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Table of contents

Material includes advanced topics in decision analysis, models of probabilistic reasoning, decision making in competitive situations, and models of human and computer reasoning including artificial intelligence. Prerequisite: IE The study of the theoretical foundation and relevance of advanced computer and communication methods in the planning and control of intelligent production operations; manufacturing operating systems; synchronization in decentralized systems; recovery in decentralized systems; parallel processing; distributed databases; factory networks; reasoning and logic for production control.

Prerequisite: IE or or IE In a Big Data environment, managers have access to vast amount of market data. Technological developments have made it possible to generate insights from data within a matter of seconds which can be leveraged to formulate and refine marketing programs in real time. Data-driven marketing requires marketers to have the ability to understand what data they have, what data they can collect and, ultimately, how to utilize data to both quantify the strategic value of marketing initiatives and to craft effective marketing programs.

This course will introduce students to this era of data-driven marketing. It has a heavy focus on understanding practical solutions for marketing applications, and better and more informed decision making. Since a large percentage of societal and management problems can be characterized as relatively unstructured, this course explores how computers can be used to aid decision makers in dealing with unstructured, as well as structured, problems.

Appropriate material from knowledge representation, artificial intelligence, and language theory is considered. Applications selected from environmental management and strategic planning in large organizations are used to illustrate theoretical ideas. Supply Chain Analytics focuses on data-driven and rigorous decision making in supply chain management. It is a complete problem solving and decision making process, and integrates a broad set of analytical methodologies that enables the creation of business value.

Introduces the concepts, techniques, tools, and applications of data mining. The material is approached from the perspective of a business analyst, with an emphasis on supporting tactical and strategic decisions. Includes a variety of techniques to identify nuggets of information or decision-making knowledge in bodies of data, and extracting these in such a way that they can be put to use in the area, such as decision support, prediction, forecasting, and estimation.

Provides the basic concepts and skills needed to analyze and organize business data, as well as to utilize the organized data to answer a variety of business queries. After successful completion, students will have an understanding of why database management is important and what it entails, how to analyze the data requirements of a business scenario and represent these requirements by means of entity-relationship ER diagrams, translate an ER diagram into normalized relations for a relational database management system, write simple and relatively complex data retrieval commands in the SQL language for an Oracle database, use Microsoft Access as a front end to a server database in Oracle, be familiar with several selected topics of current interest in the data management arena.

Accounting information Systems is a course designed to provide students with a solid background in the information systems that accountants use. Topics include, but are not limited to, input, processing and output devices, data communications and networks, document and system flowcharts, organizing and manipulating data in databases, producing reports and forms, popular accounting software, enterprise-wide information systems, security, privacy and ethics for accounting information, and information technology auditing.

The course will advance students' abilities in the following areas. Mathematical fundamentals of computing with neural networks. Survey of engineering applications. Computational metaphors from biological neurons. Artificial neural networks modeling of complex, nonlinear and ill-posed problems. Emphasizes engineering utilization of neural computing to diagnostics, control, safety, and decision-making problems. Emphasis is on the impact of human resource components e. Case analyses and computerized databases are used to illustrate major components of human resource decision making.

Prerequisite: Master's student standing and Management majors only. This course is a first-semester graduate statistics course for students in psychology and related fields who conduct quantitative research. The course involves an accelerated review of fundamental concepts e. This is a weekly forum for presenting both applied and theoretical work in the broad area of bioinformatics. Bioinformatics is the science of generating, organizing, and analyzing biological data.

This seminar series occurs both in the fall and spring semesters and attracts speakers from Purdue University, as well as throughout the world. Students are encouraged to register for this course, and everyone else is encouraged to attend this open seminar. Intensive study on specific topics in information or data science that are not otherwise covered by courses currently offered at Purdue University. Plan of study and assessment is agreed upon by faculty and student before registration.

Intensive study of selected topics varying from semester to semester, from the practice of information and data sciences. Topics may include data management and organization, digital scholarship, data visualization, computer languages for data and information science, information literacy, archival literacy, and emerging trends in information and data science. Permission of the instructor is required for undergraduates. This course provides an introduction to the process of 3D geometric modeling, and the construction techniques used in the creation of constraint-based solid and surface models.

Part modeling and assembly modeling are included, as well as manipulation of the geometric model. Emphasis is on the use of the design process as a problem solving method, and the capture of modeling behavior to enable the downstream use of 3D slid and surface modeling databases, the role of the 3D model in the overall product design process, and the place of the geometric product definition in the product lifecycle are covered.

