What courses are in an MS in Data Science?
Courses offered in the MS in Data Science program build from core concepts in algorithms and statistics to a wide range of applications in diverse fields.
MS in Data Science Courses
MS in Data Science students will earn their degree with 21 credits of required core courses, 6 credits of electives, and a 3-credit applied capstone project (30 credits in all).
We expect a full-time student to complete the program in 11 months (Fall, Spring, Summer), and the course offerings are structured to support this. In unusual circumstances a student may decide to devote a longer time to completing the program. To maintain full-time status during Fall and Spring semesters, a student must enroll in at least 9 credits.
Online students pursue the program on a part-time basis.
Application Deadlines
On-Campus Program:
Fall 2025: June 1, 2025, 11:59PM EST
Note: There is no spring admission for the on-campus program.
Online Program:
Spring 2025: December 1, 2024, 11:59PM EST
MS in Data Science Program Curriculum
CORE COURSES
GRAD 5100: Fundamentals of Data Science
Establishes background knowledge in Python and R programming, multivariate calculus and linear algebra, and basic statistics to prepare incoming students for success in the core courses in the MS in Data Science Program.
Prerequisites: Open only to students in the Data Science M.S. program.
STAT 5405: Applied Statistics for Data Science
Statistics essential for data science incorporating descriptive statistics; integrative numerical description and visualization of data; graphical methods for determining and comparing distributions of data; data-driven statistical inference of one-sample, two-sample, and k-sample problems; linear regression; and non-linear regression.
Prerequisites: Instructor consent and introductory course in mathematical statistics and regression analysis. Not open to students who have passed STAT 5505 or STAT 5605 or BIST 5505 or BIST 5605.
STAT 5410: Statistical Computing for Data Science
Principles and practice of statistical computing in data science: data structure, data programming, data visualization, simulation, resampling methods, distributed computing, and project management tools.
Prerequisites: Introductory course in mathematical and applied statistics; introductory course in programming. Instructor consent required.
CSE 5709: Machine Learning for Data Science
An introduction to the techniques of machine learning, including models for both supervised and unsupervised learning, and related optimization techniques, covering topics such as regression, neural networks, clustering, model evaluation and selection, and implementation of learning algorithms from first principles.
Prerequisites: GRAD 5100; Open to graduate students in the M.S. in Data Science program. Recommended preparation: Python programming, multivariable calculus, linear algebra, introductory statistics.
CSE 5710: Data Mining for Data Science
This course presents an introduction to data mining algorithms in the areas of classification, association analysis, clustering, and anomaly detection, with an emphasis on a conceptual understanding these algorithms along with their application in real-world problems and domains.
Prerequisites: CSE 5709; Open to graduate students in the M.S. in Data Science program. Recommended preparation: Python programming including open-source libraries: scikit-learn, Numpy, Matplotlib, introductory statistics
EPSY 5641: Research Design and Measurement for Data Science
Research design, ethical and measurement issues as they relate to data science. Measurement topics include: Design of surveys and survey instruments, reliability, validity and generalizability theory. Research design topics include: AB designs, clustering and the identification of internal and external validity threats. Open and reproducible science and ethical conduct of research are themes throughout the course.
Prerequisites: Open to students enrolled in the M.S. Data Science program or with instructor consent. Recommended preparation: Knowledge of Introductory inferential and descriptive statistics.
ARE 5353: Data Ethics and Equity in the Era of Misinformation
This course will introduce students to issues of ethics and equity in the contemporary practice of data science. The ability to collect, store, process, and analyze ever greater amounts of data offers great opportunities, as well as potential perils. This course will provide examine the ethical implications of data collection, usage, and distribution. Topics will include systematic approaches to assessing ethical issues; privacy and confidentiality; defining research and the responsibilities associated with conducting ethical research; implicit and structural biases in data collection and analysis.
Prerequisites: None
OPIM 5605: Data Visualization and Communication
Data visualization is a form of storytelling that provides an effective way to draw conclusions and share insights, allowing people to express big, complex ideas in simple ways. Utilizing state of the art software, the use of parameters, filters, calculated variables, color, space and motion to visually articulate the data are surveyed. Common pitfalls and ethics issues in visualization design are also considered. This interactive course is designed to help students learn the methods, tools, and techniques to best understand and present complex data so that they can persuasively share results and influence decisions.
Prerequisites: Open only to M.S. Data Science students; others with consent.
GRAD 5800: Applied Capstone in Data Science
This course will give M.S. in Data Science students a problem-based learning opportunity to apply knowledge and skills gained in core and elective courses to an integrative data science project. Projects for the course come from industry and program partners and will typically require students to work in small teams to solve a real-world data science problem.
Prerequisites: Enrollment in the M.S. in Data Science program; Department or Unit consent required. Recommended preparation: Nine credits of coursework required for the M.S. in Data Science.
RECOMMENDED ELECTIVES
Electives are chosen based upon a student's academic background and interests. UConn’s MS in Data Science offers a wide range of electives across the University. Below are some possibilities for full-time students depending on course offering availability. Students are required to take 2 electives, 6 credits, to complete their degree.
BIST 5625: Introduction to Biostatistics
Rates and proportions, sensitivity, specificity, two-way tables, odds ratios, relative risk, ordered and non-ordered classifications, rends, case-control studies, elements of regression including logistic and Poisson, additivity and interaction, combination of studies and meta-analysis.
Prerequisites: Open to graduate students in Biostatistics, others with permission.
CSE 5050: Algorithms and Complexity
Design and analysis of efficient computer algorithms. Algorithm design techniques, including divide-and-conquer, depth-first search, and greedy approaches. Worst-case and average-case analysis. Models of computation. NP-complete problems.
Prerequisites: Open to grad students in CSE, others with consent. Recommended preparation: Discrete math; fluency in a high-level programming language; data structures, algorithms at the level of CSE 2050. Students cannot receive credit for both CSE 3500 and 5050.
CSE 5707: Discrete Optimization
Methods and techniques used to solve combinatorial optimization problems with examples drawn from industry such as scheduling, resource allocation, and routing. Features a mix of theory and practice using state-of-the-art tools to solve classic problems.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3500 and MATH 2210Q.
CSE 5717: Big Data Analytics
Focuses on data science and big data analytics. Introduces basic concepts of data science and analytics. Different algorithmic techniques employed to process data will be discussed. Specific topics include: Parallel and out-of-core algorithms and data structures, Rules mining, Clustering algorithms, Text mining, String algorithms, Data reduction techniques, and Learning algorithms. Applications such as motif search, k-locus association, k-mer counting, error correction, sequence assembly, genotype-phenotype correlations, etc. will be investigated.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3500 and MATH 2210Q.
CSE 5800: Bioinformatics
Advanced mathematical models and computational techniques in bioinformatics. Topics covered include genome mapping and sequencing, sequence alignment, database search, gene prediction, genome rearrangements, phylogenetic trees, and computational proteomics.
Prerequisites: Open to graduate students in the CSE program, others with consent.
CSE 5825: Bayesian Machine Learning
Bayesian machine learning is a unifying methodology for reasoning about uncertainty when modelling complex data. This course begins by covering the foundations of probabilistic modelling, Monte Carlo and variational inference algorithms, and model checking. We build on these foundations by considering essential models, e.g., mixed-membership and hierarchical models, and their applications. The course concludes with a survey of recent advances in Bayesian machine learning focusing on Bayesian nonparametrics and other advanced topics.
Prerequisites: Department consent required; open to graduate students in the CSE program, others with permission. Recommended preparation: CSE3500; MATH 2110, 3160 or STAT 3345Q, or the equivalent; CSE 4820 or 5819 are also desirable but not as critical.
CSE 5830: Probabilistic Graphical Models
Probabilistic graphical models provide a flexible framework for analyzing large, complex, heterogeneous, and noisy data. They are the basis for state-of-the-art analysis methods in a wide variety of application domains, from autonomous robotics and computer vision to medical diagnosis and social networks. This course covers (a) representation, including Bayesian and Markov networks, (b) inference, both exact and approximate, and (c) estimation of both parameters and structure of graphical models.
Prerequisites: Department consent required; open to graduate students in the Computer Science and Engineering program, others with permission.
CSE 5850: Introduction to Cyber-Security
Introductory to the area of cyber-security. The course focuses on applied cryptography, and some of its applications and related areas in cyber security, including network and web security, usable security, privacy/anonymity, and block-chains. The course is systems-oriented; we will discuss many practical vulnerabilities, attacks and defenses. However, esp. in the beginning, we will also learn some theory - mainly, few definitions, and (fewer) proofs.
Prerequisites: Department consent; open to graduate students in the Computer Science and Engineering program, others with permission. Not open for credit to students who have passed CSE 3400. Recommended preparation: CSE 2500.
EPSY 5643: Text Analytics
This course provides an applied introduction to text analytics with special emphasis on its application to education. Students will learn to use common toolkits in the Python ecosystem to analyze large-scale text data in order to generate insights into educational, cognitive, and social processes.
Prerequisites: EPSY 5641. Recommended preparation: This course requires an understanding of introductory statistics and regression at the level of EPSY 5605 and EPSY 5610 as well as some prior experience with statistical programming in a language like R or Python.
MKTG 5115: Marketing Management
A strategic and analytical approach to marketing decisions. Students will develop basic proficiency with key marketing concepts and skills including: identifying opportunities and threats in the market environment; forecasting market growth; evaluating customers and competitors; segmenting, targeting, and positioning; determining product, price, place and promotion components of marketing strategies; and assessing marketing performance.
Prerequisites: Open only to MBA students, others with consent. Not open to students who have passed MKTG 5182.
NRE 5215: Introduction to Geospatial Analysis with Remote Sensing
Introduction to collecting, managing, displaying, and analyzing geospatial data. Topics include coordinate systems, finding and using existing sources of geospatial data, analysis of vector and raster data, creating geospatial data with remote sensing, concepts of Global Positioning System (GPS), topographic and landscape analyses, and spatial interpolation.
Prerequisites: None
NRE 5525: Remote Sensing of the Environment
Introduction to remote sensing theory and practice. Includes electromagnetic radiation, spectral reflectance, earth observation platforms and sensors, image processing methods, and multidisciplinary applications.
Prerequisites: Instructor Consent
NRE 5585: Python Scripting for Geospatial Analysis
GIS scripting techniques in Python for geospatial analyses, enabling students to pursue integrated research in earth resources data geoprocessing applications.
Prerequisites: Instructor consent. Recommended preparation: NRE 5215 or equivalent.
OPIM 5509: Introduction to Deep Learning
Introduction to topics related to deep learning and will build on your previous experience in predictive analytics. Use of neural networks for a host of data and applications - including time series data, text data, geospatial data, and image data.
