of geospatial phenomena (e.g., geospatial coordinate systems, spatial and attribute data types and characteristics, file formats), (2) basic statistical concepts and analyses (e.g., descriptive statistics, correlation, hypothesis testing, confidence intervals, bivariate regression), and (3) fundamental GIS operations (e.g., spatial inquiries, overlay, buffer, surface analysis, raster algebra, map making). Incoming students should already have preparations, at least, at the elementary level in (1) statistics (algebra-based rather than calculus-based) (e.g., ENSC/STAT475, CIEG315, ELEG310, MATH205, or STAT200), and (2) geographic information systems/science (GIS) (e.g., GEOG670 or 671). The course is not tied to specific software packages nor programming languages. Students are free to use any statistical and GIS software or coding environments they prefer for solving homework problems.
Instructor: Jing Gao Offered: Spring 2024
GEOG670 – Geographic Information Systems and Science
Course Description: Introduces the principles and concepts of geographic information science to effectively use a professional level geographic information system. Practical hands-on exposure to “real” data and GIS software and hardware is provided through exercises and a final project.
Instructor: Jing Gao Offered: Fall 2020, Spring 2021, Fall 2021, Spring 2022, Fall 2022, Spring 2023
📎 Syllabus
GEOG671 – Advanced Geographic Information Systems
Course Description: Advanced study of geographic information science and systems (GIS) including more complex spatial data models, editing and topology, data encoding, data quality, preprocessing techniques, spatial analysis, and cartography and visualization techniques. Hands-on experience using commercial and/or open source GIS package.
Prerequisites: GEOG 670 or permission of instructor.
Instructor: Tracy DeLiberty Offered: Fall 2024
GEOG681 – Remote Sensing of Environment
Course Description: Introduces important technology of remote sensing to further our understanding of Earth’s environment. Gain an in-depth look at the principles, techniques and applications of remote sensing. Basic skills in computer processing of digital satellite images using ENVI and ArcGIS software are provided.
Crosslisted with MAST 681 and ELEG 681.
Instructor: Pinki Mondal Offered: Fall 2024
GEOL427/627 – Introduction to Geological Remote Sensing
Course Description: Principles of active and passive remote sensing data interpretation for geologists. Study of geomorphic, structural, and lithologic characteristics of the Earth as observed in aerial and satellite data. Emphasis on the use of multispectral, radar, and LiDAR data for geologic mapping.
Instructor: Michael O’Neal Offered: Spring 2019
HOSP448 – Data Analytics in the Hospitality Industry
Course Description: Focuses on the value of data driven decisions. The course discusses the mechanics of identifying data points, data visualization, predictive modeling and machine learning concepts within the context of the hospitality industry.
Prerequisites: HOSP187 or MISY160 and HOSP180 or BUAD110
Instructor: Timothy Webb Offered: Fall 2021, Fall 2022
HOSP890 – Hospitality Business Analytics
Course Description: Provides the building blocks for hospitality analytics from a data science perspective. Covers the importance of data in today’s service economy and the challenges big data may present. Covers how to leverage data to make operational decisions specific to the hospitality industry with the goal of improving operations. Specifically, the course will tackle current issues facing hospitality executives and discuss analytically driven solutions that have been researched or identified.
Prerequisites: HOSP 848
Restrictions: Permission by instructor required.
Instructor: Timothy Webb Offered: Spring 2020, Fall 2020, Spring 2022
📎 Syllabus
MAST629 C/L CIEG629 – Marine Ecosystem Modeling
Course Description: The course introduces process-oriented ecosystem models to tackle contemporary environmental issues in marine research, focusing on integrating biogeochemical principles and numerical methods to evaluate ecosystem interactions and interpret results within assumptions and uncertainties.
Prerequisites: MATH302
Instructor: Yun Li Offered: Spring 2024
MAST638 – Machine Learning for Marine Science
Course Description: This course aims to offer a timely introduction to Machine Learning (ML) concepts, tools, and applications in Marine Science, with focuses on (1) the implementation of ML algorithms using Python, (2) the commonly used ML models (e.g., supervised and unsupervised) in Marine Science, and (3) ML model interpretation and evaluation.
Prerequisites: Basic background in data statistics, STAT475.
