Explore data science and analytics offerings at Ohio State

Students learn about information visualization techniques that help people analyze massive amounts of digital data to combat overload and aid sensemaking with applications in retail and financial decision making, logistics, information systems, manufacturing, healthcare, energy and smart grids, cybersecurity and social networks.

The place of the city in social organization; the emergence, nature, and problems of modern urbanism; projects based in census and field data.  

Methods of collecting and analyzing transportation data with empirical applications. Prereq: 3700 (570), or Grad standing in CivilEn, or permission of instructor. Not open to students with credit for 672.

In this course, students will analyze data for the purpose of planning transportation infrastructure. Students will build expertise in innovative transportation data collection (such as drones), improve familiarity with publicly available transportation data sources, develop basic R coding skills to visualize transportation data, and consider careers as transportation planning analysts. Prereq: Jr, Sr, or Grad standing.

Advanced topics in management control, strategic interactions within the firm. Students may not repeat topics.

Note: 4893 is a temporary course number. The course will be assigned a permanent course number and “Data Science” title after spring 2018.

Level: Undergraduate

Offered by: Department of Political Science, College of Arts and Sciences

Taught by: Skyler Cranmer

A revolution in data science has changed the way business, government, and the academy learn and adjust to changing environments. Companies like Facebook, Amazon, Netflix, and Google; government agencies like the CIA, NSA, and Department of Defense; as well as election forecasters like FiveThirtyEight rely on advanced data science technology to understand the vast quantities of data being produced and filter fact from noise. This new data science is a combination of the social sciences, statistics, and machine learning, and this new class is meant to provide a first introduction to the topic (no experience or pre-requisites necessary) that will culminate in a “hackathon” data project. The course is software-application focused and will tour topics such as basic design, social network analysis, text as data, machine learning, and behavioral genetics.

Applications of modern marketing research procedures to variety of marketing problems. Emphasis placed on survey design and sampling, attitude measurement, perceptual mapping, market segmentation, conjoint analysis, and measurement of customer satisfaction.

This course is designed to introduce students to commonly used software programs in data science and improve students’ problem solving skills and logical thought processes. Students will be exposed to R, SAS, and SPSS. Prereq: MBA 6271 or 6273.

Introduction to the UNIX programming environment including: shell programming (csh); regular expressions; makefiles; grep, sed, and awk programming languages. Prereq: 2122, 2123, or 2231; and 2321; and enrollment in CSE, CIS, ECE, or Data Analytics major, or CIS minor. This course is graded S/U.

Focus on the diversity of Portuguese. Examines the principal areas of linguistic variation in Portuguese and the main dialect differences between Brazil and Portugal. Students conduct basic analyses of linguistic data using computerized corpus tools for hypothesis-testing. Taught in Portuguese. Prereq: 3401 (401); or 5502 (502) and Grad standing; or permission of instructor. Not open to students with credit for Portgese 611.

Introduction to the analysis of data. Topics include sampling, data collection, probability, inference, random variables, display of data, correlation, and analysis of variance. This course does not count toward a course elective for Econ majors. Prereq: Not open to students with credit for 4400 (444), 5410 (641), 5420 (642), 443, IntStds 3400 (443), Stat 1430 (133), 1450 (145), 2450 (245) or AEDEcon 2005 (205). GE data anly course. Cross-listed in IntStds.

Introduction to the analysis of data. Topics include sampling, data collection, probability, inference, random variables, display of data, correlation, and analysis of variance. This course does not count toward a course elective for Econ majors. Prereq: Not open to students with credit for Econ 3400, 4400, 5410, 5420, Stat 1430, 1450, 2450 or AEDEcon 2005. GE data anly course. Cross-listed in Econ.

Introduction to formulation, solution and analysis of continuous and discrete linear and nonlinear models to optimize systems using data-driven techniques.

Introduction to operating system concepts: process, CPU scheduling, memory management, file system and storage, and multi-threaded programming. Prereq: 2421, or 2451 and ECE 2560; and enrollment in CSE, CIS, Data Analytics, or ECE major. Not open to students with credit for 5431.

Introduction to computer architecture at machine and assembly language level; pointers and addressing; C programming at machine level; computer organization.

Basic elements of syntactic description and an overview of syntactic structure across languages.

Addresses the use of metrics, data, and indicators to measure sustainability and track progress. The course will survey a broad range of environmental challenges and evaluate the choices confronting public and private groups. Students will use software tools to quantify sustainability measures. Prereq: 2000 or AEDEcon 2005, or other GE Data Analysis course.

Introductory course designed to teach the measurement and collection of data using traditional methods, total stations and GPS. These techniques will be integrated and applied to traversing, topographic mapping and water flow analysis. Prereq: Soph standing in Engineering. Not open to students with credit for 373.

Survey of advanced concepts, techniques, and applications of artificial intelligence, including knowledge representation, learning, natural language understanding, and vision. Prereq: 3521 or 5521, and enrollment in CSE, CIS, ECE, or Data Analytics major; or Grad standing.

Survey of basic concepts and techniques in artificial intelligence, including problem solving, knowledge representation, and machine learning.

An introduction to the application and interpretation of quantitative analysis in sociological research; emphasis on the description of social variables and hypothesis testing. Au, Sp, Su Sems. Prereq: Not open to students with credit for 549. GE data anly course.

Calculus-based introduction to the statistical analysis of biological data, including probability, common discrete and continuous distributions, experimental design, hypothesis testing, linear regression and correlation. Prereq: Math 1131, 1151 (152), 1156, 1161.XX, or 1181H, or equiv, or permission of instructor. Not open to students with credit for 2450 (245) or 218. GE data anly course.

Fundamentals of probability and statistics: Data collection and summaries, random variables, simple linear regression, two-way tables, conditional probability, sampling distributions, confidence intervals, hypothesis tests, analysis of variance. In-person recitation. Prereq: Math 1131, 1151, 1156, 1161.xx, or 1181H, or permission of instructor. Not open to students with credit for 1430, 1430.02, or BusMgt 2320. GE data anly course.

Fundamentals of probability and statistics: Data collection and summaries, random variables, simple linear regression, two-way tables, conditional probability, sampling distributions, confidence intervals, hypothesis tests, analysis of variance. On-line recitation. Prereq: Math 1131, 1151, 1156, 1161.xx, or 1181H, or permission of instructor. Not open to students with credit for 1430, 1430.01, or BusMgt 2320. GE data anyl course.

Students will learn how to work with data, formulate questions, and most importantly, learn comprehensive problem solving techniques to extract and explain pertinent information from data. The course will track the problem solving process from problem formulation to data acquisition/cleaning to data analysis and conclusion to communication of the results. Prereq: Enrollment in the SMB-A program, or permission of instructor.