This course explores the techniques used in the construction and manipulation of constraint-based solid models and assemblies. Emphasizes extracting data from databases. Downstream applications of data and the impact on overall product design processes are explored. This course is designed for students with little or no background in Data Visualization. It provides an introductory examination of data visualization through lecture, readings and hands-on experience with current visualization tools. Students will obtain an overview of the various types of data, the fundamentals of the visualization process for information and scientific visualization, and examine detail visualizations workflows that aim to answer when temporal data , where geospatial data , what topical data , and with whom trees and networks questions when visualizing data.

After taking the course students will have both the theoretical foundation and practical skills needed to create insightful visualizations for a wide range of data types. This course explores the development of interactive and dynamic media components for web and interactive media products. The course examines the design, creation and integration of 2D animation, 2D games, text, sound, video, programming, and databases for use in web and other interactive media.

Data visualization is the art and science of putting quantitative numbers into pictorial or graphical format that are easy for users to understand, use, and take action upon the data. In this course, we will learn basic data visualization design principles, theories, data management skills, and fundamental web technologies to design and develop web based interactive date visualizations. The students will be able to design the proper visual representations of the data based on the data?

This course will teach students design and develop interactive data visualization systems to communicate and analyze complicated datasets. Students will learn interaction and visual design principles, draw from human perception and cognition theories, and focus on hands-on practice of developing interactive data visualization systems that enable users to see, understand, and analyze complex data and relations. At the end of the class, students will apply the design principles and use proper technologies to create a comprehensive interactive visualization system for data analysis.

This course teaches the use of advanced web technologies to design and develop interactive infographics and data visualizations. Students will apply design principles and use proper web technologies to create professional interactive infographics or information visualization. Permission of Department required. This course introduces information systems development. Topics include types of information systems, system development, database management systems, and problem solving.

Given user requirements students will design, construct, and test a personal computer information system. PC literacy required. Survey course in Bioinformatics for information technology specialists including topics such as: virtual bio-instrumentation, data reduction and mining algorithms and tools, data visualization, pattern matching, modeling and simulation, computational methods, and collaborative application environments. A study of relational database concepts. These concepts include data design, modeling, and normalization; the use of Structured Query Language SQL to define, manipulate, and test the database; programmatic access to a database and practical issues that database developers must handle.

Topics include user interface redesign, structured design, object-oriented programming, and transition from conventional files to databases. Students learn to analyze existing programs to isolate legacy code problems, develop and test solutions, and integrate solutions into software libraries. This course explores advanced database programming techniques for enterprise-wide databases and their implementation. Topics include advanced data manipulation, storage considerations, data transformation techniques to enhance interoperability of data, stored procedure and trigger design and implementation; and query optimization.

This course examines advanced design techniques and physical issues relating to enterprise-wide data management. Topics include advanced design concepts, enhanced modeling and constructs, objects and unstructured and semi-structured data in databases, data management in non-business contexts, implementation of an enterprise data architecture, and data quality and stewardship.

Statistical Mining and Data Visualization in Atmospheric Sciences

This course explores the tools and objectives of research in the medical and life sciences industry relevant to the skills of information technology. The driving outcome of this course is for students to understand the domain demands inherent to information systems in healthcare, bioinformatics and computational life sciences, with respect to their role in commercialism, therapeutic decision support and discovery support systems. Topics include information technology application in support of health care delivery, a brief overview of healthcare delivery, the history of healthcare informatics, an overview of the state of current systems and the professional opportunities in Health Informatics.

In bioinformatics, introduce the concepts of genomics and proteomics, biotechnology, biological databases and file structures, common computational methods for exploiting biological databases, integrating computational methods within the life sciences industry, and a survey of successful computational life science applications. This course examines how the US economy functions and provides an overview of important macroeconomic issues including: unemployment, inflation, social security, national debt, international trade, the sub-prime crisis, and business cycles.

Emphasis is placed upon the role and limits of government fiscal and monetary policy in promoting economic growth and stable prices. This class applies economic principles to the professional sports entertainment industry and its derivative input markets. The class begins by examining the microeconomics of demand for by fans and supply of by teams sports entertainment.

The labor markets for the primary input, athletic talent, receive significant attention. Quantitative empirical analysis is emphasized throughout the class. This is not a sports trivia or fantasy sports strategy class. This course offers a rigorous introduction to macroeconomic theory and empirics with real-world applications. We examine longstanding stylized facts about long-run economic growth, short-run economic fluctuations booms and recessions , and the aggregate effects of government policy.

We study determinants of equilibrium in labor, consumption, investment, and money markets as well as the role of fiscal and monetary policies in closed and open economies. This course examines the statistical techniques economists use to analyze data, estimate causal effects, make predictions, and test economic theory.