Instructor: Yun Li Offered: Fall 2023, Fall 2024
MAST686 – Remote Sensing Seminar (1 credit)
Course Description: Basic and applied research topics in remote sensing of earth resources, coastal processes, estuarine productivity, ocean dynamics and climatic effects presented by University students, faculty and guest speakers. Stresses advanced data acquisition and spectral and spatial image analysis techniques.
Restrictions: May be repeated for credit when topics vary.
Instructor: Xiao-Hai Yan Offered: Fall 2022
MATH219 – Data Science I
Course Description: Component: Lecture
Introduction to methods in data science. Describes the representation and exploration of data, classification, regression, clustering, and network science, with particular attention to mathematical formulations and foundations. Includes hands-on use of software tools and applications to real-world datasets.
Prerequisites: MATH 241 and CISC 106.
Instructor: Tobin Driscoll Offered: Spring 2024
MATH419 – Data Science II
Course Description: Component: Lecture
Advanced topics in data science, with a focus on modern machine learning. Includes sophisticated mathematical formulations and analyses of methods, drawing on prior experiences from throughout the curriculum, as well as the use of software that implements the methods. Students are expected to work on open-ended projects and to communicate their findings clearly, using disciplinary-standard tools and style.
Prerequisites: MATH 219, MATH 243, MATH 349, MATH 350.
Instructor: Tobin Driscoll Offered: Spring 2024
MATH612 – Computational Method for Equation Solving and Function Minimization
Course Description: LU and QR factorizations, singular value and eigenvalue decompositions, matrix conditioning, solution of linear
systems and linear least-squares problems, iterative methods in linear algebra, descent and quasi-Newton methods of optimization, globalizing convergence, constrained optimization, applications
Prerequisites: Elementary linear algebra and programming.
Instructor: Richard Braun Offered: Fall 2019, Fall 2020, Fall 2021, Fall 2022
MATH637 – Mathematical Techniques in Data Science
Course Description: Linear methods for regression (subset selection, ridge, lasso), Logistic regression. Analysis of the convergence and complexity of common algorithms. Linear discriminant analysis, Principal component analysis, Additive Models, Kernel Smoothing. Cross-validation, Bootstrap, Support Vector Machines, Cluster analysis (K-means, spectral clustering), Undirected graphical models, Expectation maximization algorithm, Introduction to deep learning, Introduction to Bayesian methods.
Prerequisites: Probability theory and basic statistics (e.g. MATH 350), Multivariable calculus (e.g. MATH 243), Linear Algebra (e.g. MATH 349), Optimization background (e.g. MATH 529) desirable but not necessary, basic computing skills.
Instructor: Vu Dinh Offered: Spring 2022, Spring 2023, Spring 2024
MATH667 – Topological Data Analysis
Course Description: This course is a survey of topological and algebraic-topological methods for the analysis of large data sets and complex systems. Students will gain a rigorous mathematical foundation for these tools and hands-on experience using them with real data sets.
Catalog Description: Topological methods in data analysis. Network analysis, multi-layer networks, visualization and community detection. Simplicial models for data, persistent homology, topological statistics and null models. Methods such as quasi-periodicity detection, shape and image analysis, multi-dimensional persistence.
Prerequisites: Undergraduate linear algebra (e.g. MATH 349), multi-variable calculus (e.g. MATH 243), and statistics or probability (e.g MATH 350); coding ability consistent with an undergraduate programming course (e.g. CISC108)
Offered: Spring 2019, Spring 2022
MISY467 – Machine Learning for Business
Course Description: This course introduces the basic concepts and techniques of machine learning and covers most commonly used models for predictive analytics. The end-to-end workflow for typical machine learning projects is illustrated via multiple business programming cases and Kaggle competitions. If time permits, deep learning techniques are also introduced. This course is programming intensive using Python 3 and popular packages, such as Jupyter, Numbpy, Pandas, Matplotlib, Seaborn, and Scikit-Learn.
Note: this course will be renamed to MISY331 and become a required core course for the undergraduate major in Business Analytics.