Statistical models for data analysis and discovery in big-data settings. The regression methods developed in Stat 3301 are extended to data settings with binary and multi-category outcomes. An introduction to some of the most commonly used statistical methods for exploring and analyzing multivariate data is provided. Interpretation and communication of the results of analyses is emphasized.

Statistical models for data analysis and discovery in big-data settings, with primary focus on linear regression models. The challenges of building meaningful models from vast data are explored, and emphasis is placed on model building and the use of numerical and graphical diagnostics for assessing model fit. Interpretation and communication of the results of analyses is emphasized.

Introduction to construction materials and techniques.

Introduction to Mendelian principles, genetic epidemiology, and molecular genetics; family studies; model-based and model-free linkage analysis for mapping disease genes; genome wide association studies; association analysis using haplotypes. Prereq: 6301 (610) and 6302 (623), or permission of instructor.

Introduction to computational statistics. Students will learn how to manipulate data, perform statistical analyses, perform simple Monte Carlo experiments, use resampling methods and discuss the results obtained from their analyses. Prereq: 6301 (610), 6302 (623), and 6410 (641) or 6910, and 6450 (645) or 6950; or permission of instructor. Not open to students with credit for 673.

An introduction to basic data analysis methods used by economists to explain economic reasoning in the sport industry and associated markets. Prereq: 2001.01, 2001.02, 2001.03H, AEDEcon 2001, or 2001H.

This is an advanced discussion-based seminar exploring new methods, technologies, and research in biomedical data analytics and data science. Prereq: Graduate standing in a master, doctoral, or professional program; and permission of the instructor. Repeatable to a maximum of 27 cr hrs or 9 completions.

Fundamental modeling and simulation techniques in GIS, including cellular automata, diffusion models, and agent-based models, and their applications in social, environmental, and natural resources research.

An introduction to spatial data analysis in geography: the fundamental statistical and spatial analysis methods used in quantitative geographic research.

Data representation using hashing, search trees, and linked data structures; algorithms for sorting; using trees for language processing; component interface design; best practices in Java. Prereq: 2221 or 5022. Not open to students with credit for 2231, 2231.01, 321, or 421.

Data representation using hashing, search trees, and linked data structures; algorithms for sorting; using trees for language processing; component interface design

Intellectual foundations of software engineering; design-by-contract principles; mathematical modeling of software functionality; component-based software from client perspective; layered data representation. Previous programming experience in any language required. Prereq: At least one term of Calculus. Not open to students with credit for 2221, 2231, 4221, 321, or 502. Not open to students enrolled in a CSE or CIS major.

Intellectual foundations of software engineering; design-by-contract principles; mathematical modeling of software functionality; component-based software from client perspective; layered data representation.

This is a two semester course sequence focusing on how to evaluate social work practice and programs. Over the two semesters students will learn how to design an appropriate evaluation strategy, select relevant outcome measure, collect and analyze evaluation data, and use evaluation outcomes to guide practice and policy decisions. Prereq: 6401 (770 and 771) or 7400 (770 and 771) or 8401. Not open to students with credit for 772.

This course is an introduction to analyzing social media data. Students will learn how to operate an established analytics platform and complete a novel research project using real world social media data. Students will learn how to tackle industry and academic research problems using social media logs and metrics.

Six Sigma Projects class.

Designed to familiarize students with the Six Sigma process improvement methodology and to provide them an opportunity to practice using Six Sigma Black Belt tools.

In this seminar, we will study both the theoretical and substantive development of the field of social networks as well as methodological tools necessary for conducting research. We will explore how our relationships significantly shape many aspects of our lives including cognition, motivations, identity, social mobility, group organization and resource distribution.

Scientific underpinnings for analyzing clinical situations in conjunction with appraising data, theories and concepts that guide nursing practice at the clinical doctoral level. Prereq: Enrollment in DNP program, or permission of instructor.

Upper-level undergraduate class on various aspects of sport industry research methods that gives a general introduction to social research methods and will cover four broad topics: the foundations of sport industry research, research design, data collection, and data analysis. Prereq: Jr or Sr standing.

Introduction to sociological research techniques, methodological approaches, and relevant quantitative procedures.

An overview of issues, methods, and techniques of scientific psychological research. Students must be pursuing majors or minors in Psychology.

Introduction to research design and data analysis for the hearing and speech sciences. Prereq: Grad standing or permission of instructor. Not open to students with credit for 785.

Introduction to the process of scientific inquiry, research methodology, basic statistical concepts and interpretation of data analyses. Research methodology from a dental perspective will be introduced. Prereq: Admission to DHY or BDCP major.

Level: Graduate

Offered by: Department of Anthropology, College of Arts & Sciences

Taught by: Mark Moritz

This course on research design and ethnographic methods is highly recommended for students who are considering doing social science research. Students will learn how to design a study and get hands-on training in a wide range of different research methods. In a collaborative research project we will design a study, collect data, analyze data, and write up the results. Students will also learn how to write a competitive grant proposal.

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Students learn to study anthropological problems through hands on experience with ethnographic methods, critical discussion of issues in ethnographic research and design of an ethnographic study.

Continuation of 2400. Application of scientific inquiry, research methodology, basic statistical concepts, and interpretation of data analyses. Presentations of community dentistry and research poster projects will be completed. Prereq: 2400. Not open to students with credit for 3410.

Explication, interpretation, and application of techniques for quantitative analysis of political data; descriptive and inferential statistics, with emphasis on bi-variate analysis. Prereq: Not open to students with credit for 685.

Level: Undergraduate

Offered by: John Glenn College of Public Affairs

Taught by: Josh Hawley, Associate Professor

This course provides an orientation to the use of data for decision-making in the public
sector. The emphasis in the course is how to use data in context – when organizations
require the analysis of sophisticated data in order to achieve goals or priorities. Topics in
the class include:

1. Data use and decision making in the public sector;
2. Legal and ethical framework for data use in the public sector;
3. Use of data sciences in evaluation, planning, and performance reporting;
4. Visualization techniques in public policy analysis;
5. Geographic analysis; and
6. Statistical tools to improve the use of data in decision-making.

This course is an introduction for students interested in big data and public affairs, and
focuses on data science methods in the public sector. This teaches both an understanding
of the technical and methodological challenges of working with government data, but also
the ethical and legal restrictions government places on data utilization.

Questions? Email hawley.32@osu.edu.

Students learn how to design and perform policy evaluations focused on policy processes and outcomes, using both qualitative and quantitative data. There is particular emphasis on the development of research designs and application of analytic methods for public policy evaluation. Prereq: 3000, Stat 1350 or above, and Econ 2001.01, 2001.02, or 2001.03; or permission of instructor.