Prerequisites: OPIM 5512 and 5604; open only to MBA, MSBAPM, and MS FinTech students, others with consent. Not open to students who have passed OPIM 5894 when offered as Introduction to Deep Learning.
OPIM 5512: Data Science Using Python
Data science concepts using the Python programming language. Data wrangling and management using Pandas; visualization using MatPlotLib; fundamentals of matrix algebra and regression, with illustrations using Numpy; machine learning, focusing on fundamental concepts, classification, and information extraction.
Prerequisites: OPIM 5604; MBA, MSBAPM, and MS FinTech students, others with consent. Recommended preparation: Students are expected to know the fundamentals of Python programming language (or another language) through self-study, previous coursework or previous work experience, including topics such as loops, functions, and data structures. Not open to students who have passed OPIM 5894 when offered as Data Science with Python.
OPIM 5604: Predictive Modeling
Introduces the techniques of predictive modeling in a data-rich business environment. Covers the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement predictive models for a variety of practical business applications. Predictive models such as neural networks, decision trees, Bayesian classification, and others will be studied. The course emphasizes the relationship of each step to a company's specific business needs, goals and objectives. The focus on the business goal highlights how the process is both powerful and practical.
Prerequisites: Open only to MBA, MSBAPM, MS Data Science and MS FinTech students, others with consent. Corequisite: OPIM 5603.
STAT 5825: Applied Time Series
Introduction to prediction using time-series regression methods with non-seasonal and seasonal data. Smoothing methods for forecasting. Modeling and forecasting using univariate autoregressive moving average models.
Prerequisites: Open to graduate students in Statistics, others with permission.
STAT 5845: Applied Spatio-Temporal Statistics
Applied statistical methodology and computing for spatio-temporal data, including visualization, models, and inferences. Extreme value analysis in spatio-temporal contexts will be a module. Focus will be on models that account for spatio-temporal dependence and inferences that provide appropriate uncertainty measures, with applications to real-world problems using open-source software.
Prerequisites: Open to graduate students in Statistics; others with per-mission.
Recommended preparation: STAT 5405 or 5605 or GEOG 5600 or 5610 or ERTH 5150 or equivalent.
Specialty Electives
Students who are on-campus may be able to enroll in a specialty elective if they meet the prerequisite requirements, the course is available, and they gain instructor consent.
ARE 6313: Applied Econometrics II
An introduction to econometric methods used in contemporary applied economic data analysis. Emphasis on learning how to operationalize different estimation techniques in standard statistical software.
Prerequisites: ARE 5311
BIST 5615: Categorical Data Analysis
Statistical analysis of data on a nominal scale: discrete distributions, contingency tables, odds ratios, interval estimates, goodness of fit tests, logistic/probit/complementary log-log regression, Poisson-related regression.
Prerequisites: STAT 5405 or BIST 5505 and 5605, or instructor consent.
BIST 5645: Concepts and Analysis of Survival Data
Survival models, censoring and truncation, nonparametric estimation of survival functions, comparison of treatment groups, mathematical and graphical methods for assessing goodness of fit, parametric and nonparametric regression models.
Prerequisites: Open to graduate students in Biostatistics, others with permission.
BIST 5815: Longitudinal Data Analysis
Statistical theory and methodology for data collected over time in a clustered manner: design of experiments, exploratory data analysis, linear models for continuous data, general linear models for discrete data, marginal and mixed models, treatment of missing data.
Prerequisites: BIST 5505 and 5605; or instructor consent.
CSE 5103: Performance Engineering
Study of performance engineering techniques for the development of software systems to meet performance objectives. Software performance principles, hierarchical performance modeling, and current research trends related to Software Performance Engineering. Methods for computer performance evaluation and analysis with emphasis on direct measurement and analytic modeling, including queuing networks, computation structure models, state charts, probabilistic languages, and Petri-nets. Case studies for the evaluation and analysis of software architecture and design alternatives.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3504 and 4500 or the equivalent.
CSE 5304: High-Performance Parallel Computing
Topics in high-performance computing such as the following (1) Parallel Algorithms and Parallel Computation: including programming models (Circuits and PRAMs), complexity analysis, modern parallel platforms and programming libraries; (2) Shared- and Distributed-Memory Parallel Architectures: including cache coherence, Memory consistency, processor synchronization, latency tolerance and hiding; (3) Interconnection Networks: including quantitative measures, topologies, switch architectures, routing strategies; as well as (4) Contemporary and Future HPC Systems.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 4302 and 4500.
CSE 5500: Algorithms
Introduction to the design and analysis of algorithms. The course will discuss fundamental design techniques and related issues such as amortized analysis, linear programming, network flow, NP-Completeness, approximation algorithms, randomized algorithms, advanced data structures, and parallel algorithms.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3500 or the equivalent.
CSE 5506: Computational Complexity
Systematic study of resource-bounded computation, including time and space complexity, hierarchy theorems, nondeterministic and randomized computation, and reduction and completeness. Advanced topics may be introduced such as relativized computation, derandomization, communication complexity, lower bounds on circuit complexity, and probabilistically checkable proofs.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3502 and 3500; MATH 3160 or the equivalent.
CSE 5510: Distributed Computing and Fault Tolerance
Topics in the design and analysis of robust distributed algorithms that combine efficiency and fault tolerance. Models of distributed computation and failures. Inherent limitation in achieving fault tolerance in distributed systems. Basic problems considered include communication services, robust cooperation, agreement, consistent distributed memory.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE4300 and/or CSE4500.
CSE 5512: Introduction to Quantum Computing
Introduction to quantum computing, quantum algorithms, and quantum information theory. Quantum mechanics including elementary aspects of its mathematical formalism; quantum circuit model and quantum complexity theory; development and analysis of several fundamental quantum algorithms, focusing on Grover's algorithm for database search and Shor's number-theoretic algorithms. Second half covers the density matrix formalism of quantum mechanics, von Neumann entropy, quantum channels, and quantum error-correction. If time permits, some implementations of quantum computers and current progress will be discussed.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3500, CSE 3502, and MATH 2420Q.
CSE 5520: Data Visualization and Communication
This course will focus on fundamental theory and practice of data visualization and communication. Topics to be covered include different data types, algorithms for data visualization, design of effective visualization for analysis and communication, exploratory and explanatory data analysis (for discovery of new information, detecting flaws, etc.), using data visualization to convey different messages, existing tools for data visualization, and making presentations with data. Several case studies, such as engineering, economics, or health, will be discussed.
Prerequisites: Only open to Computer Science and Engineering graduate students, others with permission. Recommended preparation: knowledge of algorithms, some programming experience required.
CSE 5602: Machine Learning for Physical Sciences and Systems
Foundational knowledge in applied aspects of machine learning, including methods for handling uncertain, small, and imbalanced data; feature selection and representation learning; and model selection and assessment. Students will also gain exposure to state-of-the-art research on interpretability of machine learning models, stability of machine learning algorithms, and meta-learning. Topics will be discussed in the context of recent advances in machine learning for materials, chemistry, and physics applications, with an emphasis on the unique opportunities and challenges at the intersection of machine learning and these fields.
Prerequisites: Open to graduate students in Computer Science and Engineering, MEng in Advanced Systems Engineering, and MEng in Data Science, others with department consent. Recommended prep: Basic concepts in machine learning, linear algebra, optimization, statistics.
CSE 5815: Systems Biology: Constructing Biological Knowledgebase
Design and architecture of biological knowledge base. Topics covered include biological/biomedical data modeling, knowledge representation techniques of biological and biomedical information, review of existing inference methods, methods of assessing evidence quality, design of inference-enabling genomics annotation system, various meta-data analysis methods involving genomics and biomedical data.
Prerequisites: Open to graduate students in the CSE program, others with consent.
CSE 5820: Machine Learning
Enables students to understand and use machine learning methods across a wide range of settings. Mixture of theory, algorithms, and hands-on projects with real data. Besides traditional machine learning topics, e.g., supervised learning, unsupervised learning and semi-supervised learning, introduces advanced topics such as dimension reduction; structured data learning; kernel learning; imprecisely supervised learning; longitudinal data analysis; causal inference, etc.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3500, MATH 2110Q, MATH 2210Q, and MATH 3160 or the equivalent.
CSE 5840: String Algorithms and Applications in Bioinformatics
Classic string matching algorithms (e.g. Knuth-Morris-Pratt, Karp-Rabin, suffix tree and arrays) and more advanced string algorithms (e.g. Burrows-Wheeler transform). With a particular focus on rigorous treatment of string processing algorithms and their analysis. Applications of string algorithms to bioinformatics problems. Students are expected to have basic prior knowledge of algorithm design and analysis.
Prerequisites: Open to graduate students in the CSE program, others with consent.
CSE 5852: Modern Cryptography: Foundations
The foundations of modern cryptography introducing basic topics such as one-way functions, pseudorandom generators, and computational hardness assumptions based on number theory. Fundamental cryptographic constructions such as hard-core predicates, secure symmetric encryption and message-authentication codes, and public-key cryptography.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: MATH 3160, CSE 3500, and CSE 3502 or the equivalent.
CSE 5854: Modern Cryptography: Primitives and Protocols
Modern cryptography, emphasizing provable security and concrete constructions based on the hardness of specific computational problems. After surveying some basic cryptographic primitives and associated number-theoretic constructions, focuses on public-key infrastructure and protocols: it will cover such topics as digital signatures, identification and key-exchange schemes, distributed key generation, blind signatures, zero-knowledge proofs, and private function computation.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: MATH 3160, CSE 3500, CSE 3502, and CSE 4702 or the equivalent.
CSE 5860: Computational Problems in Evolutionary Genomics
Computational and algorithmic approaches for problems arising in evolutionary genomics. Topics may include phylogenetic trees inference, population evolutionary models and theory, understanding complex evolutionary processes and other related topics. Both combinatorial optimization and stochastic approaches will be covered.
Prerequisites: CSE 5800; open to graduate students in the CSE program, others with consent.
CSE 6512: Randomization in Computing
Introduction to the theory and practice of randomization and randomized algorithms as a technique for science and engineering problem solving. Topics to be covered include: probability theory, types of randomization, sorting and selection, hashing and skip list, finger-printing, packet routing, geometry and linear programming, graph algorithms, combinatorial optimization, and external memory algorithms.
Prerequisites: CSE 5500. Open to graduate students in the CSE program, others with consent.
CSE 6800: Computational Genomics
Advanced computational methods for genomic data analysis. Topics covered include motif finding, gene expression analysis, regulatory network inference, comparative genomics, genomic sequence variation and linkage analysis.