Instructor: Harry Jiannan Wang Offered: Spring 2020
MISY615 – Enterprise Architecture: Digitally Transforming Business
Course Description: Explore key components of corporate Enterprise Architecture (EA) and how the environment transforms business creating sustainable value. Examine contemporary EA platforms: ERP, CRM, cloud, social & mobile computing, e-commerce, supply chain, data governance, mashups, big data, & data centers.
Offered: Fall 2023
MISY636 – Unstructured Data Analytics
Course Description: Topics on analyzing and predicting from unstructured data. Suggested topics include natural language processing for mining textual data and deep learning models for analyzing audio and video data. Knowledge of Python required.
Offered: Spring 2023
MISY640 – Project Management and Costing
Course Description: Provides the technical knowledge and skills needed to successfully plan, execute, and evaluate IT projects. Strong emphasis on the costing of IT projects.
Offered: Spring 2023, Fall 2023
MISY641 – Data Mining for Business Analytics
Course Description: Introduces fundamental strategies and methodologies for data mining along with the concepts underlying them and will provide hands-on experience with a variety of different techniques in a business setting. Students will learn to use data mining tools.
Prerequisites: BUAD 620
Offered: Spring 2023
MISY650 – Security and Control
Course Description: Considers state-of-the-art technological and organizational approaches to enhancing the security and integrity of corporate information resources in a cost-effective manner.
Offered: Spring 2023
MISY655 – Ethics in Technology Management
Course Description: This course offers extensive and topical coverage of the legal, ethical, and societal implications of information technology. Students will learn about issues such as file sharing, infringement of intellectual property, security risks, Internet crime, identity theft, employee surveillance, privacy, compliance, social networking, and ethics of IT corporations. Students will gain an excellent foundation in ethical decision making for current and future business managers and IT professionals.
Offered: Fall 2023
MISY665 – Introduction to Cybersecurity
Course Description: Introduction to computer and network security and covers the foundation security policies and methods to provide confidentiality, integrity, and availability, as well as cryptography, auditing, and user security. Topics are reinforced with hands-on exercises run in a virtual machine environment.
Restrictions: Students who received credit in CISC465, CISC665, CPEG465, CPEG665, ELEG465, ELEG665, MISY465 or MISY665 are not eligible to take this course without permission.
Offered: Fall 2023
MISY667 – Introduction to Python
Course Description: An introductory programming course for solving problems using Python. Students will be exposed to many fundamental topics such as data types, control structures, input/output, loops, iterations, functions, classes, inheritance, polymorphism, graphical user interfaces, and data access & exploration. Assignments give hands-on experience of application development and data analytics using an integrated development environment.
Offered: Fall 2023
MISY675 – Dashboard Design & Storytelling
Course Description: Upon completion of this course, students should be able to: Clearly understand the specifics of various chart types, the process of chart making and chart selection. Learn how to (i) design dashboards and integrate data with Qlik Sense, and (ii) use Qlik Sense for storytelling. Develop skills to tell a compelling data-driven story.
Offered: Fall 2023
NURS844/HLTH844 – Population Healthcare Informatics
Course Description: Integrates knowledge of healthcare information technology and public health data to support and facilitate individual and population health management and improvement. Focuses on the analysis and application of information technologies that support the provision of care including social context, availability of technology, and structure of information along with legal, regulatory, and ethical concerns. Emerging technologies and contemporary issues are highlighted.
Restrictions: Graduate student. Instructor Permission required
Instructor: Susan Conaty-Buck Offered: Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023
PHIL655 – Ethics in Data Science & Artificial Intelligence
Course Description: Seminar on societal impacts of data gathering and analysis, with applications in health sciences, disaster science, policing, and e-commerce. Participation-based format. Topics include: privacy, algorithmic biases and data incompleteness, profiling, safety, and informed consent. Available as 1 credit or 3 credit course
Instructor: Thomas M Powers Offered: Spring 2021, Fall 2021, Fall 2022
PHYS467 – Data Science for Scientists
Course Description: Computational data analysis, application of statistical and machine learning methods in the physical sciences, consideration on epistemology and scientific ethics. Project-based.
Prerequisites: Highly recommend completion of CISC 106, MATH 205 or 241, PHYS207, familiarity with linear algebra prior to enrollment.