Introduces students to the concepts, theories, and methods of applied policy analysis. Students develop an understanding of market failures and the tools available to public sector actors to mitigate them. Students also learn various methodological approaches to analyze public policy at the formulation and implementation stages. Prereq: Econ 2001.01, 2001.02, or 2001.03, and GE Data Analysis course; or permission of instructor.

Level: Undergraduate and Graduate

Offered by: College of Public Health

Taught by: Ayaz Hyder

In this course, students will gain a very general introduction to data analytics concepts and methods in the context of current and emerging public health issues. Students will be exposed to foundational concepts in data analytics that are relevant to the design, analysis, and interpretation of data-driven decision-making in public health and healthcare settings. After taking this course, students can opt to take PUBHLTH 7015 – Public Health Data Analytics II, which will allow guided application of methods.

Public Health and Data Analytics 1. Students will learn how to program in R, identify strengths and weaknesses of specific analytical methods in given data sets and scientific hypothesis, learn terminology and methods of data analytics in public health, describe best practices around the sharing of data, and examine the social, political, ethical, and scientific considerations around Public Health research findings. Class number: 3922 for Graduate and 3923 for Undergraduate students. Prereq: PubHBio 2210, Stat 3202, 3470, or 5301; or permission of instructor.

Provides students with analytical knowledge and tools necessary to acquire, manage and analyze data. Students learn probability and statistics to conduct analysis and evaluation and evaluate the quality of analyses conducted by others. Prereq: Not open to students with credit for both 820 and 822.

An overview of theoretical and practical aspects of the assessment and prediction of human behavior; topics include achievement, intelligence, personality, attitudes, interests, and interpersonal relations.

Intensive group project involving design, development, and documentation of a web application; client-side and server-side scripting; communication skills emphasized; builds programming maturity. Prereq: 2231; and 2321; and 2421 or 3430, or 2451 and ECE 2560; and enrollment in CSE, CIS, ECE, or Data Analytics major.

Intensive group project involving design, development, and documentation of an interactive software system, a 2D interactive game; communication skills emphasized; builds programming maturity. Prereq: 2231; and 2321; and 2421 or 3430, or 2451 and ECE 2560; and enrollment in CSE, CIS, ECE, or Data Analytics major.

Python programming for students well-versed in programming with another imperative language. Prereq: 2122, 2123, or 2231; and 2321; and enrollment in CSE, CIS, ECE, or Data Analytics major, or CIS minor. This course is graded S/U.

Syntax and pragmatics of C++ programming; C++ types, arrays, classes, pointers; objects and classes; compile-time vs. run-time picture; inheritance; template classes. Prereq: 2123 or 2231; and 2321; and enrollment in CSE, CIS, ECE, or Data Analytics major, or CIS minor. Not open to students with credit for 2122. This course is graded S/U.

C# programming for students well-versed in programming with another object-oriented language. Prereq: 2122, 2123, or 2231; and 2321; and enrollment in CSE, CIS, ECE, or Data Analytics major, or CIS minor. This course is graded S/U.

Fundamentals of probability and statistical analysis for applications and data interpretation in Civil and Environmental Engineering design and analysis. Prereq: Enrollment in CivilEn or EnvEng major. Not open to students with credit for 405.

Formal languages and grammars; recursive descent parsing; data types, expressions, control structures, parameter passing; compilers and interpreters; memory management; functional programming principles. Prereq: 2231, 2331 (680), and 2421, and 3901 (560), 3902, or 3903. Not open to students with credit for 5341 (655).

Formal languages and grammars; recursive descent parsing; data types, expressions, control structures, parameter passing; compilers and interpreters; memory management; functional programming principles. Prereq: 2231, 2331 (680), and 2421, and 3901 (560), 3902, or 3903. Not open to students with credit for 5341 (655).

Formal languages and grammars; recursive descent parsing; data types, expressions, control structures, parameter passing; compilers and interpreters; memory management; functional programming principles. Prereq: 2231, 2331 (680) and 2421, and 3901 (560), 3902, or 3903. Not open to students with credit for 3341 (655).

Introduction to economic theory: supply and demand for goods, services, and factor inputs; market structure; international trade, the distribution of income. First required course for students planning to take 4000-level courses in Econ.

Presents the concepts, framework and tools of market-based management to deliver superior customer satisfaction, value and profitability. The focus of the course is on identifying, creating, delivering and measuring value creation.

Introduction to the theory of national income determination; economic fluctuations; money; government policy; international economics.

Principles of designing experiments; analysis of variance techniques for hypothesis testing; simultaneous confidence intervals; block designs; factorial experiments; random effects and mixed models; observational data. Prereq: 3201 and 3202; or permission of instructor.

Introduction to the development of conceptual and data models for public policy analysis including hands-on experience with using simulations tools for agent-based and system dynamics modeling. Prereq: 6080 (804) or permission of instructor.

Introduction to phonological analysis and description, and an overview of phonological structure across languages.

Cross-linguistic survey of the sounds of the world’s languages.

Provides students with an understanding of performance measurement and management principles to create useful performance information, to use data to manage operations, to develop more effective employees and to make financial decisions.

Introduction to computer architecture and organization at machine and assembly level; pointers and addressing using C programming; introduction to operating system concepts: process, memory management, file system and storage, and multi-threaded programming.

Methods of gathering, interpreting, and reporting nutrition assessment data. Prereq: HumnNtr 2310, EEOB 2520, or equiv.

Numerical methods for scientific computation: computer arithmetic, rounding errors, machine precision, machine representation, root-finding, interpolation, integration, linear systems, splines, smoothing, curve-fitting, linear programming.

Security threats and services, elements of cryptography, protocols for security services, network and internet security, advanced security issues and technologies.

Introductory data analysis course that focuses on understanding and applying basic statistical concepts, problem solving, and interpreting the results of statistical analysis. Au, Sp Sems. Prereq: Not open to students with credit for 222. GE data anly course.

An introduction to modern curatorial practice in a museum, with a particular emphasis on natural history collections. Use of public data repository including data acquisition and curation. Process physical specimens and other research materials, and make data available and searchable using electronic means. Prereq: Permission of instructor.

Introduction to a wide variety of multimedia tools for use in STEM Education. The tools are constantly changing but some of the areas included are sensors, data collection, graphics, animation, presentation, audio, video, and multimedia authoring. Prereq: Not open to students with credit for 735. Repeatable to a maximum of 9 cr hrs.

Spreadsheet and database modeling/programming concepts and techniques to solve business and engineering related problems; efficient/effective data handling, computational analysis and decision support. Prereq: 1222 (202), 1223 (201), Engr 1281.01H, or 1281.02H. Prereq or concur: Math 1151, 1161.01, or 1161.02. Not open to students with credit for 2111 (200), 1111 (101), 1112 (105), or 1113.

