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Introduction to statistical inference, estimation and hypothesis testing. Includes summary measures, probability, random variables and their distributions, sampling distributions, elements of Bayesian decision theory, linear regression and correlation, and time series analysis. Uses commercial statistical packages to perform statistical data analysis. Prerequisite(s): .

This course is designed to provide students with an understanding of the fundamentals of Python programming to prepare them for the growing demand for these skills in modern business. This course will use Python Notebooks to introduce students to important Python packages essential for data analysis, such as Numpy, Pandas, Matplotlib, Scikit-learn, etc. Students will learn how to program in Python; perform scientific computations; prepare, manipulate, transform, and clean data; create descriptive statistics; visualize different types of data; and use the data to create analytical models. Upon successful completion of this course, students should be skillful with python programming for analytics with a solid foundation for further study in data science and a competitive edge in the contemporary workplace.

Prerequisite(s): Graduate Status or ECON 231 and ECON 300 level class or MIS 310

This class is designed for students with little to no business knowledge, who are interested in pursuing a graduate degree or profession in an Analytics field. It is intended to provide these students with an introduction to key business topics that include business culture, communication, finance and accounting, marketing, operations management and supply chain, effective teams, and strategic thinking. The class delivery will focus on critical thinking, teamwork, business writing, and presentation skills.

Prerequisite(s): Not open for MBA students or undergraduate business majors from an accredited US university

Offers an overview of business analytics and its relationship to data analytics and data science. Covers the ethical issues surrounding the use of data in analytics and the different analytics models at the descriptive (includes visualization), predictive and prescriptive levels. The emphasis is on business problems in various disciplines (operations, supply chain, finance, marketing, human resources, etc.). Students are exposed to various technologies available for analytics (beyond Excel), including Tableau and open-source software. Topics covered in the course assist students, regardless of their background, understand a problem, frame the problem, select the proper analytical model, select the technology to use, run the model, analyze the results, and communicate them in a professional and effective manner. The course also includes case analysis, a term project, and discussions of emerging topics and trends in analytics.

Introduction to databases, data warehouses, data mining processes, and techniques (e.g., predictive machine-learning models and clustering), simple text mining techniques (e.g., sentiment analysis and topic modeling) and data mining approaches for big data (e.g., MapReduce and the Hadoop ecosystem). The course focuses on the application of these techniques more than theoretical considerations. The techniques and material are presented and demonstrated using Jupyter notebooks in the Python programming language.

Remove data cleaning and prep and add more about databases

Prerequisite(s): BSAN 710 or equivalent, or instructor's consent.

Covers advanced machine learning, natural language processing and deep learning techniques that are relevant to business applications involving high dimensional data sets, unstructured data or other complex data sets. Supervised learning, unsupervised learning, transfer learning and feature representation are all introduced in the context of real-world problems. Methods covered include deep neural networks, transformer language models, multimodal models, recurrent neural networks, convolutional neural networks, clustering, dimensionality reduction, decision trees, support vector machines and ensembles. Students use premade Jupyter and Colab notebooks (with packages such as pandas, scikitlearn, Keras, Hugging Face, and Tensorflow) to apply these techniques on topics ranging from marketing to finance to social media analytics. The assignments and project focus on applying the techniques via the provided notebooks rather than coding the models from scratch.

Prerequisite(s): BSAN 734 or CS 746 or equivalent or instructor's consent.

Provides an opportunity for students to work on a project that draws on the skills learned from descriptive, predictive, and prescriptive analytics modeling to frame a business problem, work effectively with data, visualize data, and use statistical, machine-learning, or optimization models to support data-driven decision-making processes. Whenever possible, projects are based on real business problems faced by organizations in the business community. The capstone project also furthers student skills in developing business insight from quantitative analysis, knowledge of functional areas in business or/and specific industries, managing a project from start to finish, communicating with stakeholders, and using storytelling to present the final project.

Prerequisite(s) or Co-requisite: BSAN 735 or BSAN 875 or instructor's consent.

Introduces basic mathematical tools and principles for data analysis techniques used in analyzing data sets. Topics include matrix decomposition, gradient descent, continuous optimization, linear regression, dimension reduction and clustering. For students to be successful in this course, basic calculus and statistics knowledge is needed prior to enrolling. Prerequisite(s): departmental consent.

Covers basic mathematical techniques for analyzing data sets. Uses object oriented programming, like Python or R, to show how to organize, visualize and analyze large data. For students to be successful in this course, basic programming knowledge is needed prior to enrolling. Prerequisite(s): , 571, or instructor's consent.

Covers the perspectives and fundamentals of data science. Topics include data collection, preprocessing, transformation, exploratory data analysis, visualization, predictive modeling, descriptive modeling, clustering, regression and classification and data science project life cycle. This course is limited to engineering students and students in other colleges majoring in data science/analytics related programs. Prerequisite(s):  and  for undergraduate students; instructor's consent for graduate students.