Instructor: Federica Bianco Offered: Fall 2019
PHYS667 – Machine Learning for Time Series Analysis
Course Description: This class covers applications of machine learning to time series analysis in a project-based framework. Using real world data and examples in the natural sciences (astrophysics, bioinformatics, seismology, neuroscience, particle physics, and condensed matter) and real-world problems including finance and policy, we will
explore a variety of modern time series analysis techniques including Bayesian approaches to template fitting, Gaussian Processes, Time Warping, and Artificial Intelligence including Recurrent Neural Networks and Transformers.
Prerequisites: background in coding and statistics (e.g. CISC 106 or CISC 108, and MATH 349). Domain knowledge in any specific field is not required.
Restrictions: Graduate-level (+undergrad with permission)
Instructor: Federica Bianco Offered: Spring 2020, Spring 2022
📎 Syllabus
PHYS667 – Computer Vision for the Physical Sciences
Course Description: The testing of hypotheses in scientific theories relies on analysis of experimental data. In many cases, that data takes the form of images acquired with camera systems of varying technological complexity and modality such as RGB cameras, imaging spectrometers, MRI machines, telescopes, scanning electron microscopes, etc. Computer vision (CV) is a branch of the computer and information sciences focused on extracting information from imaging data. In recent years, advancements in Machine Learning methodology have led to correspondingly significant advancements in the power of CV algorithms to learn from data. In modern Data Science applications, CV plays a prominent role in a variety of domain-specific problem solving, including the physical sciences. This course will introduce students in the physical sciences to the foundations of information extraction from imaging data via the application of CV algorithms to data across numerous fields of physics. Students will learn the theory behind CV and implement those algorithms on real-world data sets using the Python coding language.
Instructor: Gregory Dobler Offered: Fall 2020, Fall 2022
POSC815 – Introduction to Statistical Analysis for Political Science
Course Description: Training in the basics of statistical analysis and quantitative approaches to politics and society. Topics include elementary statistics, probability theory, hypothesis testing and regression analysis. Provides foundation for further quantitative methods.
Restrictions: Open to graduate students only.
Instructor: Benjamin Bagozzi Offered: Fall 2019, Fall 2020, Fall 2021
POSC817 – Statistical Analysis for Political Science II
Course Description: Advanced course in statistical methods in Political Science. Provides greater depth in quantitative methods, giving the opportunity to learn how to read and analyze quantitative work and to prepare for conducting independent research.
Prerequisites: POSC815 or permission of the instructor.
Instructor: Benjamin Bagozzi Offered: Spring 2021, Spring 2022
SOCI301 – Introduction to Sociological Research
Course Description: Survey of research methods and data analysis employed in sociology.
Prerequisites: SOCI201 and completion of the College of Arts and Sciences math requirement
Instructor: Mieke Eeckhaut Offered: Fall 2021, Spring 2022, Fall 2022, Fall 2023, Spring 2024
SOCI614 – Advanced Data Analysis
Course Description: This course intends to expose graduate students to advanced statistical methods used in the social sciences. The course builds upon the lessons of ordinary regression analysis and introduces new material on the prediction of alternative outcomes (e.g. binary, categorical, ordered, and count). The course also teaches students how to develop and present their own empirical studies in anticipation of the peer-review process.
Instructor: Ellen Donnelly Offered: Spring 2020, Spring 2022
SOCI625 – Advanced Social Statistics
Course Description: The application of advanced statistics to social science research questions and data, including the use of longitudinal modeling, multilevel modeling, structural equation modeling, cluster solutions, models for categorical and limited dependent variables, and others as appropriate.
Prerequisites: SOCI 614 or Instructor Consent.
Instructor: Mieke Eeckhaut Offered: Fall 2024
SPPA667-011 – Seminar: Urban Evidence Based Policy
Course Description: This class will focus on the technical and interpretation aspects of evidence-based policy in the context of problems related to urban environments. Students will learn data science methodology including statistics and machine learning, focusing on time series and geospatial analysis. Curricular elements include: exploiting open data collections – gather data and prepare data – extract statistical information – model mechanisms – elements of machine learning based analysis – visualize data and present evidence – interpret evidence presented by their peers.Each method will be approached as it applies to existing data and problems, and problems will be explored in multiple urban contexts, generating comparative studies.