Spreadsheet and database modeling/programming concepts and techniques to solve business related problems; efficient/effective data handling, computational analysis and decision support. Additional topics: computer concepts, networking, project integration. Prereq: Math 1130 or above, or Math Placement Level L. Not open to students with credit for 1111 or 1112. This course is available for EM credit. GE quant reason math and logical anly course.

Practical experience in the fabrication of instruments, deployment of measurement systems in the field, and recording of data related to microclimates. Prereq: A grade of C- or above in 5921.

Microbes are major players across diverse ecosystems including humans, soils, and the oceans. Through concept-introducing lectures, hands-on practical exercises and a semester-long group research project, this course will introduce the student to modern approaches for interpreting sequence datasets to improve understanding of microbes and their viruses in complex communities. Prereq: 5161, 6155, or permission of instructor.

This course will introduce research methodologies specifically in technology-supported learning environments, including self-report and experience sampling method, data intensive research, big data movement, learning analytics, data collection issues, data access and privacy, content analysis of online discussions, social network analysis, as well as design based research.

Astronomical observational techniques and quantitative analysis of astronomical data; practical experience with modern astronomical instrumentation and computer-based reduction, analysis, and interpretation of astronomical data. Prereq: 1162 (162), 1162H (162H), or 2292 (292), and Math 1152 (153), and Physics 1251 (133). Not open to students with credit for 350. GE data anly course.

An intensive, application-oriented survey of methods used during the course of the design, implementation and evaluation of BMI platforms, including clinical info systems, decision support systems, databases, elec, data capture instruments, data visualization tools and other analytical “pipelines”. These methods span a broad spectrum from information needs assessments to systems evaluation. Prereq: Basic knowledge of the following areas – basic computer science principles (logic, procedural and/or object oriented programming, data structures and algorithms), statistical methods, and medical terminology.

Free and forced vibration analysis of single-degree-of-freedom systems with various forms of damping, vibration isolation and control methods and devices, vibration sensors, equations of motion of multi-degree of freedom systems using Lagrange’s method, Eigen value problem, modal analysis method for free and forced vibration analysis, frequency-domain data analysis fundamentals. Prereq: 3260 (481). Prereq or concur: 3360 (482), or Grad standing in Engineering. Not open to students with credit for 650, 666, or 7250.

Explores the principles and procedures for accurately assessing the sensory and hedonic properties of foods and consumer products. Appropriate test design, statistical analyses and data interpretation will be discussed and the physiological and psychological principles impacting sensory judgments will be explored. A proficiency in basic statistics and Excel is required. Prereq: ComLdr 3537, AEDEcon 2005, AnimSci 2260, HCS 2260, ENR 2000, or Stat 1450.

Laboratory experiments related to materials processes and properties. Introduction to experimental techniques used in materials fields. Data analysis, presentation, and technical writing skills. Prereq: 2331, and enrollment as MatScEn-BS major; or permission of instructor. Not open to students with credit for both 581.01 and 581.02.

Course examines the role of marketing research in the formulation and solution of marketing problems. Emphasis is placed on problem formulation, research design, data collection methods (instruments, sampling, operations) and analysis techniques.

Analysis of advanced capital budgeting problems through understanding the theories and applications of capital structure, leasing and real options. Theories and applications in corporate control, corporate governance and mergers and acquisitions.

Introduction to the power of maps, covering spatial representation, visual literacy, and geographic information technology in a global society.  

Introduction to basic concepts of machine learning and statistical pattern recognition; techniques for classification, clustering and data representation and their theoretical analysis.

Focus on the operational use of logistics software technologies such as transportation management systems, warehouse management systems, and inventory management systems. Course taught in a lab environment with tools for lectures, exercises and HW.

Concepts and methods used to plan and manage logistics activities in a business environment. Understanding of the components of logistics management and tradeoffs required to manage the integrated flow of goods through the supply chain.

This case-based course will prepare students to make data-driven decisions on key logistics issues and then make these decisions actionable through a focus on appropriate solution methods needed to achieve firm financial goals.  

Will equip student with the tools and skills necessary to recognize, analyze and solve significant problems in the operation of a logistics system through the application of techniques using spreadsheet analysis, optimization and simulation.

Introduction to linguistic meaning across languages, including word meaning, the contribution of syntactic structure, and the role of context in interpretation.

Matrix algebra, vector spaces and linear maps, bases and dimension, eigenvalues and eigenvectors, applications.

Level: Graduate

Offered by: College of Public Health

Taught by: Macarius Donneyong

The primary objective of this course is to introduce students to the practical issues in obtaining and analyzing healthcare databases and to become familiar with the basic principles of observational study designs regarding the use of these databases to study health outcomes and health policy.

An introduction to spatial data analysis in geography: the fundamental statistical and spatial analysis methods used in quantitative geographic research. Prereq: Math 1116 or 1130 or above, or Math Placement Level M or L, or permission of instructor. Not open to students with credit for 5100.

Algebra-based introduction to data analysis, experimental design, sampling, probability, inference, and linear regression. Emphasis on applications, statistical reasoning, and data analysis using statistical software. Prereq: Math 1116 (116) or 1130 (130) or above, or Math Placement Level L or M, or permission of instructor. Not open to students with credit for 2450 (245) or 145. GE data anly course.

The course provides an introduction to the principles of statistical learning and standard learning techniques for regression, classification, clustering, dimensionality reduction, and feature extraction.

Foundational inferential methods for learning about populations from samples, including point and interval estimation, and the formulation and testing of hypotheses. Statistical theory is introduced to justify the approaches. The course emphasizes challenges that arise when applying classical ideas to big data, partially through the use of computational and simulation techniques.

Calculus-based introduction to statistical data analysis. Includes sampling, experimental design, probability, binomial and normal distributions, sampling distributions, inference, regression, ANOVA, two-way tables.

Principles and methods for visualizing data from measurements and calculations in physical and life sciences, and transactional and social disciplines; information visualization; scientific visualization.

Introduction to programming using SAS software to accomplish public health data management and analysis. Prereq: Grad standing in PubHlth, or permission of instructor. Not open to students with credit for Stat 5740 or 6740.

A survey of biomedical informatics theories and methods employed in the design, implementation and management of clinical and translational research programs. Recommended experience with computer science, statistics, anatomy, medical terminology, and molecular biology.

An introduction to probability and its role in statistical methods for data analytics. Equal emphasis is placed on analytical and simulation-based methods for quantifying uncertainty. Approaches to assessing the accuracy of simulation methods are discussed. Students should have some prior knowledge of basic programming. Applications of probability and sampling to big-data settings are discussed.

Introduction to probability, Bayes theorem; discrete and continuous random variables, expected value, probability distributions; point and interval estimation; hypotheses tests for means and proportions; least squares regression. Prereq: Math 1152, 1161.xx, 1172, 1181H, or equiv, or permission of instructor. Not open to students with credit for 3450 or 3460. GE data anly course.

