Concepts and structures relating to design, implementation, and administration of database management systems. Emphasis on relational databases and development of integrated applications. prereq: FMIS 2201 or MIS 2201 or CS 1121 or CS 1511, LSBE candidate or non-LSBE MIS minor or college consent; credit will not be granted if already received for FMIS 3220

Data visualization is the art and science of presenting data effectively in order to facilitate knowledge sharing and decision making. How to present and visualize data is an important skill for business professions to develop. This course will teach the principles and techniques that empower students to understand and interpret data, as well as make effective decisions based on data. Students will learn the benefits of effective data presentation and visualization, understand the principles and methods of visualization, and apply the principles using popular data visualization technologies. pre-req: FMIS 2201 or MIS 2201, LSBE candidate or Business Analytics minor, no grad credit, credit will not be granted if already received for MIS 3231

Data visualization is the art and science of presenting data effectively in order to facilitate knowledge sharing and decision making. How to present and visualize data is an important skill for business professions to develop. This course will teach the principles and techniques that empower students to understand and interpret data, as well as make effective decisions based on data. Students will learn the benefits of effective data presentation and visualization, understand the principles and methods of visualization, and apply the principles using popular data visualization technologies. Students enrolled in the 5410 version of the course will have to fulfill an extra assignment/project to earn graduate credit. pre-req: FMIS 2201 or MIS 2201, LSBE candidate or Business Analytics minor, credit will not be granted if already received for MIS 3231

This course introduces the basic elements of business analytics and how to analytically think about data and its role in business. The goal of the course is to provide students with the toolset and capabilities as they analyze data to ask the right questions that matter to businesses and help solve business problems. Topics include data preprocessing, exploratory data analysis (EDA), predictive analytics, modeling and model evaluation. The course is designed to trigger passion for analytics, develop data-analytic thinking demonstrate how analytics matter in different business domains, illustrate real-world examples in different business contexts while working hands-on using data analytics is as such an art as it is a science. pre-req: MIS 2201, MIS 3220, ECON 2030, LSBE candidate or Business Analytics minor, no grad credit. Credit will not be granted if already received for MIS 3241, MIS 4241, CIA 3760 or CIA 4761 or CIA 5761

This course introduces the basic elements of business analytics and how to analytically think about data and its role in business. The goal of the course is to provide students with the toolset and capabilities as they analyze data to ask the right questions that matter to businesses and help solve business problems. Topics include data preprocessing, exploratory data analysis (EDA), predictive analytics, modeling and model evaluation. The course is designed to trigger passion for analytics, develop data-analytic thinking demonstrate how analytics matter in different business domains, illustrate real-world examples in different business contexts while working hands-on using data analytics is as such an art as it is a science. Students enrolled in the 5420 version of the courses will have to fulfill an extra assignment/project to earn graduate credit. pre-req: MIS 2201, ECON 2030, LSBE candidate or Business Analytics minor. Credit will not be granted if already received for MIS 3241, MIS 4241, CIA 3760 or CIA 4761 or CIA 5761

This course is a practical introduction to mathematical spreadsheet models with an emphasis on predictive and prescriptive analytics for making business decisions. Concepts covered include data exploration and slicing and diving data using spreadsheets, optimization, sensitivity analysis, network modeling, simulation, regression, decision analysis, cluster analysis, and time series forecasting. Students are expected to communicate insights from the analysis in written and oral formats appropriate for a general audience. pre-req: MIS 2201, ECON 3020, LSBE candidate or Business Analytics minor or instructor approval, no grad credit

This course is a practical introduction to managing big data in the enterprise and covers aspects of technology infrastructure, data warehousing and structuring data for use in the organization. Using state-of-the-art open source big data ecosystems and cloud resources for data acquisition, extraction, cleansing, transformation and loading, the course demonstrates how the ecosystem integrates with other analytic tools to provide solutions for practical use cases. pre-req: MIS 3220 or equivalent, LSBE candidate or Business Analytics minor or instructor consent, no grad credit

A second introductory statistics course including more advanced topics. Topics include hypothesis testing, analysis of variance, and introduction to correlation and regression. pre-req: LSBE Candidate and one of the following courses: ECON 2030, POL 2700, PSY 3020, SOC 3155, STAT 1411, STAT 2411, STAT 3411 or STAT 3611.