Prerequisites: Basic statistical knowledge and basic (python) coding skills will be assumed
Restrictions: Undergrads allowed with permission
Instructor: Federica Bianco Offered: Fall 2020
SPPA721 – Data Science Tools for Evidence-based Policy
Course Description: As societal data has become increasingly ubiquitous and accessible, there has been a rapid growth in the opportunity to utilize evidence-based decision making in public policy for a variety of tasks including planning, performance evaluation, and comparative analysis. At the same time, the recently established field of Data Science is focused on developing methods and tools to extract insight, understanding, and evidence from diverse and often large data sets. This course will introduce students to those tools and techniques for working with data in the context of evidenced-based policy, focusing on how data is accessed, handled, analyzed, and visualized. Topics covered will include: an introduction to Python, R, and Data Science; accessing and working with policy-relevant data; data structures (including geospatial information); basic statistics and analysis; and data visualization.
Instructor: Gregory Dobler Offered: Fall 2021, Fall 2023
SPPA800 – Research Design and Data Analysis
Course Description: Focuses on concepts, issues and techniques related to research design, data acquisition and data analysis in the fields of urban affairs and public policy.
Restrictions: Open to all UAPP & DISA PhD students. Others need Instructor consent
Instructor: A.R. Siders Offered: Fall 2020, Fall 2021, Fall 2022
STAT601010 – Probability Theory for Operations Research and Statistics
Course Description: Provides the basic background in probability theory for further work in statistics and operations research. Basic topics: sample spaces and axioms of probability; conditional probability and independence; Bayes theorem; random variables; moments and moment generating functions; transformations of random variables; common families of distributions; multivariate distributions, covariance and correlation; probability inequalities and limit theorems.
Restrictions: Students are expected to have had Analytic Geometry & Calculus C.
Instructor: Peng Zhao Offered: Fall 2024
STAT602 – Mathematical Statistics
Course Description: Derived sampling distributions; decision theory; estimation theory; multivariate normal; hypotheses testing; special topics.
Prerequisites: STAT601
Instructor: Peng Zhao Offered: Spring 2024
STAT603 – Statistical Computing and Optimization
Course Description: Many modern statistical machine learning problems for Big Data analytics can be formulated by function optimization and linear algebraic computation. This course will provide necessary knowledge of convex optimization and matrix computation, and gain fundamental understandings of important numerical algorithms commonly used in statistical machine learning. We will emphasize on both efficient implementation and understanding for statistical computing problems. The topics to be covered include: fundamental methods for matrix and linear systems computation, matrix decomposition, convex analysis, duality and KKT conditions, 1st/2nd order methods, EM methods. Important statistical computing applications including GLM, SVM, sparsity learning, greedy function approximation, and deep neural networks will be covered.
Prerequisites: STAT601 and STAT602
Restrictions: Basic programming knowledge (such as R, Python, MATLAB, or C/C++) is assumed.
Instructor: Wei Qian Offered: Spring 2021, Spring 2022, Spring 2023
STAT608 – Statistical Research Methods
Course Description: An introductory statistics course for advanced undergraduate and graduate students with applications for life sciences, business, health, engineering, and the social sciences. The course managing and describing data; the normal, t, F and chi squared distributions; the logic of inference; inferential statistics for one and two sample problems; analysis of table data; analysis of variance; and multiple regression. The course is taught using statistical software.
Offered: Fall 2023
STAT611 – Linear Regression
Course Description: Simple linear and nonlinear regression. Subset regression; principal component and ridge regression. Introduction to experimental design and design models.
Restrictions: Students are expected to have completed a Linear Algebra course.
Instructor: Peng Zhao Offered: Fall 2020, Fall 2021, Fall 2022, Fall 2023
STAT612 – Advanced Regression Techniques
Course Description: Selected topics in advanced regression analysis.
Restrictions: Requires permission of instructor.
Instructor: Cencheng Shen Offered: Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023
STAT613 – Applied Multivariate Statistics
Course Description: Explores the main topics of multivariate statistics, including principal components, discrimination, classification procedures, and clustering techniques. Emphasis on how to identify the correct technique for a given problem, computer packages for its computation, and how to interpret the results.