Prerequisites: CSE 5800 or BME 5800. Open to graduate students in the CSE program, others with consent.
EEB 5050: Fundamentals of Ecological Modeling
Quantitative inference from ecological and environmental data. Choosing modeling methods based on knowledge of biological processes. Frequentist and Bayesian approaches; analysis of real and simulated data sets.
Prerequisites: STAT 1000Q or 1100Q or 3445 or 5005 or 5505; or equivalent with instructor consent.
EEB 5349: Phylogenetics
Estimation of genealogies at the level of species and above, and their application and relevance to various biological disciplines, including systematics, ecology, and morphological and molecular evolution. Surveys both parsimony and model-based methods, but emphasizes maximum likelihood and Bayesian approaches.
Prerequisites: EEB 5347 or instructor consent.
EPSY 6611: Hierarchical Linear Modeling
Theory and applications of hierarchical linear modeling, including organizational and longitudinal multilevel models.
Prerequisites: None
EPSY 6615: Structural Equation Modeling
An introduction to structural equation modeling. Develop, modify, and interpret a variety of structural equation models commonly used in social science research. Linear models with only observed variables (path analysis), latent variable models without causal paths (confirmatory factor analysis), and latent variable models with causal paths (structural equation modeling). Conceptual understanding, application, and interpretation of structural equation models.
Prerequisites: None
MKTG 5220: Big Data and Strategic Marketing
Offers students the tools to analyze "big" data, to identify patterns that have actionable marketing value. Students will gain hands-on exposure to advanced analytical tools such as neural networks, market basket analysis, sequence detection, text mining, and use of state-of-the-art business modeling software to apply course concepts. Applications include financial services, retail, advertising, insurance, health care and human resources. Directed at students preparing for positions in digital analytics, digital marketing, marketing research, and consulting.
Prerequisites: MKTG 5115, or MKTG 5181 and 5182; OPIM 5103 or 5603, or BADM 5103, or BADM 5180 and 5181.
MKTG 5250: Marketing Research and Intelligence
Introduction to methods and techniques used to gain customer and market insights through marketing research. Students will learn how to identify the most appropriate research techniques to answer particular marketing questions, how to design studies for maximum insight, and how to analyze and critically read results. Qualitative and quantitative approaches will be covered. Directed at students preparing for positions in marketing research, digital analytics, consulting, product and brand management, and product development and innovation.
Prerequisites: MKTG 5115, or MKTG 5181 and 5182; OPIM 5103 or 5603, or BADM 5103, or BADM 5180 and 5181.
MKTG 5251: Marketing and Digital Analytics
Examines how analytic techniques can be used to transform marketing data into strategic insights for decision making. Students gain hands-on experience designing, conducting, and communicating results of marketing analytics on topics such as evaluating markets, discovering customer needs, satisfying customer preferences, predicting customer behavior, and allocating media and communication resources. Directed at students preparing for positions in digital analytics, digital marketing, consulting, product and brand management, customer relationship management, media and communications.
Prerequisites: MKTG 5115, or MKTG 5181 and 5182; OPIM 5103 or 5603, or BADM 5103, or BADM 5180 and 5181.
MKTG 5655: Pricing and Revenue Management
This course provides an overview of pricing strategies and tactics to achieve revenue management goals. Students learn how to develop pricing strategies to account for temporal changes in customer demands, differences across customer segments, loyalty programs, product inventory, and price-quality relationships. Students gain experience using analytic tools for revenue management.
Prerequisites: MKTG 5115, or MKTG 5181 AND MKTG 5182.
NRE 5535: Remote Sensing Image Processing
A variety of related topics that include the physical processes involved in remote sensing and various image processing methods. The labs will be primarily focused on how to use image processing software (e.g., ENVI) to analyze satellite imagery.
Prerequisites: A course in remote sensing of the environment.
OPIM 5502: Big Data Analytics with Cloud Computing
In-depth, hands-on exploration of various cutting-edge information technologies used for big data analytics. The first half focuses on using big data management techniques for ETL (extract-transform-load) operations. The second half focuses on using big data analytics tools for data mining algorithms such as classification, clustering, and collaborative filtering. Extremely hands-on, requiring students to spend significant time working with large datasets. Students are expected to have taken at least one course in data modeling and one course in data mining (please see pre-requisites) or have significant related work experience. Students should expect to become familiar with the Unix operating system, as well as data programming concepts. Students may be required to install some software on their computers on their own, with very little support, if any, from the instructor or anyone else. Students should be willing to troubleshoot any issues during installation, drawing help from Google searches.
Prerequisites: OPIM 5604 or BADM 5604; and OPIM 5272.
OPIM 5504: Adaptive Business Intelligence
The use of techniques from statistics and optimization to implement adaptive business intelligence (ABI) decision support systems. The course will introduce students to the different components of ABI systems as well as to the fundamentals of adaptive statistical methods, simulation adaptive methods, and evolutionary algorithms. Applications to diverse management contexts evolving in time will also be discussed.
Prerequisites: OPIM 5603; open only to MBA and MSBAPM students, others with consent.
OPIM 5508: Healthcare Analytics and Research Methods
Evidence-based practice, research techniques, health data collection devices, legislation and regulation of health data, ethical use of health data, and reporting tools. Prepares students for employment opportunities within a clinical or medical research environment.
Prerequisites: BADM 5103 or BADM 5180 or OPIM 5103 or OPIM 5603; open only to MBA and MSBAPM students, others with consent. Not open for credit to students who have passed OPIM 5894 when offered as Healthcare Analytics.
OPIM 5510: Web Analytics
Introduction to key concepts, techniques, and tools for analyzing web data to derive actionable customer intelligence, develop digital marketing strategies and evaluate their impacts. Clickstream tracking, search engine analytics, digital experiments, and social analytics.
Prerequisites: OPIM 5604; open only to MBA and MSBAPM students, others with consent. Not open for credits who have passed OPIM 5894 when offered as Web Analytics.
OPIM 5511: Survival Analysis with SAS
Describes the various methods used for modeling and evaluating survival data, also called time-to-event data. General statistical concepts and techniques, including survival and hazard functions, Kaplan-Meier graphs, log-rank, and related tests, Cox proportional hazards model, and the extended Cox model for time-varying covariates and non-proportional hazards.
Prerequisites: OPIM 5604; open only to MBA and MSBAPM students, others with consent. Not open for credits who have passed OPIM 5894 when offered as Survival Analysis using SAS.
OPIM 5671: Data Mining and Business Intelligence
Discusses data mining techniques that can be utilized to effectively sift through large volumes of operational data and extract actionable information and knowledge (meaningful patterns, trends, and anomalies) to help optimize businesses and significantly improve bottom lines. The course is practically oriented with a focus of applying various data analytical techniques in various business domains such as customer profiling and segmentation, database marketing, credit rating, fraud detection, click-stream Web mining, and component failure predictions.
Prerequisites: OPIM 5604 or BADM 5604; open only to MBA, MSBAPM, and MS FinTech students, others with consent.
STAT 5665: Applied Multivariate Analysis
Multivariate normal distributions, inference about a mean vector, comparison of several multivariate means, principal components, factor analysis, canonical correlation analysis, discrimination and classification, cluster analysis.
Prerequisites: Open to graduate students in Statistics, others with permission.
STAT 5675: Bayesian Data Analysis
Theory of statistical inference based on Bayes’ Theorem: basic probability theory, linear/nonlinear, graphical, and hierarchical models, decision theory, Bayes estimation and hypothesis testing, prior elicitation, Gibbs sampling, the Metropolis-Hastings algorithm, Monte Carlo integration.
Prerequisites: STAT 5585 and STAT 5685, or instructor consent.
UConn’s Master’s in Data Science is an 11-month cohort based full-time in-person program and is eligible for F-1 and J-1 visa sponsorship. Courses are offered on the University of Connecticut’s Storrs, CT USA Campus. This program is eligible for the STEM OPT extension that affords certain F-1 graduate students an opportunity to apply for a 24-month extension of their post-completion optional practical training (OPT). The program does not offer graduate assistantships and scholarships at this time.
MS in Data Science Course Calendar
| COURSES | FALL | SPRING | SUMMER |
|---|---|---|---|
| Agriculture Resource & Economics (ARE) Courses | |||
| ARE 5353 |
TBA | TBA | TBA |
| Biostatistics (BIST) Courses | |||
| BIST 5615 | Every Other Spring | ||
| BIST 5625 | X | ||
| BIST 5645 | X | ||
| BIST 5815 | X | ||
| BIST 5825 | X | ||
| BIST 5845 | Every Other Spring | ||
| Computer Science & Engineering (CSE) Courses | |||
| CSE 5299 | X | ||
| CSE 5300 | X | ||
| CSE 5304 | X | ||
| CSE 5309 | X | ||
| CSE 5713 | X | ||
| CSE 5800 | X | ||
| CSE 5815 | X | ||
| CSE 5819 | X | ||
| CSE 5840 | X | ||
| CSE 5850 | X | ||
| CSE 5852 | X | ||
| CSE 5854 | X | ||
| CSE 5860 | X | ||
| Educational Psychology (EPSY) Courses |
|||
| EPSY 5641 | TBA | TBA | TBA |
| EPSY 6611 | X | ||
| EPSY 6615 | X | ||
| EPSY 6XXX | X | ||
| Graduate School (GRAD) Courses | |||
| GRAD 5800 | X | ||
| Healthcare Management & Insurance (HCMI) Courses | |||
| HCMI 5240 | X | ||
| HCMI 5243 | X | ||
| HCMI 5686 | X | ||
| Management & Entrpreneurship (MENT) Courses |
|||
| MENT 5377 | X | ||
| MENT 5650 | X | X | X |
| MENT 5674 | X | X | X |
| MENT 5675 | X | X | |
| MENT 5680 | X | ||
| Marketing (MKTG) Courses |
|||
| MKTG 5115 | X | X | X |
| MKTG 5220 | X | Every Other Summer | |
| MKTG 5250 | X | X | Every Other Summer |
| MKTG 5251 | X | X | X |
| MKTG 5665 | X | X | |
| Natural Resources & the Environment (NRE) Courses |
|||
| NRE 5215 | X | ||
| NRE 5525 | X | ||
| NRE 5235 | X | ||
| NRE 5545 | X | ||
| NRE 5560 | X | ||
| NRE 5585 | X | ||
| Operations & Information Management (OPIM) Courses |
|||
| OPIM 5501 | X | X | X |
| OPIM 5502 | X | X | X |
| OPIM 5504 | X | ||
| OPIM 5508 | Every Other Spring & Fall |
||
| OPIM 5509 | X | X | |
| OPIM 5510 | Every Other Spring & Fall | ||
| OPIM 5511 | X | ||
| OPIM 5512 | X | X | X |
| OPIM 5604 | X | ||
| Statistics (STAT) Courses | |||
| STAT 5125 | X | ||
| STAT 5405 | X | ||
| STAT 5415 | X | ||
| STAT 5665 | X | ||
| STAT 5675 | X | ||
| STAT 5825 | X | ||
| STAT 5845 | X | ||
| STAT 5915 | X | ||
MS in Data Science Course Descriptions
AGRICULTURE RESOURCE & ECONOMICS
ARE 5353: Data Ethics and Equity in the Era of Misinformation
This course will introduce students to issues of ethics and equity in the contemporary practice of data science. The ability to collect, store, process, and analyze ever greater amounts of data offers great opportunities, as well as potential perils. This course will provide examine the ethical implications of data collection, usage, and distribution. Topics will include systematic approaches to assessing ethical issues; privacy and confidentiality; defining research and the responsibilities associated with conducting ethical research; implicit and structural biases in data collection and analysis.