Parallel programming models; sequential and parallel performance issues; high-performance computer architecture; design, analysis, implementation and performance evaluation of parallel algorithms.

Introduction to operations and supply chain management to improve manufacturing and service organizations; analyzing, controlling and improving resources and processes to increase productivity, generate value-added output and meet business goals.

Prereq: Econ 2001.01 (200), 2002.01 (201); and Stat 1430 (133) or equivalent.

Survey of fundamental methods and techniques of neural networks; single- and multi-layer perceptrons; radial-basis function networks; support vector machines; recurrent networks; supervised and unsupervised learning.

Introduction to Network Science; Global and Local Network Measures; PageRank; Community Discovery  Algorithms; Network Models; Understanding the role of network analysis in Web and Social network applications

Foundation in experimental measurement and data analysis in mechanical engineering; team planning and execution of experiments; technical report writing. Prereq: Stat 3450 or equiv, and 3260 (482) or 3261, and a second writing course, and enrollment in Mechanical Engineering major; or permission of instructor. Prereq or concur: 3503 (504) or 3504. Not open to students with credit for 570.

Decision theory, point and interval estimation, Neyman-Pearson lemma, likelihood ratio tests, tests for means, variances, and proportions, nonparametric tests, regression, and ANOVA. Prereq: C- or better in 4201, Math 4530, or 5530H, or permission of instructor. Not open to students with credit for 3202, 6201, 6302, or 6802. GE data anly course.

Teach students from different backgrounds the role of imaging in biomedical research, decision support and personalized medicine. Recommended familiarity with a programming language and operating system.

Introduction to experimental design, including selection and layout of plots, data analysis software, and data interpretation.

Database concepts and techniques for efficient/effective data handling, computational analysis and decision support. Prereq: Not open to students with credit for 1111, 1112, or 2111.

Database systems use; logical design; entity-relationship model; normalization; query languages and SQL; relational algebra and calculus; object relational databases; XML; active databases; database design project. Prereq: 2133 or 2231 or 2233 (314) or 321, and 2321 or Math 2366 (366). Not open to students with credit for 3241 (670).

Database systems use, logical design, entity-relationship model, normalization, query languages and SQL, relational algebra and calculus, object relational databases, XML, active databases; database design project.

Knowledge discovery, data mining, data preprocessing, data transformations; clustering, classification, frequent pattern mining, anomaly detection, graph and network analysis; applications.

Problem solving techniques using productivity software; spreadsheets, formulas, conditional logic; relational databases, relational algebra; word processing; data presentation; graphics. Prereq: Not open to students with credit for 1112 (105), 1113 (101), or 200. GE quant reason math and logical anly course.

Exploration of current interdisciplinary best business practices in industry. Students will work across business specializations to learn strategy and implementation details from current industry professionals.

A survey of biomedical informatics theories and methods employed in the design, implementation and management of information systems supporting basic science, clinical and translational research, clinical care, and public health. Recommended course work in computer science, statistics, anatomy, physiology, and medical terminology.

Prereq: Not open to students with credit for 710.

Introduces students to basic topics of bioinformatics including sequence analyses, proteomics, microarrays, regulatory networks, sequence and protein databases. Recommended background in molecular biology and computer science.

An introduction to Bayesian statistics & data analysis for graduate students in the Dept of Psychology. It reviews basic probability theory & Bayes theorem, provides a broad introduction to inference from the modern Bayesian perspective & contrasts that to more traditional frequentist inference. Topics covered include analysis of proportions & means, interval estimates, regression, & Bayes factors. Prereq: 6810 (828), and enrollment in Psychology.

The design of accounting information systems; focus is on business processes and internal control.

The second course in a two-semester sequence in data analysis covering simple linear regression (inference, model diagnostics), multiple regression models, variable selection, model selection, two-way ANOVA, mixed effects model. Prereq: 5299, 5301, or 529, or permission of instructor. Not open to students with credit for 530. GE data anly course.

The first course in a two-semester non-calculus sequence in data analysis covering descriptive statistics, design of experiments, probability, statistical inference, one-sample t, goodness of fit, two sample problem, and one-way ANOVA. Prereq: Math 1075 (104) or equiv, or Math Placement Level of R, or permission of instructor. Not open to students with credit for 5301 (528 and 529), or 5302 (529 and 530). GE data anly course.

Overview of molecular and cellular biology of single cells, tissues, organisms and their interactions, including genetics and biochemistry of single cells.

Team-based projects: solve information security problems (mobile/static host/network hardening, intrusion detection and vulnerability scanning, forensics); results communicated through report writing and presentation.

Introduction to security of digital information; threats and attacks; regulations; risk management; attack detection and response; cryptography; forensics; technical training and certifications.

Applied systems planning and analysis techniques including database design and project management for health information systems evaluation, modeling and implementation. Prereq: 3520, 5648, and 5650; or Grad standing in HRS; or permission of instructor.

Health care regulatory and industry features of agreements relating to physician employment, licensing and data management, long-term care residency, joint operating arrangements and joint ventures, mergers and acquisitions. Credit for 7200 preferred prior to enrollment.

Health Analytics is the science of analyzing health data for knowledge discovery and decision making. The sheer diversity of data types in health care settings results in what scholars call a DRIP environment: Data Rich-Information Poor. Data has become ubiquitous in healthcare settings from clinical decision making to operational/business planning; health decisions are now being made similarly. No programming experience is required, but students are expected to vigorously engage in their own learning and parts of courses will require that students use and adapt to tools demonstrated during class.

The theory and algorithms underpinning today’s GIS technology and the development of custom GIS applications using the Python programming language.

Examination of issues, techniques and applications of analytic cartography, interactive maps and scientific visualization for exploring geographic data.

Focuses on designing, implementing, querying and managing spatial databases or persistent data stores where most entities have footprints in geographic space and time. This is critical for designing and implementing GIS for projects and organizations. It is also crucial for moving beyond GIS to the bigger world of geographic information services.

Introduction to the fundamental principles, methods, and geographic applications of remote sensing.

First course for science majors, covering dimensional analysis, atomic structure, the mole, stoichiometry, chemical reactions, thermochemistry, electron configuration, bonding, molecular structure, gases, liquids, and solids.

Prereq: One unit of high school chemistry, and Math Placement Level L or M; or a grade of C- or above in Math 1130 (130), 1131 (131), 1148 (148), 1150 (150), or above. Not open to students with credit for 1220 (123), 1620 (162), 1920H (202H), 1250 (125), 1610, or 1910H (202H). This course is available for EM credit. GE nat sci phys course. NS Admis Cond course.