Offered: Fall 2023
STAT617 – Multivariate Methods and Statistical Learning
Course Description: This is an applied multivariate and statistical machine learning course intended for graduate students in statistics or related fields. The aim of this course is to introduce a variety of statistical methods for multivariate analysis and machine learning, involving statistical computing mostly with R and Python.
Prerequisites: STAT 601 (Probability) and STAT611 (Regression), or their equivalent courses. Basic programming knowledge (such as R, Python, MATLAB, or C/C++) and linear algebra is assumed.
Instructor: Shanshan Ding Offered: Fall 2023, Fall 2024
STAT619 – Time Series Analysis
Course Description: Fundamental topics in time series analysis – features the Box and Jenkins techniques of fitting time series data. Includes an introduction to appropriate statistical packages.
Restrictions: Requires permission of instructor.
Instructor: Shanshan Ding Offered: Spring 2019, Spring 2021, Spring 2022, Spring 2023
STAT621 – Survival Analysis
Course Description: Statistical techniques used in the analysis of censored data including the Kaplan-Meier estimator, the analysis of one, two and K sample problems, and regression analysis based on the Cox proportional hazards model.
Restrictions: Requires permission of instructor.
Instructor: Jing Qiu Offered: Fall 2018, Fall 2019, Fall 2020, Fall 2022
STAT622 – Statistical Network Analysis
Course Description: Statistical theory and learning for network data, including random graph models, graph embedding theory, and machine learning for graph data.
Instructor: Cencheng Shen Offered: Fall 2022, Fall 2023, Fall 2024
STAT631 – Introduction to Python
Course Description: An introductory statistics course in the use of Python statistical software. The
course is designed to give a good introduction into the use of Python in statistical analysis. Students will learn to read data from different sources such as SAS, SPSS, or Excel; modify data through formulas and transformation; manage data through merging, concatenating, and stacking data; graph data; and conduct a statistical analysis.
Offered: Spring 2024
STAT632 – Introduction to JMP Software
Course Description: An introductory statistics course in the use of JMP statistical software. The course is designed to give a good introduction into the use of JMP in statistical analysis. Students will learn to read data from different sources such as SAS, SPSS, or Excel; modify data through formulas and transformation; manage data through merging, concatenating, and stacking data; graph data; and conduct a statistical analysis.
Offered: Spring 2024
STAT634 – Introduction to R
Course Description: An introductory statistics course in the use of R statistical software. The course is designed to give a good introduction into the use of R in statistical analysis. Students will learn how to set up R; data structure, data type, and control structure in R; data frames and functions; qualitative data in R; quantitative data in R; and how to conduct a statistical analysis in R.
Offered: Spring 2024
STAT656 – Biostatistics
Course Description: Research designs, review of inference and regression, categorical data, logistic regression, rates and proportions, sample size determination. Additional topics such as nonparametric methods, survival analysis, longitudinal data analysis, and randomized clinical trial may be covered.
Prerequisites: STAT 608 or STAT 611
Offered: Fall 2023
STAT675 – Logistic Regression
Course Description: Practical and computational introduction to logistic regression and related topics. Applications include financial, marketing and biomedical research. The use of SAS and other statistical packages will be emphasized.
Restrictions: Requires permission of instructor.
Instructor: Shanshan Ding Offered: Spring 2019, Spring 2020, Spring 2021, Spring 2022
UAPP667 – Machine Learning for Public Policy
Course Description: Data-driven policy decisions are becoming an increasingly important aspect of civic operations. From infrastructure assessment to environmental regulations to public safety, data exploration has opened up new avenues for enabling decision makers to enhance the public welfare and quality of life. However, the data themselves do not tell the story and do not drive the decision making process. Rather, it is the analysis of that data by inference through the application of computational algorithms known as machine learning (ML; including artificial intelligence) that enable their use. This course will survey some of the most common ML and analysis techniques used in the analysis of data (from academic, public, and private sectors) that impacts policy and decision making. It will focus on both qualitative and quantitative understanding of these techniques as well as appropriate use of analysis methods including uncertainties, model bias, and ethical considerations.
Instructor: Gregory Dobler Offered: Spring 2019, Spring 2020, Spring 2021
📎 Syllabus