Prerequisites: None
BIOSTATISTICS
BIST 5615: Categorical Data Analysis
Statistical analysis of data on a nominal scale: discrete distributions, contingency tables, odds ratios, interval estimates, goodness of fit tests, logistic/probit/complementary log-log regression, Poisson-related regression.
Prerequisites: STAT 5405 or BIST 5505 and 5605, or instructor consent.
BIST 5625: Introduction to Biostatistics
Rates and proportions, sensitivity, specificity, two-way tables, odds ratios, relative risk, ordered and non-ordered classifications, rends, case-control studies, elements of regression including logistic and Poisson, additivity and interaction, combination of studies and meta-analysis.
Prerequisites: Open to graduate students in Biostatistics, others with permission.
BIST 5645: Concepts and Analysis of Survival Data
Survival models, censoring and truncation, nonparametric estimation of survival functions, comparison of treatment groups, mathematical and graphical methods for assessing goodness of fit, parametric and nonparametric regression models.
Prerequisites: Open to graduate students in Biostatistics, others with permission.
BIST 5815: Longitudinal Data Analysis
Statistical theory and methodology for data collected over time in a clustered manner: design of experiments, exploratory data analysis, linear models for continuous data, general linear models for discrete data, marginal and mixed models, treatment of missing data.
Prerequisites: BIST 5505 and 5605; or instructor consent.
BIST 5825: Applied Time Series
Coming Soon
Prerequisites: Coming Soon
BIST 5845: Applied Spatio-Temporal Statistics
Coming Soon
Prerequisites: Coming Soon
COMPUTER SCIENCE & ENGINEERING
CSE 5299: Computer Networks and Data Communication
Introduction to computer networks and data communications. Network types, components and topology, protocol architecture, routing algorithms, and performance. Case studies including LAN and other architectures.
Prerequisites: Department consent; open to graduate students in the Computer Science and Engineering program, others with permission. Not open for credit to students who have passed CSE 3300. Recommended preparation: CSE 2304 or 3666.
CSE 5300: Advanced Computer Networks
Advanced fundamental principles of computer networks. Topics include network design and optimization, protocol design and implementation, network algorithms, advanced network architectures, network simulation, performance evaluation, and network measurement.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3300 or the equivalent. This course and ECE 6431 may not both be taken for credit.
CSE 5304: High-Performance Parallel Computing
Topics in high-performance computing such as the following (1) Parallel Algorithms and Parallel Computation: including programming models (Circuits and PRAMs), complexity analysis, modern parallel platforms and programming libraries; (2) Shared- and Distributed-Memory Parallel Architectures: including cache coherence, Memory consistency, processor synchronization, latency tolerance and hiding; (3) Interconnection Networks: including quantitative measures, topologies, switch architectures, routing strategies; as well as (4) Contemporary and Future HPC Systems.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 4302 and 4500.
CSE 5309: Networked Embedded Systems
Introduction to the design, analysis and implementation of networked embedded systems that interact with the physical environment. Applications of such systems include environmental monitoring, consumer electronics, medical devices, automotive systems, industrial process control, distributed robotics, and smart structures. Topics covered include concepts, technologies and protocols for low-power and resource-restricted wireless networks; models of computation and physical systems; embedded system architectures; and real-time system concepts, theory and design principles.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 2300W, CSE 3300 and CSE 3666.
CSE 5713: Data Mining and Management
Introduction to data mining algorithms and their analysis. Application of and experimentation with data mining algorithms on real-world problems and domains, with a dual focus on addressing the solution quality issue and the time efficiency issue.
Prerequisites: Open to graduate students in the CSE program, others with consent.
CSE 5800: Bioinformatics
Advanced mathematical models and computational techniques in bioinformatics. Topics covered include genome mapping and sequencing, sequence alignment, database search, gene prediction, genome rearrangements, phylogenetic trees, and computational proteomics.
Prerequisites: Open to graduate students in the CSE program, others with consent.
CSE 5815: Systems Biology: Constructing Biological Knowledgebase
Design and architecture of biological knowledge base. Topics covered include biological/biomedical data modeling, knowledge representation techniques of biological and biomedical information, review of existing inference methods, methods of assessing evidence quality, design of inference-enabling genomics annotation system, various meta-data analysis methods involving genomics and biomedical data.
Prerequisites: Open to graduate students in the CSE program, others with consent.
CSE 5819: Introduction to Machine Learning
An introduction to the basic tools and techniques of machine learning, including models for both supervised and unsupervised learning, related optimization techniques, and methods for model validation. Topics include linear and logistic regression, SVM classification and regression, kernels, regularization, clustering, and on-line algorithms for regret minimization.
Prerequisites: Department consent required; open to graduate students in the Computer Science and Engineering program, others with permission. Recommended preparation: MATH 2210Q; STAT 3025, or 3345, or 3375, or MATH 3160; CSE 3500.
CSE 5840: String Algorithms and Applications in Bioinformatics
Classic string matching algorithms (e.g. Knuth-Morris-Pratt, Karp-Rabin, suffix tree and arrays) and more advanced string algorithms (e.g. Burrows-Wheeler transform). With a particular focus on rigorous treatment of string processing algorithms and their analysis. Applications of string algorithms to bioinformatics problems. Students are expected to have basic prior knowledge of algorithm design and analysis.
Prerequisites: Open to graduate students in the CSE program, others with consent.
CSE 5850: Introduction to Cyber-Security
Introductory to the area of cyber-security. The course focuses on applied cryptography, and some of its applications and related areas in cyber security, including network and web security, usable security, privacy/anonymity, and block-chains. The course is systems-oriented; we will discuss many practical vulnerabilities, attacks and defenses. However, esp. in the beginning, we will also learn some theory - mainly, few definitions, and (fewer) proofs.
Prerequisites: Department consent; open to graduate students in the Computer Science and Engineering program, others with permission. Not open for credit to students who have passed CSE 3400. Recommended preparation: CSE 2500.
CSE 5852: Modern Cryptography: Foundations
The foundations of modern cryptography introducing basic topics such as one-way functions, pseudorandom generators, and computational hardness assumptions based on number theory. Fundamental cryptographic constructions such as hard-core predicates, secure symmetric encryption and message-authentication codes, and public-key cryptography.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: MATH 3160, CSE 3500, and CSE 3502 or the equivalent.
CSE 5854: Modern Cryptography: Primitives and Protocols
Modern cryptography, emphasizing provable security and concrete constructions based on the hardness of specific computational problems. After surveying some basic cryptographic primitives and associated number-theoretic constructions, focuses on public-key infrastructure and protocols: it will cover such topics as digital signatures, identification and key-exchange schemes, distributed key generation, blind signatures, zero-knowledge proofs, and private function computation.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: MATH 3160, CSE 3500, CSE 3502, and CSE 4702 or the equivalent.
CSE 5860: Computational Problems in Evolutionary Genomics
Computational and algorithmic approaches for problems arising in evolutionary genomics. Topics may include phylogenetic trees inference, population evolutionary models and theory, understanding complex evolutionary processes and other related topics. Both combinatorial optimization and stochastic approaches will be covered.
Prerequisites: CSE 5800; open to graduate students in the CSE program, others with consent.
EDUCATIONAL PSYCHOLOGY
EPSY 5641: Research Design and Measurement for Data Science
Research design, ethical and measurement issues as they relate to data science. Measurement topics include: Design of surveys and survey instruments, reliability, validity and generalizability theory. Research design topics include: AB designs, clustering and the identification of internal and external validity threats. Open and reproducible science and ethical conduct of research are themes throughout the course.
Prerequisites: Open to students enrolled in the M.S. Data Science program or with instructor consent. Recommended preparation: Knowledge of Introductory inferential and descriptive statistics.
EPSY 6611: Hierarchical Linear Modeling
Theory and applications of hierarchical linear modeling, including organizational and longitudinal multilevel models.
Prerequisites: None
EPSY 6615: Structural Equation Modeling
An introduction to structural equation modeling. Develop, modify, and interpret a variety of structural equation models commonly used in social science research. Linear models with only observed variables (path analysis), latent variable models without causal paths (confirmatory factor analysis), and latent variable models with causal paths (structural equation modeling). Conceptual understanding, application, and interpretation of structural equation models.
Prerequisites: None
EPSY 6XXX: Coming Soon
Coming Soon
Prerequisites: Coming Soon
THE GRADUATE SCHOOL
GRAD 5800: Applied Capstone in Data Science
This course will give M.S. in Data Science students a problem-based learning opportunity to apply knowledge and skills gained in core and elective courses to an integrative data science project. Projects for the course come from industry and program partners and will typically require students to work in small teams to solve a real-world data science problem.
Prerequisites: Enrollment in the M.S. in Data Science program; Department or Unit consent required. Recommended preparation: Nine credits of coursework required for the M.S. in Data Science.
HEALTHCARE MANAGEMENT & INSURANCE
HCMI 5240: Health Care Organization and Management
Examines the nation's healthcare delivery system with overviews provided for each major sector of the health economy. The basic tools of economics and finance are employed to gain critical insights into the structure, conduct and performance of each of these sectors. Designed to accommodate both health care professionals and individuals from other business areas interested in learning more about the health care industry.
Prerequisites: Open to MBA students and others with consent.
HCMI 5243: Health Care Economics
Demonstrates how various economic theories can be used to think about health care issues and takes a macro or industry perspective of various health care problems and policy questions. Students are provided with a set of economic tools to evaluate a theoretical or empirical argument relating to health or medical care. Culminates with an in-depth analysis of the structure, conduct, and performance of the markets for medical insurance, physician services, hospital services, pharmaceutical products, and long-term care. Health care reform is also discussed.