It emphasizes estimating multiple regression models including models of firm output, product prices, and wages. Students learn to make statistical and practical inferences about the true causal relationships. The course covers the data analysis skills required for careers as a consultant, financial analyst, researcher, or economist in the private and public sectors. The skills practiced in the course include using statistical software to analyze data. Typically offered Fall, Spring, Summer. This course focuses on quantifying the extent of, and determining the underlying causes of racial and gender disparities in the labor market.

Topics include the impact of prejudice on labor market outcomes, statistical discrimination, early childhood factors and differences in environment, and fertility and reproductive technology. Students learn about human behavior in economic environments, with a strong emphasis on classroom laboratory exercises.

Topics considered include behavior in a variety of markets - for example, markets with price controls, markets for financial assets and auction markets — and behavior in social dilemmas that arise when people try to provide public goods voluntarily or when sellers try to conspire to fix prices.

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Students will also learn how people bargain with, trust each other, and show social preferences towards others. Decision-making and anomalies for risky and uncertain choices will also be covered. Execution by economics honors students of a senior honors thesis under the direction and supervision of the faculty. In addition to a paper, completion of the research project may involve the presentation of the findings in a seminar or workshop setting.

Game theory is a powerful tool that facilitates a decision making process of individuals. Players are decision making units, e. Game theory analyzes situations in which people or other animals interact by breaking these situations down into basic descriptions of the set of players, the strategies available to these players, and the payoffs that the players receive for different combinations of strategy choices. Course content includes money and banking, national income and aggregative economics; the analysis of the determination of national income, employment, the price level, and the balance of payments.

Dimensionality Reduction: High Dimensional Data, Part 1

Consideration of both theory and economic policy. Not open to students with credit in ECON This masters-level course in econometrics covers the tools that will enable students to conduct empirical analysis using economics data. The course examines the statistical techniques used in testing economic theories, estimating casual effects, and making predictions. Emphasis is placed on estimating a single equation e. This course offers an introduction of basic principles of econometric analysis that will help students understand finance theories and their empirical applications.

It will also equip students with appropriate statistical techniques for doing applied financial research. The statistical techniques are particularly well suited for analyzing financial time-series data. This is a graduate-level course in microeconometrics. This course is primarily related to the specification and estimation of various models commonly encountered in microeconomic applications. The course will first briefly cover the theory of maximum likelihood estimation MLE , and apply MLE in some introductory settings.

These methods will then be applied to various latent-variable economic models including binary choice models the logit, probit, and other alternatives , censored regression models e. This course explores human economic behavior, with a strong emphasis on laboratory and field experiment methodology used in behavioral economics research. Topics considered include behavior in markets for financial assets and auction markets, and behavior in social dilemmas that arise when people try to provide public goods voluntarily or increase economic surplus through trust.

Students will also study how people bargain with and exhibit social preferences towards others. Decision-making and anomalies for risky uncertain choices will also be covered. ECON IND — Economics Independent Study Students usually choose an empirical topic of their interest, gather data, and analyze these data using empirical methods from previous courses.

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Topics include generalized method of moments, estimation and inference with weak instruments, panel data models and bootstrap methods. Prerequisite: ECON This course will cover the fundamentals of business rules and logic in a business application development context. Students will acquire these skills using computer programming. A modern programming language, such as Java, will be used to learn and reinforce logical concepts, including abstraction, process flow, variable assignment, and control structures, as well as proper programming and application development practices, including documentation.

Introduction to business statistics as related to facilitating managerial decision making. Topics include descriptive statistics, probability models, estimation, hypothesis testing, and regression analysis. Students use software to do their own analyses. Use of optimization, simulation, and decision theory models to support management decision making. Emphasis on modeling and interpreting results for managerial applications of linear and integer programming models, network problems, simulation models, and decision analysis.

Computer applications are stressed. This case-oriented course is designed to familiarize students with existing and emerging technologies and their business applications. It also covers issues, problems, and opportunities that information systems IS executives and general managers face when managing IS resources in their organizations.

Includes lectures, presentations, case analyses and discussions, and a World Wide Web project. Case discussions cover real situations and deal with the operational and strategic decisions that every IS manager has to make in managing and exploiting the available information technology. Provides an overview of various tools and methods for total quality management. This course covers essential decision models and strategic metrics that form the cornerstone of marketing analytics.

Using the insight gained in the course, students can predict the outcome of marketing plans to boost return on marketing investment ROMI.