Survey of research problems, methods, and designs utilized in respiratory care, with emphasis on data collection, analysis, and presentation. Prereq: 4589, or permission of instructor. Not open to students with credit for 593, 600, and AlliMed 680.

Basic principles of geographic and land information systems and their use in spatial analysis and information management.

Provides an introduction to using large datasets to better understand corporate financial reporting and market behavior. Prereq: Enrollment in Master of Accounting Program.

Fundamentals of natural language processing, automatic speech recognition and speech synthesis; lab projects concentrating on building systems to process written and/or spoken language.

Presents the concepts, framework and tools of market-based management. Topics include the marketing concept and the role that product, price, promotion and distribution play in marketing strategy and implementation.

Development of language skills using Italian media sources such as soap operas, internet resources, films, newspapers/magazines, etc. Not open to native speakers of Italian.

Management of financial risk inherent with international business; problems of implementing corporate finance principles; International Capital Markets, for non-Business majors.

Develops skills related to Time Value of Money, Risk and Return with applications in business and personal finance.

Prereq: AcctMIS 2000 (310), Math 1130 (130) or equiv, Econ 2001.01 (200), and CSE 100 or equiv.

A survey of accounting for non-business students; financial statements and analysis; accounting for assets, liabilities and owners’ equity; and elements of managerial accounting.

Design/analysis of algorithms and data structures; divide-and-conquer; sorting and selection, search trees, hashing, graph algorithms, string matching; probabilistic analysis; randomized algorithms; NP-completeness.

Design/analysis of algorithms and data structures; divide-and-conquer; sorting and selection, search trees, hashing, graph algorithms, string matching; probabilistic analysis; randomized algorithms; NP-completeness. Prereq: 2231 or 321, and 2321 or Math 366, and 2566 (566), and Stat 3470 (427). Not open to students with credit for 2331 (680).

Propositional and first-order logic; basic proof techniques; graphs, trees; analysis of algorithms; asymptotic analysis; recurrence relations.

How to use relevant accounting information for various applications in finance such as estimating stock and bond prices and free cash flows.

Construction, simulation and statistical analysis of data from advanced experiments in nuclear processes. Introduction to advanced instrumentation and computer controlled data acquisition. Computer programming is required. Prereq: 1251, 1251H, or 1261; and CSE 1222, 1223, Engr 1281H, or Astron 1221. GE data anly course.

Introduction to economics as an experimental social science. Students participate in and study results of economic experiments dealing with markets, individual decision making, and a broad array of game theoretic economic models.

This first of two Excel courses focuses on how to use Microsoft Excel from basic data entry, formatting, filtering and sorting, basic functions and formulas, basic nest formulas, and basic charts and graphs. A variety of techniques and data management concepts are presented and discussed. Real world examples are used to provide context. Repeatable to a maximum of 2 cr hrs.

Excel Advanced Skills focuses on how to use intermediate to advanced Microsoft Excel functionality from nested formulas to data analysis through the use of pivot tables and creating interactive worksheets. A variety of techniques and data management concepts are presented and discussed. Real world examples are used to provide context. Excel Basic class not required but recommended for beginners.

Analysis and interpretation of data from case-control and cohort studies and from clinical trials. Prereq: 7410 and 6211, or permission of instructor. Not open to students with credit for 712.

Introduction to probability and statistics, experiments, and sampling, data analysis and interpretation. Prereq: Math 1050 (050), or Math Placement Level S, or permission of instructor. Not open to students with credit for GEC Data Analysis (AEDEcon 205, AEE 387, AnimSci 260, Astron 350, Chem 221, DentHyg 383, EarthSci 245, Econ 443, ENR 222, HCS 260, IntStds 443, Linguist 286, Philos 153, Physics 416, PolitSc 485 (585), SocWork 570, Sociol 549, SphHrng 286, Stat 135, 145, 245, 427, or 520, or semester equivs). GE data anly course.

Introductory chemistry for non-science majors, including dimensional analysis, atomic structure, bonding, chemical reactions, states of matter, solutions, chemical equilibrium, acids and bases, along with topics in organic and biological chemistry.

Prereq: Eligibility to enroll in Math 1116 (116). Not open to students with credit for 1210 (121), 1250, 1610 (161), 1910H (201H), or 102. This course is available for EM credit. GE nat sci phys course. NS Admis Cond course.

Analysis of analog and digital electronic components and circuits. Analysis and design of measurement systems; data acquisition, signal conditioning, error and noise control, sensors and transducers used in food, agricultural and biological systems. Prereq: 3150 or equiv, or Grad standing in Engineering. Not open to students with credit for 605.

Discrete choice models, panel data, endogeneity, instrumental variables, and systems of equations; stationary and nonstationary time series; applications in various fields of economics. Continuation of 5410.

Introduction to discourse analysis in educational research, theories, field work, data collection, analytic procedures including video and audio analysis, and ethics. First course of two course sequence. Prereq: Permission of instructor. 8001 and 8002 must be taken in the same academic year. Not open to students with credit for 933. Repeatable to a maximum of 8 cr hrs. This course is progress graded.

Plan and analyze experiments relevant to system design. Also, students will learn regression and alternative approaches for on-hand data analysis. Prereq: 4210; or Stat 3470 or equiv, and Grad standing. Not open to students with credit for 610 or IndEng 610.

Practice-oriented development, design, implementation and evaluation of spatial databases, with an emphasis on local problems.

Hands-on experience in the execution of industry business practices to fill a need articulated by an operating public or private organization.

Study of computer-based systems designed to support decision-making within the organization.

This course will provide the knowledge and tools necessary to analyze data. Students learn about fundamentals of statistics to conduct analysis, and they will develop an understanding of program evaluation and research design in order to evaluate the quality of analyses conducted by others and communicate findings. Work on applied project related to their jobs that culminate in Capstone projects.

Subroutines and modular programming; searching; basic data structures; recursion; introduction to sequential files. Prereq: 1223 (201). Not open to students with credit for 214.

Introduction to programming in C++ and object-oriented programming; encapsulation using classes, inheritance, etc. Prereq: 1222 (202). Not open to students with credit for 230.

Systematic organization of data on cloud computing architectures; basic indexing techniques, including B-tree and hash-based indexing; fundamentals of query optimization, including access path selection and cardinality estimation; full and partial replication; data partitioning and distributed task scheduling. Prereq: 3241 or 5241; and 2421 or 3430; and enrollment in CSE, CIS, or Data Analytics major.

Business Analytics is the process of transforming raw data into insights and predictions. This course is on the fundamentals of data management-the art and science of collecting, organizing, cleaning, and manipulating raw data in a variety of formats and from multiple sources, and being able to do it efficiently and at scale using R. Prereq: Enrollment in the SMB-A program, or permission of instructor.

Introduction to the tools of data analysis in political science, with an emphasis on data visualization.