Prerequisites: HCMI 5240 or instructor consent.
HCMI 5686: Health Insurance and Risk Management
A detailed overview of the purpose, structure, operation, and performance of the health insurance industry from the perspective of various stakeholders including insurance company owners, employers, individual consumers of health insurance services, and society. Emphasis is placed on individual and group health insurance products with respect to administration, selling and marketing, underwriting, pricing, and claims administration. Managed-care techniques, benefit-package design, and cost-sharing mechanisms are also evaluated and discussed.
Prerequisites: HCMI 5240 or instructor consent.
MANAGEMENT & ENTREPRENUERSHIP
MENT 5377: Human Resource Metrics and Talent Analytics
Creating and managing appropriate metrics is vital to enabling the development of high-achieving people in organizations and maintaining an effective human resource function. Introduces techniques for developing effective metrics and identifies connections between human resource metrics and other performance measurement systems commonly used in organizations. Introduces students to talent analytics, the tools and techniques managers use to mine organizational data in pursuit of actionable knowledge. Students learn how to structure research questions, communicate data needs to technical specialists, and interpret data to yield organizational insights and support effective decisions. Formerly offered as MGMT 5377.
Prerequisites: None
MENT 5650: Interpersonal Relations, Influence, and Ethical Leadership
Communication challenges and difficult conversations faced by business professionals. Emphasizes core values associated with ethical leadership in the professional world with a particular focus on the connections between applied ethics and management issues. Topics include conflict resolution styles and models, negotiation, organizational politics, influencing processes, the language of leadership, and models for examination and resolution of ethical workplace dilemmas. Formerly offered as MGMT 5650.
Prerequisites: MENT 5138 or 5183, either of which may be taken concurrently; open to MBA students, others with consent.
MENT 5674: Negotiation Strategies
Effective negotiations skills are essential for successful managers in complex contemporary organizations characterized by changing structures, temporary task forces, multiple demands on resources, and the increased importance of interdepartmental cooperation. Critical negotiation situations with other organizations range from those dealing with labor unions, purchasing, mergers, acquisitions, and joint ventures. During this course, participants plan and conduct negotiations simulations and receive feedback on their performance. Formerly offered as MGMT 5674.
Prerequisites: MENT 5138 or 5183, either of which may be taken concurrently; open to MBA students, others with consent.
MENT 5675: Business Acumen and Strategic Human Resource Management
Business acumen involves understanding and managing a business situation in a manner that is likely to lead to a good outcome. Human resources managers need the capability to evaluate multiple dimensions of complex business issues and to understand their implications for a range of stakeholders. In pursuit of these objectives, the course examines the role of HRM activities in organizational strategy design and execution. Specific topics include identification of human capital as a firm resource, understanding employee value propositions and the role of human resources in creating value for customers and other stakeholders. Formerly offered as MGMT 5675.
Prerequisites: None
MENT 5680: Talent Management Through the Employee Lifecycle
One of the primary responsibilities of human resources is managing talent throughout the employee lifecycle. Talent management spans recruiting, hiring, retention, and separation and requires a keen awareness of individual and organizational issues and strategies. Topics covered include recruitment, selection, on-boarding, career planning, job/competency analysis, benefits administration, retention, retirement, voluntary and involuntary separation, and downsizing. Formerly offered as MGMT 5680.
Prerequisites: None
MARKETING
MKTG 5115: Marketing Management
A strategic and analytical approach to marketing decisions. Students will develop basic proficiency with key marketing concepts and skills including: identifying opportunities and threats in the market environment; forecasting market growth; evaluating customers and competitors; segmenting, targeting, and positioning; determining product, price, place and promotion components of marketing strategies; and assessing marketing performance.
Prerequisites: Open only to MBA students, others with consent. Not open to students who have passed MKTG 5182.
MKTG 5220: Big Data and Strategic Marketing
Offers students the tools to analyze "big" data, to identify patterns that have actionable marketing value. Students will gain hands-on exposure to advanced analytical tools such as neural networks, market basket analysis, sequence detection, text mining, and use of state-of-the-art business modeling software to apply course concepts. Applications include financial services, retail, advertising, insurance, health care and human resources. Directed at students preparing for positions in digital analytics, digital marketing, marketing research, and consulting.
Prerequisites: MKTG 5115, or MKTG 5181 and 5182; OPIM 5103 or 5603, or BADM 5103, or BADM 5180 and 5181.
MKTG 5250: Marketing Research and Intelligence
Introduction to methods and techniques used to gain customer and market insights through marketing research. Students will learn how to identify the most appropriate research techniques to answer particular marketing questions, how to design studies for maximum insight, and how to analyze and critically read results. Qualitative and quantitative approaches will be covered. Directed at students preparing for positions in marketing research, digital analytics, consulting, product and brand management, and product development and innovation.
Prerequisites: MKTG 5115, or MKTG 5181 and 5182; OPIM 5103 or 5603, or BADM 5103, or BADM 5180 and 5181.
MKTG 5251: Marketing and Digital Analytics
Examines how analytic techniques can be used to transform marketing data into strategic insights for decision making. Students gain hands-on experience designing, conducting, and communicating results of marketing analytics on topics such as evaluating markets, discovering customer needs, satisfying customer preferences, predicting customer behavior, and allocating media and communication resources. Directed at students preparing for positions in digital analytics, digital marketing, consulting, product and brand management, customer relationship management, media and communications.
Prerequisites: MKTG 5115, or MKTG 5181 and 5182; OPIM 5103 or 5603, or BADM 5103, or BADM 5180 and 5181.
MKTG 5665: Pricing and Revenue Management
The strategy and tactics of pricing, with an emphasis on revenue management. Students will learn how to develop dynamic pricing strategies that account for changing customer demand, differences across customer segments and markets, competitive pricing strategies, and the role of pricing in loyalty programs, managing product inventory, and price-quality relationships. Students use analytic tools to apply course concepts to revenue management. Directed at students preparing for positions in product and brand management, digital analytics, and consulting.
Prerequisites: MKTG 5115, or MKTG 5181 AND MKTG 5182.
NATURAL RESOURSCES & THE ENVIRONMENT
NRE 5215: Introduction to Geospatial Analysis with Remote Sensing
Introduction to collecting, managing, displaying, and analyzing geospatial data. Topics include coordinate systems, finding and using existing sources of geospatial data, analysis of vector and raster data, creating geospatial data with remote sensing, concepts of Global Positioning System (GPS), topographic and landscape analyses, and spatial interpolation.
Prerequisites: None
NRE 5235: Remote Sensing Image Processing
Coming Soon
Prerequisites: Coming Soon
NRE 5525: Remote Sensing of the Environment
Introduction to remote sensing theory and practice. Includes electromagnetic radiation, spectral reflectance, earth observation platforms and sensors, image processing methods, and multidisciplinary applications.
Prerequisites: Instructor consent.
NRE 5545: Quantitative Remote Sensing Methods
Quantitative remote sensing methods for solving real-world problems, and methods for quantitative analysis of remotely sensed imagery plus various remote sensing applications.
Prerequisites: A course in remote sensing image processing.
NRE 5560: High Resolution Remote Sensing: Applications of UAS and LiDAR
Introduction to high-resolution remote sensing data and collection platforms. The first half of the course focuses on unmanned aerial systems (UAS) including operations, data collection, and post-processing of acquired data. Topics include laws, safety, and ethical considerations; mission planning, sensor selection, and photogrammetric processing of the collected data in commercial software. The second half of the course focuses on the fundamentals of light detection and ranging (LiDAR) and applications of LiDAR in mapping and environmental analysis. Topics include LiDAR point-cloud visualization and interpretation, creation of digital elevation and surface models, and feature extraction using ArcGIS and LAS Tools.
Prerequisites: None
NRE 5585: Python Scripting for Geospatial Analysis
GIS scripting techniques in Python for geospatial analyses, enabling students to pursue integrated research in earth resources data geoprocessing applications.
Prerequisites: Instructor consent. Recommended preparation: NRE 5215 or equivalent.
OPERATIONS & INFORMATION MANAGAMENT
OPIM 5501: Visual Analytics
Explores techniques and best practices in visualizing data. From simple cross tabs to more complex multi-dimensional analysis, explores why particular data visualizations can better illustrate patterns and correlations inherent in the data itself. Examines cognitive function and its role in data visualization designs; showing that data visualization can reveal answers and questions alike. Utilizing state of the art software, the use of parameters, filters, calculated variables, color, space and motion to visually articulate the data are surveyed. The use of dashboards to quickly reveal data-driven information that has daily relevance to executives, managers, supervisors and line personnel are investigated. Common pitfalls in visualization design and why less is often more are considered.
Prerequisites: None
OPIM 5502: Big Data Analytics with Cloud Computing
In-depth, hands-on exploration of various cutting-edge information technologies used for big data analytics. The first half focuses on using big data management techniques for ETL (extract-transform-load) operations. The second half focuses on using big data analytics tools for data mining algorithms such as classification, clustering, and collaborative filtering. Extremely hands-on, requiring students to spend significant time working with large datasets. Students are expected to have taken at least one course in data modeling and one course in data mining (please see pre-requisites) or have significant related work experience. Students should expect to become familiar with the Unix operating system, as well as data programming concepts. Students may be required to install some software on their computers on their own, with very little support, if any, from the instructor or anyone else. Students should be willing to troubleshoot any issues during installation, drawing help from Google searches.
Prerequisites: OPIM 5604 or BADM 5604; and OPIM 5272.
OPIM 5504: Adaptive Business Intelligence
The use of techniques from statistics and optimization to implement adaptive business intelligence (ABI) decision support systems. The course will introduce students to the different components of ABI systems as well as to the fundamentals of adaptive statistical methods, simulation adaptive methods, and evolutionary algorithms. Applications to diverse management contexts evolving in time will also be discussed.
Prerequisites: OPIM 5603; open only to MBA and MSBAPM students, others with consent.
OPIM 5508: Healthcare Analytics and Research Methods
Evidence-based practice, research techniques, health data collection devices, legislation and regulation of health data, ethical use of health data, and reporting tools. Prepares students for employment opportunities within a clinical or medical research environment.
Prerequisites: BADM 5103 or BADM 5180 or OPIM 5103 or OPIM 5603; open only to MBA and MSBAPM students, others with consent. Not open for credit to students who have passed OPIM 5894 when offered as Healthcare Analytics.