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The course emphasizes hands-on learning so students can immediately apply the econometric tools and techniques on a variety of marketing applications. A number of relevant topics are discussed, such as market sizing, forecasting and positioning, promotion budget allocation, and profit maximization. The final piece of this course is guiding the students on how best to craft data-driven presentations to key stakeholders through the use of an experiential learning project provided by an external client. It is a complete problem solving and decision making process, and integrates a broad set of analytical methodologies that enable the creation of business value.

MGMT is highly recommended but not required. Basic Calculus, Statistics, and Excel literacy are required. This course covers up-to-date and practical spreadsheet modeling tools that can be applied to a wide variety of business problems from finance, marketing, and operations. Students follow a structured analytical process using popular industry tools e.

More focus is on descriptive analytics which includes business segmentation and clustering methods, but also introduces two predictive analytic methods, decision trees, and neural networks. Students build and evaluate predictive models to support regression-type and classification-type business problems using popular industry tools e. This course is an introduction to the techniques and algorithms for creating effective data visualization.

Visualizations are graphical depictions of data that can improve comprehension, communication, and decision making. This course emphasizes application in business settings where visual representation methods that are covered in this course will improve our understanding of complex data and models. We focus in particular on pattern identification, difference and trends in data sets across categories, space, and time.

In addition to participating in in-class discussions, students will have sufficient hands-on experience with the tools and techniques covered in this course. Students will be expected to complete several short programming and data analysis assignments. Students will also have an opportunity to work as groups on a final project. This course analyzes the changes in business models that have been enabled by Internet technologies, and exposes students to hands-on data analyses to quantify the impact these technologies and business models have on firms, economies, and people.

Deals with concepts, methods, and applications of decision modeling to address such marketing issues as segmentation, target market selection, new product forecasting, positioning, and resource allocation. Provides skills to translate conceptual understanding into specific operational plans-a skill in increasing demand in organizations today. Using market simulations and related exercises tied to PC-based computer software, students will develop marketing strategy and plans in various decision contexts.

Covers the theory and practice of database design and usage. Students will learn the importance of data modeling concepts and how to use these effectively and how to plan and design a database, including issues such as data security and control. Covers up-to-date and practical spreadsheet modeling tools, which can be applied to a wide variety of business problems in finance, marketing, and operations.

Establishes the link between quality and productivity design and improvement and variance reduction. The course examines some of the more traditional views on quality, as well as those today, which are gaining greater credibility and influence under the umbrella of TQM.

Workshops + conferences

It also covers up-to-date and practical spreadsheet modeling tools that can be applied to a wide variety of business problems from finance, marketing, and operations. The course emphasizes applications of optimization through cases and computer exercises. The applications are chosen to provide insights into business and economics.

Areas covered include linear, network, integer, and nonlinear optimization. At the end of the course, the students should have the ability to model optimization problems work with software to solve optimization problems relate to optimization theory in a variety of application settings develop optimization insights into applications in marketing, finance, and operations and get some basic exposure to EXCEL automation. The purpose of this class is to provide students with a basic understanding of the tasks and challenges facing IT and analytics project managers.

Students learn about projects, roles and responsibilities of project managers. We start by discussing the skills and approaches commonly used in creating and monitoring project plans and braking complex projects down into manageable segments. Next we look at how IT projects have traditionally been managed, and move on to the study of agile methods.

We go through some of the most important aspects of agile project management, such as user stories, agile teams an agile planning, execution and tracking. In learning these tools and techniques, we take a hands-on approach wherever possible and use project management software, exercises and case studies. Open only to a limited number of seniors and graduate students. We will cover ideas that provide insights into, for example, new product development, entrepreneurship or designing HR policies.

time series - Common statistical methods for analyzing weather station data - Cross Validated

In this course, you will examine the mechanisms behind designing for human instincts and thereby developing an understanding of their effective use in the modern firm. Gamification is one form of design for human instincts. To identify effective strategies, and metrics for the application of techniques to business, this course will draw upon interdisciplinary source material as well as real-world case studies. It will also identify a number of significant pitfalls to techniques, as well as notable legal and ethical issues, and the problems with implementing radical change in established firms.

As a part of this class, you will be designing, playing, and evaluating various games. Symeonidis Pericles A. Leendert M. Wolfgang Hardle Zdenek Hlavka. Guozhu Dong Jian Pei. Text and Context. Krzysztof J. Cios Witold Pedrycz Roman W. Swiniarski Lukasz Kurgan. John A. Lee Michel Verleysen. Fundamentals of Data Mining in Genomics and Proteomics. Dianne Cook Deborah F.

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