Cross-listed as . Provides students with an understanding of what Enterprise Resource Planning (ERP) systems are (also known as Enterprise Systems). ERPs are designed to assist an organization with integrating and managing its business processes by moving away from numerous disintegrated and costly legacy systems towards one main IT system for the organization. ERPs are a critical component of an organization鈥檚 IT strategy because they integrate many functions in business including operations, supply chain, sales, distribution and accounting. The course provides a technical overview of ERP systems and their managerial impact on organizations. SAP is introduced to illustrate the concepts, fundamentals, framework, information technology context, technological infrastructure and integration of business enterprise-wide applications. Latest technological trends in the ERP market are discussed. Additional accompanying software is introduced, as time permits.

Cross-listed as . Introduces data visualization principles and prepares managers for developing and implementing digital performance dashboards to monitor business processes and make informed decisions. Covers a broad category of data visualization strategies for descriptive data analysis, visual data analysis and design choices. Emphasizes the importance of using big data and insightful visualizations to improve the business decision-making process. Hands-on projects with the use of modern data visualization software are included.

Introduces advanced analytical techniques for different types of practical business problems. Students will use PowerBI to combine data from multiple sources and create meaningful visualizations and dashboards to assist in managerial decision making. Students will further build upon their knowledge of predictive and prescriptive analytics to learn advanced models including machine learning, optimization and simulation. Simulation is useful for decision making under uncertainty (e.g. disruptions in the supply chain) because it introduces risk that needs to be measured and planned for to make data driven decisions. Finally, students will learn how to use different software and python packages for predictive and prescriptive analytics.  

笔谤别谤别辩耻颈蝉颈迟别(蝉):鈥�BSAN鈥�710 and BSAN 775鈥痮r equivalent, or instructor's consent. 

Introduces various methodologies for conceptual data modeling including entity-relationship data modeling and logical database design. Covers relational database management systems, the SQL standard and data administration issues. Students obtain hands-on development with SQL servers in a client/server environment through a required database programming project. Covers topics of data warehousing, data mining, distributed database management and emerging topics in database areas.

Prepares students to deal with issues in planning and managing organization-wide integrated databases. Emphasizes logical database design and relational database implementation. Includes SQL, assuring database integrity, database conversion, database administration and data management.

The cloud market is rapidly evolving, and with many technologies available for cloud, it is a difficult task for IT professionals to make decisions for their companies about how to move to cloud. In this course, students learn the complete basics of the cloud ecosystem, explore applications in the cloud, and receive a detailed overview of cloud platforms including Amazon Web Services and Microsoft Azure. By the end of this course, students know what cloud computing is all about and are ready to apply that knowledge to solve real world case studies and scenarios. Prerequisite(s): junior standing, advanced standing.

Surveys contemporary issues in finance, introduces selective finance topics/methods that use analytics, and enables students to learn SAS software to conduct basic descriptive and prescriptive analysis of stock market data. The survey of contemporary finance issues includes topics such as digitalization of cash (i.e., cryptocurrencies and underlying block chain technology), payment systems and trading platforms. The selective topics in traditional finance analytics include portfolio optimization, binomial option pricing. SAS programming includes quantitative finance research methods such as event study, portfolio construction and testing using SAS. Prerequisite(s):  with a grade of C (2.000) or FIN 803 (or equivalent).

Studies the theory and practice of security valuation and investment management. Includes portfolio analysis, asset allocation, fixed income securities and term structure, equity analysis, derivatives and measurement of performance. Prerequisite(s):  or equivalent.

Provides a graduate-level introduction to methods in data science and big data analytics with engineering applications. Specifically, examines some widely used statistical methods and machine learning tools for big data (data with high volume, velocity and variety). A variety of up-to-date industrial engineering topics are covered as application examples. Prerequisite(s): basic engineering statistics and programming skills.

Introduces the theory and practical applications of artificial neural networks and machine learning. Covers several network paradigms, emphasizing the use of neural networks as a solution tool for industrial problems which require pattern recognition, predictive and interpretive models, pattern classification, optimization and clustering. Covers machine learning. Presents examples and discusses them from a variety of areas including quality detection, process monitoring, robotics, simulation metamodeling, diagnostic models, combinatorial optimization and machine vision. For students from a variety of disciplines.

A study of the concepts, components and technologies of CIM systems; enterprise modeling for CIM, local area networks, CAD/CAM interfaces, information flow for CIM, shop floor control and justification of CIM systems. Prerequisite(s): knowledge of a programming language, .

A study of business decision making and problem solving methodologies including problem definition, research design, data gathering techniques, analytical techniques, reporting strategies and communication issues. Prerequisite(s):  or equivalent, MBA 801 or equivalent.

An application of the scientific method to the design and implementation of research procedures that support the need for management decision making, planning and strategy development in the marketplace. Prerequisite(s): MBA 801 or equivalent.

Intensive study of research methodologies and forecasting for real life business decision making. Covers formulation of research questions, specification of models, collection of time series and survey data, applications of forecasting techniques, and interpretation and communication of the results. Prerequisite(s):  or instructor's consent.

Covers topics on time series regression models, forecasting and smoothing, exploratory data analytics, predictive analytics and modeling, and ARIMA models. Students will use R program to model predictive analytics problems. Prerequisite(s):  and  or instructor鈥檚 approval.