OPIM 5509: Introduction to Deep Learning
Introduction to topics related to deep learning and will build on your previous experience in predictive analytics. Use of neural networks for a host of data and applications - including time series data, text data, geospatial data, and image data.
Prerequisites: OPIM 5512 and 5604; open only to MBA, MSBAPM, and MS FinTech students, others with consent. Not open to students who have passed OPIM 5894 when offered as Introduction to Deep Learning.
OPIM 5510: Web Analytics
Introduction to key concepts, techniques, and tools for analyzing web data to derive actionable customer intelligence, develop digital marketing strategies and evaluate their impacts. Clickstream tracking, search engine analytics, digital experiments, and social analytics.
Prerequisites: OPIM 5604; open only to MBA and MSBAPM students, others with consent. Not open for credits who have passed OPIM 5894 when offered as Web Analytics.
OPIM 5511: Survival Analysis with SAS
Describes the various methods used for modeling and evaluating survival data, also called time-to-event data. General statistical concepts and techniques, including survival and hazard functions, Kaplan-Meier graphs, log-rank, and related tests, Cox proportional hazards model, and the extended Cox model for time-varying covariates and non-proportional hazards.
Prerequisites: OPIM 5604; open only to MBA and MSBAPM students, others with consent. Not open for credits who have passed OPIM 5894 when offered as Survival Analysis using SAS.
OPIM 5512: Data Science Using Python
Data science concepts using the Python programming language. Data wrangling and management using Pandas; visualization using MatPlotLib; fundamentals of matrix algebra and regression, with illustrations using Numpy; machine learning, focusing on fundamental concepts, classification, and information extraction.
Prerequisites: OPIM 5604; MBA, MSBAPM, and MS FinTech students, others with consent. Recommended preparation: Students are expected to know the fundamentals of Python programming language (or another language) through self-study, previous coursework or previous work experience, including topics such as loops, functions, and data structures. Not open to students who have passed OPIM 5894 when offered as Data Science with Python.
OPIM 5604: Predictive Modeling
Introduces the techniques of predictive modeling in a data-rich business environment. Covers the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement predictive models for a variety of practical business applications. Predictive models such as neural networks, decision trees, Bayesian classification, and others will be studied. The course emphasizes the relationship of each step to a company's specific business needs, goals and objectives. The focus on the business goal highlights how the process is both powerful and practical.
Prerequisites: Open only to MBA, MSBAPM, MS Data Science and MS FinTech students, others with consent. Corequisite: OPIM 5603.
STATISTICS
STAT 5125: Statistical Computing for Data Science
Principles and practice of statistical computing in data science: data structure, data programming, data visualization, simulation, resampling methods, distributed computing, and project management tools.
Prerequisites: Introductory course in mathematical and applied statistics; introductory course in programming. Instructor consent required.
STAT 5405: Applied Statistics for Data Science
Statistics essential for data science incorporating descriptive statistics; integrative numerical description and visualization of data; graphical methods for determining and comparing distributions of data; data-driven statistical inference of one-sample, two-sample, and k-sample problems; linear regression; and non-linear regression.
Prerequisites: Instructor consent and introductory course in mathematical statistics and regression analysis. Not open to students who have passed STAT 5505 or STAT 5605 or BIST 5505 or BIST 5605.
STAT 5825: Applied Time Series
Introduction to prediction using time-series regression methods with non-seasonal and seasonal data. Smoothing methods for forecasting. Modeling and forecasting using univariate autoregressive moving average models.
Prerequisites: Open to graduate students in Statistics, others with permission.
STAT 5415: Statistical Methods for Data Science
Basic probabilistic concepts; marginal, joint and conditional probability distributions; point and interval estimation; and hypothesis testing.
Prerequisites: Differential calculus; introductory course in statistics; and Instructor consent. Not open to students who have passed STAT 5585 or STAT 5685 or BIST 5585 or BIST 5685.
STAT 5665: Applied Multivariate Analysis
Multivariate normal distributions, inference about a mean vector, comparison of several multivariate means, principal components, factor analysis, canonical correlation analysis, discrimination and classification, cluster analysis.
Prerequisites: Open to graduate students in Statistics, others with permission.
STAT 5675: Bayesian Data Analysis
Theory of statistical inference based on Bayes’ Theorem: basic probability theory, linear/nonlinear, graphical, and hierarchical models, decision theory, Bayes estimation and hypothesis testing, prior elicitation, Gibbs sampling, the Metropolis-Hastings algorithm, Monte Carlo integration.
Prerequisites: STAT 5585 and STAT 5685, or instructor consent.
STAT 5825: Applied Time Series
Introduction to prediction using time-series regression methods with non-seasonal and seasonal data. Smoothing methods for forecasting. Modeling and forecasting using univariate autoregressive moving average models.
Prerequisites: Open to graduate students in Statistics, others with permission.
STAT 5845: Applied Spatio-Temporal Statistics
Applied statistical methodology and computing for spatio-temporal data, including visualization, models, and inferences. Extreme value analysis in spatio-temporal contexts will be a module. Focus will be on models that account for spatio-temporal dependence and inferences that provide appropriate uncertainty measures, with applications to real-world problems using open-source software.
Prerequisites: Open to graduate students in Statistics; others with per-mission.
Recommended preparation: STAT 5405 or 5605 or GEOG 5600 or 5610 or ERTH 5150 or equivalent.
STAT 5915: Statistical Data Science in Action
Real-world statistical data science practice: problem formulation; integration of statistics, computing, and domain knowledge; collaboration; communication; reproducibility; project management.
Prerequisites: STAT 5405 or instructor consent.
MS in Data Science Program Curriculum
CORE COURSES
GRAD 5100: Fundamentals of Data Science
Establishes background knowledge in Python and R programming, multivariate calculus and linear algebra, and basic statistics to prepare incoming students for success in the core courses in the MS in Data Science Program.
Prerequisites: Open only to students in the Data Science M.S. program.
STAT 5405: Applied Statistics for Data Science
Statistics essential for data science incorporating descriptive statistics; integrative numerical description and visualization of data; graphical methods for determining and comparing distributions of data; data-driven statistical inference of one-sample, two-sample, and k-sample problems; linear regression; and non-linear regression.
Prerequisites: Instructor consent and introductory course in mathematical statistics and regression analysis. Not open to students who have passed STAT 5505 or STAT 5605 or BIST 5505 or BIST 5605.
STAT 5125: Statistical Computing for Data Science
Principles and practice of statistical computing in data science: data structure, data programming, data visualization, simulation, resampling methods, distributed computing, and project management tools.
Prerequisites: Introductory course in mathematical and applied statistics; introductory course in programming. Instructor consent required.
CSE 5819: Introduction to Machine Learning
An introduction to the basic tools and techniques of machine learning, including models for both supervised and unsupervised learning, related optimization techniques, and methods for model validation. Topics include linear and logistic regression, SVM classification and regression, kernels, regularization, clustering, and on-line algorithms for regret minimization.
Prerequisites: Department consent required; open to graduate students in the Computer Science and Engineering program, others with permission. Recommended preparation: MATH 2210Q; STAT 3025, or 3345, or 3375, or MATH 3160; CSE 3500.
CSE 5713: Data Mining and Management
Introduction to data mining algorithms and their analysis. Application of and experimentation with data mining algorithms on real-world problems and domains, with a dual focus on addressing the solution quality issue and the time efficiency issue.
Prerequisites: Open to graduate students in the CSE program, others with consent.
EPSY 5641: Research Design and Measurement for Data Science
Research design, ethical and measurement issues as they relate to data science. Measurement topics include: Design of surveys and survey instruments, reliability, validity and generalizability theory. Research design topics include: AB designs, clustering and the identification of internal and external validity threats. Open and reproducible science and ethical conduct of research are themes throughout the course.
Prerequisites: Open to students enrolled in the M.S. Data Science program or with instructor consent. Recommended preparation: Knowledge of Introductory inferential and descriptive statistics.
ARE 5353: Data Ethics and Equity in the Era of Misinformation
This course will introduce students to issues of ethics and equity in the contemporary practice of data science. The ability to collect, store, process, and analyze ever greater amounts of data offers great opportunities, as well as potential perils. This course will provide examine the ethical implications of data collection, usage, and distribution. Topics will include systematic approaches to assessing ethical issues; privacy and confidentiality; defining research and the responsibilities associated with conducting ethical research; implicit and structural biases in data collection and analysis.
Prerequisites: None
OPIM 5605: Data Visualization and Communication
Data visualization is a form of storytelling that provides an effective way to draw conclusions and share insights, allowing people to express big, complex ideas in simple ways. Utilizing state of the art software, the use of parameters, filters, calculated variables, color, space and motion to visually articulate the data are surveyed. Common pitfalls and ethics issues in visualization design are also considered. This interactive course is designed to help students learn the methods, tools, and techniques to best understand and present complex data so that they can persuasively share results and influence decisions.
Prerequisites: Open only to M.S. Data Science students; others with consent.
GRAD 5800: Applied Capstone in Data Science
This course will give M.S. in Data Science students a problem-based learning opportunity to apply knowledge and skills gained in core and elective courses to an integrative data science project. Projects for the course come from industry and program partners and will typically require students to work in small teams to solve a real-world data science problem.
Prerequisites: Enrollment in the M.S. in Data Science program; Department or Unit consent required. Recommended preparation: Nine credits of coursework required for the M.S. in Data Science.
ELECTIVES
Electives are chosen based upon a student's academic background and interests. UConn’s MS in Data Science offers a wide range of electives across the University. Below are some possibilities for full-time students depending on required prerequisite courses and availability. Students are required to take 2 electives, 6 credits, to complete their degree.
BIOSTATISTICS
BIST 5615: Categorical Data Analysis
Statistical analysis of data on a nominal scale: discrete distributions, contingency tables, odds ratios, interval estimates, goodness of fit tests, logistic/probit/complementary log-log regression, Poisson-related regression.
Prerequisites: STAT 5405 or BIST 5505 and 5605, or instructor consent.
BIST 5625: Introduction to Biostatistics
Rates and proportions, sensitivity, specificity, two-way tables, odds ratios, relative risk, ordered and non-ordered classifications, rends, case-control studies, elements of regression including logistic and Poisson, additivity and interaction, combination of studies and meta-analysis.
Prerequisites: Open to graduate students in Biostatistics, others with permission.
BIST 5645: Concepts and Analysis of Survival Data
Survival models, censoring and truncation, nonparametric estimation of survival functions, comparison of treatment groups, mathematical and graphical methods for assessing goodness of fit, parametric and nonparametric regression models.
Prerequisites: Open to graduate students in Biostatistics, others with permission.