Introduction to the tools of data analysis in political science, with an emphasis on data visualization. GE data anly course.

Most social science debates can be addressed with data, and sources of data are growing exponentially. This course introduces students to tools of data analysis and principles behind their use in the context of social-science applications. GE data anly course.

The purpose of this course is to develop an overview and basic understanding of descriptive and inferential statistics. Prereq: Math 1130 (130) or 1148 (148). Not open to students with credit for AEE 387. GE data anly course. MA Admis Cond course.

Discussion of statistical analysis of psychological data – random samples, graphical and numerical techniques of descriptive statistics, correlation, regression, probability, sampling distribution, and hypothesis testing. Prereq: 1100 (100) or 1100H (100H), and Stat 1450 (145), Math 1130, Math 1148 (148), Math 1149, 1150 (150), 1151 (151), or 152, or equiv, or Math Placement Level M or higher; or permission of instructor. Not open to students with credit for 220, 220H, 320, or 320H. This course is available for EM credit.

Covers basic techniques for analyzing data in political science.

Covers basic techniques for analyzing data in political science. Prereq: Math 1151 and one course in PolitSc at the 3000 level or above; or permission of instructor. Not open to students with credit for 485, 4781H (485H), 585, or 585H. GE data anly course.

Application of programming and statistical methods for engineering data analysis. Will explore distribution, variance, and multivariate methods. Will provide a deeper understanding of analysis theories in the space, time, and spectral domain. Students will develop computer programming toolboxes and theoretical skills for analyzing and modeling data in their own research. Prereq: Stat 3450, 3460, 3470, or CivilEn 2050, or equiv; and Grad standing in the Civil Engineering or Environmental Science Graduate programs.

Introduction to data analysis and statistics for business. Emphasis on achieving an application-oriented understanding of statistical inference and regression analysis and their use in decision making. Part 2 of 2 course sequence. Prereq: Enrollment in MBOE or SMB-Finance Program Course. Concur: 6221. Not open to students with credit for 870.

Introduction to data analysis and statistics for business. Emphasis on achieving an application-oriented understanding of statistical inference and regression analysis and their use in decision making. Part I of 2 course sequence. Prereq: Enrollment in MBOE or SMB-Finance Program Course. Not open to students with credit for 870. This course is progress graded.

Introductory course in data analysis, stressing computer applications of probability and statistics, problems of data gathering, presentation, and interpretation in economics and business. Prereq: Math 1130 (130) or 1131 (131) or 1148 (148) or 1150 (150) or 1151.01 (151.01) or 1151.02 (151.02) or 1156 (151.03). Not open to students with credit for 2005 (205) or ComLdr 3537 (AgrEduc 387 or AEE 387) or HCS 2260 (260) or AnimSci 2260 (260) or Stat 1430 (133) or 1450 (145). GE data anly course.

Designed to equip students with competencies in translating real-world problems into forms that such technologies can assist with, to portray/visualize these translations in ways that enhance the understanding of the dynamics of these problems, to structure mechanisms that derive suggested solutions, to clearly convey the justification and practicality of final solutions to others. 

Introduction to the principles of biostatistical methods used in biomedical research. Analysis of clinical and preclinical research data and interpretation of statistical results in biomedical studies. Prereq: Enrollment in Master of Clinical Research program or MS Translational Pharmacology program, or permission of instructor. Not open to students with credit for PubHBio 6210.

Introduces students to the use of statistics in data analysis and interpretation. Students develop skills in quantitative literacy and logical reasoning. Prereq: Math 1130, 1148, 1150, or 1151. Not open to students with credit for AEDEcon 2205, ComLdr 3537, ENR 2000, HCS 2260, or Stat 1450. GE data anly course.

Basic concepts of probability and statistics applied to the interpretation of quantitative data.

Examines the theories of methods used to identify profitable customers, understand their needs and wants, and how to build a bond with them by developing customer-centric products and services directed toward providing customer value.

Tools for predicting the effects of product reformulation. A choice simulator is constructed to investigate the source of volume and effect of targeted marketing actions. A comparison to, and the integration of, marketplace data is examined. Prereq: MBA 6250, 6252, or 6253. Concur: BusML 7219.02. Not open to students with credit for 7204.01.

Tools for the analysis of survey and marketplace data. Topics include deign of the surveys, scale development, analysis of the recency, frequency and monetary value of transactions, web-based ‘click-stream’ data and others. Prereq: MBA 6250, 6252, or 6253. Concur: BusML 7204.02. Not open to students with credit for 7219.01 or 7219.

Provides students with an understanding of how to convey complex information visually using software tools so as to facilitate, frame and inform debates around issues in public policy and public finance.  

Application of marketing research and statistical analysis using spreadsheet applications to examine consumers, consumption patterns, workforce behavior, and retailer/marketplace responses. Prereq: Stat 1350, 1430, or 1450, and CSE 1111 or 2111. Not open to students with credit for 340.

Computer vision algorithms for use in human-computer interactive systems; image formation, image features, segmentation, shape analysis, object tracking, motion calculation, and applications.

Computer networks, communication protocols, Internet TCP/IP and applications, wireless communications and network security.

Computational models of language acquisition and application of machine learning techniques to language processing.

Symbolic and probabilistic computation applied to the structure of words and sentences, models of syntax, parsing algorithms.

Tools to identify, parse, and analyze the economic and market data necessary to complete a comprehensive strategic analysis of business competition. Application of major steps in industry analysis, competitive positioning, capability analysis, competitor analysis, and some private equity valuation. Prereq: Enrollment in MBA or WPMBA program, or permission of instructor. Not open to students with credit for 838.

Overview of major empirical methods in communication research.

Overview of major empirical methods in communication industry research.

A study of the cartographic techniques of map compilation and design including generalization, symbolization, reproduction, and GIS-based mapping with an emphasis on thematic mapping.

Students will learn about the genetic evolution of human cancer. Specifically, the hallmarks of cellular transformation and tumor progression will be discussed. Students will be introduced to modern approaches aimed at targeting genetic aberrations in cancer cells. Includes hands-on training in the use of online databases of cancer genetics. Prereq: A grade of C- or above in 4606 or 4500; or Grad standing.

Business data processing principles and programming: sequential file processing algorithms, sorting, data validation; COBOL is taught. Prereq: 2123, and enrollment in Business Info Sys major or CIS minor.

Introductory finance class which allows students develop the skills to understand how financial managers make value-maximizing decisions for their firms.

Understand how organizations can successfully collect, organize, manipulate, use and present data. Prereq: Econ 2001.01 and 2002.01 or equiv; and Math 1152 or 1161.01 or equiv; and CSE 2111 or equiv.

Examination of the use of business analytic models used in managerial decision making processes. Emphasis on formulation and interpretation of models; supported by spreadsheet based software.