BIST 5815: Longitudinal Data Analysis
Statistical theory and methodology for data collected over time in a clustered manner: design of experiments, exploratory data analysis, linear models for continuous data, general linear models for discrete data, marginal and mixed models, treatment of missing data.
Prerequisites: BIST 5505 and 5605; or instructor consent.
COMPUTER SCIENCE & ENGINEERING
CSE 5299: Computer Networks and Data Communication
Introduction to computer networks and data communications. Network types, components and topology, protocol architecture, routing algorithms, and performance. Case studies including LAN and other architectures.
Prerequisites: Department consent; open to graduate students in the Computer Science and Engineering program, others with permission. Not open for credit to students who have passed CSE 3300. Recommended preparation: CSE 2304 or 3666.
CSE 5300: Advanced Computer Networks
Advanced fundamental principles of computer networks. Topics include network design and optimization, protocol design and implementation, network algorithms, advanced network architectures, network simulation, performance evaluation, and network measurement.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3300 or the equivalent. This course and ECE 6431 may not both be taken for credit.
CSE 5304: High-Performance Parallel Computing
Topics in high-performance computing such as the following (1) Parallel Algorithms and Parallel Computation: including programming models (Circuits and PRAMs), complexity analysis, modern parallel platforms and programming libraries; (2) Shared- and Distributed-Memory Parallel Architectures: including cache coherence, Memory consistency, processor synchronization, latency tolerance and hiding; (3) Interconnection Networks: including quantitative measures, topologies, switch architectures, routing strategies; as well as (4) Contemporary and Future HPC Systems.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 4302 and 4500.
CSE 5309: Networked Embedded Systems
Introduction to the design, analysis and implementation of networked embedded systems that interact with the physical environment. Applications of such systems include environmental monitoring, consumer electronics, medical devices, automotive systems, industrial process control, distributed robotics, and smart structures. Topics covered include concepts, technologies and protocols for low-power and resource-restricted wireless networks; models of computation and physical systems; embedded system architectures; and real-time system concepts, theory and design principles.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 2300W, CSE 3300 and CSE 3666.
CSE 5800: Bioinformatics
Advanced mathematical models and computational techniques in bioinformatics. Topics covered include genome mapping and sequencing, sequence alignment, database search, gene prediction, genome rearrangements, phylogenetic trees, and computational proteomics.
Prerequisites: Open to graduate students in the CSE program, others with consent.
CSE 5815: Systems Biology: Constructing Biological Knowledgebase
Design and architecture of biological knowledge base. Topics covered include biological/biomedical data modeling, knowledge representation techniques of biological and biomedical information, review of existing inference methods, methods of assessing evidence quality, design of inference-enabling genomics annotation system, various meta-data analysis methods involving genomics and biomedical data.
Prerequisites: Open to graduate students in the CSE program, others with consent.
CSE 5840: String Algorithms and Applications in Bioinformatics
Classic string matching algorithms (e.g. Knuth-Morris-Pratt, Karp-Rabin, suffix tree and arrays) and more advanced string algorithms (e.g. Burrows-Wheeler transform). With a particular focus on rigorous treatment of string processing algorithms and their analysis. Applications of string algorithms to bioinformatics problems. Students are expected to have basic prior knowledge of algorithm design and analysis.
Prerequisites: Open to graduate students in the CSE program, others with consent.
CSE 5850: Introduction to Cyber-Security
Introductory to the area of cyber-security. The course focuses on applied cryptography, and some of its applications and related areas in cyber security, including network and web security, usable security, privacy/anonymity, and block-chains. The course is systems-oriented; we will discuss many practical vulnerabilities, attacks and defenses. However, esp. in the beginning, we will also learn some theory - mainly, few definitions, and (fewer) proofs.
Prerequisites: Department consent; open to graduate students in the Computer Science and Engineering program, others with permission. Not open for credit to students who have passed CSE 3400. Recommended preparation: CSE 2500.
CSE 5852: Modern Cryptography: Foundations
The foundations of modern cryptography introducing basic topics such as one-way functions, pseudorandom generators, and computational hardness assumptions based on number theory. Fundamental cryptographic constructions such as hard-core predicates, secure symmetric encryption and message-authentication codes, and public-key cryptography.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: MATH 3160, CSE 3500, and CSE 3502 or the equivalent.
CSE 5854: Modern Cryptography: Primitives and Protocols
Modern cryptography, emphasizing provable security and concrete constructions based on the hardness of specific computational problems. After surveying some basic cryptographic primitives and associated number-theoretic constructions, focuses on public-key infrastructure and protocols: it will cover such topics as digital signatures, identification and key-exchange schemes, distributed key generation, blind signatures, zero-knowledge proofs, and private function computation.
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: MATH 3160, CSE 3500, CSE 3502, and CSE 4702 or the equivalent.
CSE 5860: Computational Problems in Evolutionary Genomics
Computational and algorithmic approaches for problems arising in evolutionary genomics. Topics may include phylogenetic trees inference, population evolutionary models and theory, understanding complex evolutionary processes and other related topics. Both combinatorial optimization and stochastic approaches will be covered.
Prerequisites: CSE 5800; open to graduate students in the CSE program, others with consent.
EDUCATIONAL PSYCHOLOGY
EPSY 5643: Text Analytics
This course provides an applied introduction to text analytics with special emphasis on its application to education. Students will learn to use common toolkits in the Python ecosystem to analyze large-scale text data in order to generate insights into educational, cognitive, and social processes.
Prerequisites: EPSY 5641. Recommended preparation: This course requires an understanding of introductory statistics and regression at the level of EPSY 5605 and EPSY 5610 as well as some prior experience with statistical programming in a language like R or Python.
EPSY 6611: Hierarchical Linear Modeling
Theory and applications of hierarchical linear modeling, including organizational and longitudinal multilevel models.
Prerequisites: None
EPSY 6615: Structural Equation Modeling
An introduction to structural equation modeling. Develop, modify, and interpret a variety of structural equation models commonly used in social science research. Linear models with only observed variables (path analysis), latent variable models without causal paths (confirmatory factor analysis), and latent variable models with causal paths (structural equation modeling). Conceptual understanding, application, and interpretation of structural equation models.
Prerequisites: None
HEALTHCARE MANAGEMENT & INSURANCE
HCMI 5240: Health Care Organization and Management
Examines the nation's healthcare delivery system with overviews provided for each major sector of the health economy. The basic tools of economics and finance are employed to gain critical insights into the structure, conduct and performance of each of these sectors. Designed to accommodate both health care professionals and individuals from other business areas interested in learning more about the health care industry.
Prerequisites: Open to MBA students and others with consent.
HCMI 5243: Health Care Economics
Demonstrates how various economic theories can be used to think about health care issues and takes a macro or industry perspective of various health care problems and policy questions. Students are provided with a set of economic tools to evaluate a theoretical or empirical argument relating to health or medical care. Culminates with an in-depth analysis of the structure, conduct, and performance of the markets for medical insurance, physician services, hospital services, pharmaceutical products, and long-term care. Health care reform is also discussed.
Prerequisites: HCMI 5240 or instructor consent.
HCMI 5686: Health Insurance and Risk Management
A detailed overview of the purpose, structure, operation, and performance of the health insurance industry from the perspective of various stakeholders including insurance company owners, employers, individual consumers of health insurance services, and society. Emphasis is placed on individual and group health insurance products with respect to administration, selling and marketing, underwriting, pricing, and claims administration. Managed-care techniques, benefit-package design, and cost-sharing mechanisms are also evaluated and discussed.
Prerequisites: HCMI 5240 or instructor consent.
MANAGEMENT & ENTREPRENUERSHIP
MENT 5377: Human Resource Metrics and Talent Analytics
Creating and managing appropriate metrics is vital to enabling the development of high-achieving people in organizations and maintaining an effective human resource function. Introduces techniques for developing effective metrics and identifies connections between human resource metrics and other performance measurement systems commonly used in organizations. Introduces students to talent analytics, the tools and techniques managers use to mine organizational data in pursuit of actionable knowledge. Students learn how to structure research questions, communicate data needs to technical specialists, and interpret data to yield organizational insights and support effective decisions. Formerly offered as MGMT 5377.
Prerequisites: None
MENT 5650: Interpersonal Relations, Influence, and Ethical Leadership
Communication challenges and difficult conversations faced by business professionals. Emphasizes core values associated with ethical leadership in the professional world with a particular focus on the connections between applied ethics and management issues. Topics include conflict resolution styles and models, negotiation, organizational politics, influencing processes, the language of leadership, and models for examination and resolution of ethical workplace dilemmas. Formerly offered as MGMT 5650.
Prerequisites: MENT 5138 or 5183, either of which may be taken concurrently; open to MBA students, others with consent.
MENT 5674: Negotiation Strategies
Effective negotiations skills are essential for successful managers in complex contemporary organizations characterized by changing structures, temporary task forces, multiple demands on resources, and the increased importance of interdepartmental cooperation. Critical negotiation situations with other organizations range from those dealing with labor unions, purchasing, mergers, acquisitions, and joint ventures. During this course, participants plan and conduct negotiations simulations and receive feedback on their performance. Formerly offered as MGMT 5674.
Prerequisites: MENT 5138 or 5183, either of which may be taken concurrently; open to MBA students, others with consent.
MENT 5675: Business Acumen and Strategic Human Resource Management
Business acumen involves understanding and managing a business situation in a manner that is likely to lead to a good outcome. Human resources managers need the capability to evaluate multiple dimensions of complex business issues and to understand their implications for a range of stakeholders. In pursuit of these objectives, the course examines the role of HRM activities in organizational strategy design and execution. Specific topics include identification of human capital as a firm resource, understanding employee value propositions and the role of human resources in creating value for customers and other stakeholders. Formerly offered as MGMT 5675.
Prerequisites: None
MENT 5680: Talent Management Through the Employee Lifecycle
One of the primary responsibilities of human resources is managing talent throughout the employee lifecycle. Talent management spans recruiting, hiring, retention, and separation and requires a keen awareness of individual and organizational issues and strategies. Topics covered include recruitment, selection, on-boarding, career planning, job/competency analysis, benefits administration, retention, retirement, voluntary and involuntary separation, and downsizing. Formerly offered as MGMT 5680.
Prerequisites: None
MARKETING
MKTG 5115: Marketing Management
A strategic and analytical approach to marketing decisions. Students will develop basic proficiency with key marketing concepts and skills including: identifying opportunities and threats in the market environment; forecasting market growth; evaluating customers and competitors; segmenting, targeting, and positioning; determining product, price, place and promotion components of marketing strategies; and assessing marketing performance.
Prerequisites: Open only to MBA students, others with consent. Not open to students who have passed MKTG 5182.
MKTG 5220: Big Data and Strategic Marketing
Offers students the tools to analyze "big" data, to identify patterns that have actionable marketing value. Students will gain hands-on exposure to advanced analytical tools such as neural networks, market basket analysis, sequence detection, text mining, and use of state-of-the-art business modeling software to apply course concepts. Applications include financial services, retail, advertising, insurance, health care and human resources. Directed at students preparing for positions in digital analytics, digital marketing, marketing research, and consulting.
Prerequisites: MKTG 5115, or MKTG 5181 and 5182; OPIM 5103 or 5603, or BADM 5103, or BADM 5180 and 5181.
MKTG 5250: Marketing Research and Intelligence
Introduction to methods and techniques used to gain customer and market insights through marketing research. Students will learn how to identify the most appropriate research techniques to answer particular marketing questions, how to design studies for maximum insight, and how to analyze and critically read results. Qualitative and quantitative approaches will be covered. Directed at students preparing for positions in marketing research, digital analytics, consulting, product and brand management, and product development and innovation.
Prerequisites: MKTG 5115, or MKTG 5181 and 5182; OPIM 5103 or 5603, or BADM 5103, or BADM 5180 and 5181.
MKTG 5251: Marketing and Digital Analytics
Examines how analytic techniques can be used to transform marketing data into strategic insights for decision making. Students gain hands-on experience designing, conducting, and communicating results of marketing analytics on topics such as evaluating markets, discovering customer needs, satisfying customer preferences, predicting customer behavior, and allocating media and communication resources. Directed at students preparing for positions in digital analytics, digital marketing, consulting, product and brand management, customer relationship management, media and communications.
Prerequisites: MKTG 5115, or MKTG 5181 and 5182; OPIM 5103 or 5603, or BADM 5103, or BADM 5180 and 5181.
MKTG 5665: Pricing and Revenue Management
The strategy and tactics of pricing, with an emphasis on revenue management. Students will learn how to develop dynamic pricing strategies that account for changing customer demand, differences across customer segments and markets, competitive pricing strategies, and the role of pricing in loyalty programs, managing product inventory, and price-quality relationships. Students use analytic tools to apply course concepts to revenue management. Directed at students preparing for positions in product and brand management, digital analytics, and consulting.
Prerequisites: MKTG 5115, or MKTG 5181 AND MKTG 5182.
NATURAL RESOURSCES & THE ENVIRONMENT
NRE 5215: Introduction to Geospatial Analysis with Remote Sensing
Introduction to collecting, managing, displaying, and analyzing geospatial data. Topics include coordinate systems, finding and using existing sources of geospatial data, analysis of vector and raster data, creating geospatial data with remote sensing, concepts of Global Positioning System (GPS), topographic and landscape analyses, and spatial interpolation.
Prerequisites: None
NRE 5235: Remote Sensing Image Processing
Coming Soon
Prerequisites: Coming Soon
NRE 5525: Remote Sensing of the Environment
Introduction to remote sensing theory and practice. Includes electromagnetic radiation, spectral reflectance, earth observation platforms and sensors, image processing methods, and multidisciplinary applications.
Prerequisites: Instructor consent.
NRE 5545: Quantitative Remote Sensing Methods
Quantitative remote sensing methods for solving real-world problems, and methods for quantitative analysis of remotely sensed imagery plus various remote sensing applications.
Prerequisites: A course in remote sensing image processing.
NRE 5560: High Resolution Remote Sensing: Applications of UAS and LiDAR
Introduction to high-resolution remote sensing data and collection platforms. The first half of the course focuses on unmanned aerial systems (UAS) including operations, data collection, and post-processing of acquired data. Topics include laws, safety, and ethical considerations; mission planning, sensor selection, and photogrammetric processing of the collected data in commercial software. The second half of the course focuses on the fundamentals of light detection and ranging (LiDAR) and applications of LiDAR in mapping and environmental analysis. Topics include LiDAR point-cloud visualization and interpretation, creation of digital elevation and surface models, and feature extraction using ArcGIS and LAS Tools.
Prerequisites: None
NRE 5585: Python Scripting for Geospatial Analysis
GIS scripting techniques in Python for geospatial analyses, enabling students to pursue integrated research in earth resources data geoprocessing applications.
Prerequisites: Instructor consent. Recommended preparation: NRE 5215 or equivalent.
OPERATIONS & INFORMATION MANAGAMENT
OPIM 5501: Visual Analytics
Explores techniques and best practices in visualizing data. From simple cross tabs to more complex multi-dimensional analysis, explores why particular data visualizations can better illustrate patterns and correlations inherent in the data itself. Examines cognitive function and its role in data visualization designs; showing that data visualization can reveal answers and questions alike. Utilizing state of the art software, the use of parameters, filters, calculated variables, color, space and motion to visually articulate the data are surveyed. The use of dashboards to quickly reveal data-driven information that has daily relevance to executives, managers, supervisors and line personnel are investigated. Common pitfalls in visualization design and why less is often more are considered.
Prerequisites: None
OPIM 5502: Big Data Analytics with Cloud Computing
In-depth, hands-on exploration of various cutting-edge information technologies used for big data analytics. The first half focuses on using big data management techniques for ETL (extract-transform-load) operations. The second half focuses on using big data analytics tools for data mining algorithms such as classification, clustering, and collaborative filtering. Extremely hands-on, requiring students to spend significant time working with large datasets. Students are expected to have taken at least one course in data modeling and one course in data mining (please see pre-requisites) or have significant related work experience. Students should expect to become familiar with the Unix operating system, as well as data programming concepts. Students may be required to install some software on their computers on their own, with very little support, if any, from the instructor or anyone else. Students should be willing to troubleshoot any issues during installation, drawing help from Google searches.
Prerequisites: OPIM 5604 or BADM 5604; and OPIM 5272.
OPIM 5504: Adaptive Business Intelligence
The use of techniques from statistics and optimization to implement adaptive business intelligence (ABI) decision support systems. The course will introduce students to the different components of ABI systems as well as to the fundamentals of adaptive statistical methods, simulation adaptive methods, and evolutionary algorithms. Applications to diverse management contexts evolving in time will also be discussed.
Prerequisites: OPIM 5603; open only to MBA and MSBAPM students, others with consent.
OPIM 5508: Healthcare Analytics and Research Methods
Evidence-based practice, research techniques, health data collection devices, legislation and regulation of health data, ethical use of health data, and reporting tools. Prepares students for employment opportunities within a clinical or medical research environment.
Prerequisites: BADM 5103 or BADM 5180 or OPIM 5103 or OPIM 5603; open only to MBA and MSBAPM students, others with consent. Not open for credit to students who have passed OPIM 5894 when offered as Healthcare Analytics.
OPIM 5509: Introduction to Deep Learning
Introduction to topics related to deep learning and will build on your previous experience in predictive analytics. Use of neural networks for a host of data and applications - including time series data, text data, geospatial data, and image data.
Prerequisites: OPIM 5512 and 5604; open only to MBA, MSBAPM, and MS FinTech students, others with consent. Not open to students who have passed OPIM 5894 when offered as Introduction to Deep Learning.
OPIM 5510: Web Analytics
Introduction to key concepts, techniques, and tools for analyzing web data to derive actionable customer intelligence, develop digital marketing strategies and evaluate their impacts. Clickstream tracking, search engine analytics, digital experiments, and social analytics.
Prerequisites: OPIM 5604; open only to MBA and MSBAPM students, others with consent. Not open for credits who have passed OPIM 5894 when offered as Web Analytics.
OPIM 5511: Survival Analysis with SAS
Describes the various methods used for modeling and evaluating survival data, also called time-to-event data. General statistical concepts and techniques, including survival and hazard functions, Kaplan-Meier graphs, log-rank, and related tests, Cox proportional hazards model, and the extended Cox model for time-varying covariates and non-proportional hazards.
Prerequisites: OPIM 5604; open only to MBA and MSBAPM students, others with consent. Not open for credits who have passed OPIM 5894 when offered as Survival Analysis using SAS.
OPIM 5512: Data Science Using Python
Data science concepts using the Python programming language. Data wrangling and management using Pandas; visualization using MatPlotLib; fundamentals of matrix algebra and regression, with illustrations using Numpy; machine learning, focusing on fundamental concepts, classification, and information extraction.
Prerequisites: OPIM 5604; MBA, MSBAPM, and MS FinTech students, others with consent. Recommended preparation: Students are expected to know the fundamentals of Python programming language (or another language) through self-study, previous coursework or previous work experience, including topics such as loops, functions, and data structures. Not open to students who have passed OPIM 5894 when offered as Data Science with Python.
OPIM 5604: Predictive Modeling
Introduces the techniques of predictive modeling in a data-rich business environment. Covers the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement predictive models for a variety of practical business applications. Predictive models such as neural networks, decision trees, Bayesian classification, and others will be studied. The course emphasizes the relationship of each step to a company's specific business needs, goals and objectives. The focus on the business goal highlights how the process is both powerful and practical.
Prerequisites: Open only to MBA, MSBAPM, MS Data Science and MS FinTech students, others with consent. Corequisite: OPIM 5603.
STATISTICS
STAT 5825: Applied Time Series
Introduction to prediction using time-series regression methods with non-seasonal and seasonal data. Smoothing methods for forecasting. Modeling and forecasting using univariate autoregressive moving average models.
Prerequisites: Open to graduate students in Statistics, others with permission.
STAT 5665: Applied Multivariate Analysis
Multivariate normal distributions, inference about a mean vector, comparison of several multivariate means, principal components, factor analysis, canonical correlation analysis, discrimination and classification, cluster analysis.
Prerequisites: Open to graduate students in Statistics, others with permission.
STAT 5675: Bayesian Data Analysis
Theory of statistical inference based on Bayes’ Theorem: basic probability theory, linear/nonlinear, graphical, and hierarchical models, decision theory, Bayes estimation and hypothesis testing, prior elicitation, Gibbs sampling, the Metropolis-Hastings algorithm, Monte Carlo integration.
Prerequisites: STAT 5585 and STAT 5685, or instructor consent.
STAT 5845: Applied Spatio-Temporal Statistics
Applied statistical methodology and computing for spatio-temporal data, including visualization, models, and inferences. Extreme value analysis in spatio-temporal contexts will be a module. Focus will be on models that account for spatio-temporal dependence and inferences that provide appropriate uncertainty measures, with applications to real-world problems using open-source software.
Prerequisites: Open to graduate students in Statistics; others with per-mission.
Recommended preparation: STAT 5405 or 5605 or GEOG 5600 or 5610 or ERTH 5150 or equivalent.