Hands-on experience using statistical tools to answer real-world questions. Students will design and implement a short survey and analyze their results. Emphasis on analysis of actual survey data using statistical software. Statistical topics include numerical/graphical summaries, measures of association, and hypothesis testing. Focus is on interpretation, not calculation. Prereq: Stat 1350 (135) or Stat 1450 (145), or equiv.

Exploration of biology and biological principles; evolution and speciation, diversity in structure, function, behavior, and ecology among prokaryotes and eukaryotes. A broad introduction to biology comprises both Biology 1113 and 1114.

Prereq: Math 1130 (130), 1148, or 1150 or above, or Math Placement Level L or M.

Exploration of biology and biological principles; evolution and the origin of life, cellular structure and function, bioenergetics, and genetics. A broad introduction to biology comprises both Biology 1113 and 1114.

Introduction to concepts and methods for making decisions in the presence of uncertainty. Topics include: formulation of decision problems and quantification of their components; learning about unknown features of a decision problem based on data via Bayesian analysis; characterizing and finding optimal decisions. Techniques and computational methods for practical implementation are presented.

Introduction to probability; Normal distribution; Confidence intervals for means; Hypothesis tests for means; Multi-factor experiments; Experiments with blocking. Prereq: Math 1152, 1161.xx, 1172, or 1181, or equiv, or permission of instructor. Not open to students with credit for 3460 or 3470. GE data anly course.

This course provides students with an overview of basic behavioral processes related to human and nonhuman learning. This course focuses on the basic principles of the science of behavior, the direct application of these principles, and the use of data collection procedures for analysis of behavior.

Models and methods for the dataset with missing values, including imputation, likelihood-based, and Bayesian models. Prereq: 6211 (703), or Stat 6201, or 6302, or 6802 (622), or 6302, or 6450, or 6950, or permission of instructor. Not open to students with credit for Stat 6520 (652). Cross-listed in Stat 6520.

Applied Bayesian Statistics. Students will learn basic Bayesian principles such as Bayes’ theorem, prior and conditional distributions, and likelihood functions, how Bayesian and frequentist approaches differ, the ability to factor conditional distributions, and how to program R around Bayesian analysis. Students will also gain experience working with supercomputers (HPC). The course is taught using R. Basic statistic and command-line programming experience are recommended prerequisites.

This course aims to provide a practical introduction to Bayesian data analysis. Students will be exposed to a variety of Bayesian models including the Bayesian linear model for normal and nonnormal data. Bayesian hierarchical modeling will be discussed as a strategy for modeling complex processes and as a means of assimilating a variety of sources of data. Simulation-based methods for model-fitting will be introduced, and students will learn to use the JAGS software in addition to programming basic MCMC algorithms in R.

Prerequisites: STAT 6301 or STAT 6801, or permission of instructor.Prerequisites or concurrence: STAT 6450 or STAT 6950, and STAT 6302 [with 6301 prerequisite]or STAT 6802 [with 6801 prerequisite].

The sounds that languages use are examined. Quantitative analytical tools used in phonetic science are introduced. Small experiments are conducted to introduce students to research. Prereq: Math 1075 or 1148, or an ACT math subscore of 22 or higher that is less than two years old; or Math Placement Level R. Not open to students with credit for 2051H (Linguist 286H) or 286, SphHrng 2051 (286), or 286H. GE data anly course. Cross-listed in SphHrng.

Course gives students experience analyzing language in social media. It covers theoretical issues arising in digital communication and provides hands-on practice at computational data analysis, applicable across fields. Students gain an understanding of the sociolinguistic dynamics of online communication and the technical skills to conduct research on them. Team-taught w/ faculty in Linguistics. 

Course gives students experience analyzing language in social media. It covers theoretical issues arising in digital communication and provides hands-on practice at computational data analysis, applicable across fields. Students gain an understanding of the sociolinguistic dynamics of online communication and the technical skills to conduct research on them. Team-taught w/ faculty in English. 

Principles and practice of data acquisition, processing, and analysis in chemical research. Prereq: 4870 (587) or equiv. Not open to students with credit for 720.

Provides an introduction to event history analysis and other types of longitudinal data analysis. Prereq: Grad standing, or permission of instructor. Not open to students with credit for 852.

Methods of analyzing biological data including: sampling, descriptive statistics, distributions, analysis of variance, inference, regression, and correlation. Emphasizes practical applications of statistics in the biological sciences. Prereq: Math 1149 or 1150 (150) or equiv, and 10 semester cr hrs at the 3000-level (or 300 level in the quarter system) or above in Agricultural or Biological Sciences. Not open to students with credit for 650.

This course introduces trainees to the fundamental algorithms needed to understand and analyze genome-scale expression data sets. The course will cover three major kinds of applications: Class Comparison, Class Discovery, and Class Prediction. We will also discuss methods for cross-validation and independent validation of predictive models. R software environment will be introduced and utilized.

Weather theory, patterns, data, and analysis. Data formats and sources. Consideration of weather conditions as they relate to aircraft and flight performance. Prereq: Aviatn 2000 and 2100. Not open to students with credit for Aviation 410 or 411.

Advanced-level topics in databases. Repeatable to a maximum of 24 cr hrs or 8 completions. This course is graded S/U.

Application of quantitative methods to geographic problems; spatial statistics, area sampling, maps of residuals, regionalization methods, and simulation maps.

Applications of modern marketing research procedures to variety of marketing problems. Emphasis placed on survey design and sampling, attitude measurement, perceptual mapping, market segmentation, conjoint analysis, and measurement of customer satisfaction.

Level: Graduate

Offered by: Department of Economics, College of Arts and Sciences

Taught by: Mehmet Caner

This is a graduate-level course on high-dimensional econometrics, aka big data. This means we analyze two cases, where the regressors are growing with sample size. First, when the regressors are less than the sample size, where the main emphasis will be in asymptotics and selection consistency proofs. We will see a benchmark estimator: adaptive lasso. The second case is when the number of regressors are larger than sample size; there we focus on finite sample issues (oracle inequalities) and then we talk about building confidence intervals in high dimensional cases. In certain weeks, we will work on R programs that run high-dimensional regression. The important packages in R are lars and glmnet; we will learn how to implement them.

Transaction management; query processing and optimization; organization of database systems, advanced indexing, multi-dimensional data, similarity-based analysis, performance evaluation, new database applications.

An introduction to accounting and cost analysis for planning, decision-making, and control.

Students will understand the fundamentals of accounting (managerial and financial) and finance. The course will cover defining financial data, transforming data into information and leveraging financial information to make financial decisions. Students will have hands-on experience with various types of real-world financial data and build a detailed discounted cash flow model. Prereq: Enrollment in SMB-Analytics program.