Introduction to the types, use, and analysis of data in health services delivery and research. This includes electronic health record, claims, and patient satisfaction data, as well as publicly available data sets. Topics include data organization, data sources available in the health services, conceptualizing analysis, sampling, data validity and reliability, qualitative and quantitative data analysis, applying research results, and communicating findings. prereq: 4520 or instructor consent, no grad credit

This course is intended for mathematics or statistics majors. The focus of the course is solving industrial mathematics research problems. Students will work in teams of three to five on a semester-long research problem from business, industry or government. Students will acquire specialized mathematical knowledge specific to the research problems posed for the semester. In addition, students will develop problem solving, teamwork, and communication skills as they design and implement a solution strategy for one of the research problems. A business, industry or government partner will serve as a liaison for project teams. Presentation to professional partners will occur throughout the semester. A final solution product will include oral, written and video presentations. pre-req: Minimum 2 courses in MATH or STATS at about about the 3xxx level, with a minimum of 3 credits each, instructor consent; no grad credit

This course provides students with a broad range of modeling techniques, methods and tools used in problem solving and decision making to help them develop the competence and skills necessary to navigate challenging situations and become effective decision makers. Topics covered include principles for problem solving, decision analysis with uncertainty (e.g. multi-attribute utility models, decision trees, and Bayesian models), utility and game theory, linear and nonlinear programming, dynamic programming, distribution and network optimization models, Markov decision processes, advanced optimization, etc. Students are expected to communicate insights from the application of decision models and the analysis in written and oral formats appropriate for a general audience. pre-req: pre-req: MGTS 3301 or IE 3115, or IE 5315 or EE 3151 or MATH 5810 or STAT 4531 or STAT 5531, or instructor consent; no grad credit

Human Resource (HR) analytics is a sector within the field of human resource management that aims at using measurement and analysis techniques to understand, improve, and optimize the people side of the business. HR analytics adds value to businesses by improving vital decisions about talent and how it is organized in organizations. This course will teach the analytical foundations of HR decisions, the connections between data analytics and strategic HRM, and the applications of analytic logic and processes of various HR functions and workplace trends. Students will learn how to gather and analyze pertinent HR metrics and how to properly communicate findings to support HR decisions and drive organizational decisions. pre-req: MGTS 3801, LSBE candidate or HRM minor; no grad credit

Sales Analytics introduces students to the foundation metrics used in Business to Business and other sales environments. Students use excel to manage and summarize data sets, analyze product category and brand trends, and assess the impacts of various trade promotions. Students develop business insights from the data sets and use these insights to build compelling sales presentations. The course focuses on the use of data sets typical to consumer packaged goods industries but will also integrate data from other sources including: the US Census, other government surveys and Experian Simmons Oneview. pre-req: MKTG 3701 and Professional Sales Major

Statistical, graphical and numerical data analysis using modern statistical software. Database management and statistical modeling including regression and categorical data analysis. Topics in data generation and simulation. prereq: A grade of at least C- in STAT 3411 or 3611 or instructor consent.

Introduction to statistical methods applicable to biological and biomedical data. Analysis of bioassay, case-control, and disease/expose data. Introduction to statistics in clinical trials. Use of regression and logistic regression in analyzing biological/biomedical data. Categorical data analysis with application to the life sciences. Basic survival analysis. prereq: Math 1290 or 1296 or 1596 and STAT 2411 or 3411 or 3611 with grade of C- or better or consent of instructor.

Elementary functions, matrices, graphical and algebraic methods for solving systems of linear equations and inequalities, introduction to linear programming, and abbreviated treatment of calculus with emphasis on business and social science applications. prereq: Math ACT 24 or higher or a grade of at least C- in Math 1005 or department consent; if you have received credit for 1290 or 1296 or 1596, you will not receive credit for Math 1160.

First part of a standard introduction to calculus of functions of a single variable. Limits, continuity, derivatives, integrals, and their applications. prereq: Math ACT 27 or higher or a grade of at least C- in Math 1250 or department consent

A comprehensive introduction to computer programming using the C++ language. The course covers program design, C++ programming basics, control structures, functions and parameter passing. Students write and implement programs with data structures (arrays), pointers and files. Object-oriented programming is also introduced, along with concepts of abstraction, ADTs, encapsulation and data hiding. prereq: 3 1/2 yrs high school math or instructor consent

Introduction to information technology (IT) concepts: computer hardware and software; use of personal productivity tools (spreadsheet, database, and presentation software); system development processes; Web technologies; applications of IT in business processes. prereq: LSBE major or minor student or Graphic Design and Marketing major or Graphic Design with Marketing subplan major or Computer Information Systems majors or minors, or Arts Administration, minimum 15 credits or college consent; credit will not be granted if already received for FMIS 2201

Survey of Accounting provides an overview of fundamental concepts and procedures in financial and managerial accounting. The emphasis is on helping students to develop a basic understanding of the contexts of accounting reports provided to decision makers. Credit cannot be applied toward the BAcc or BBA degree programs or the Accounting minor.

Introduction to context, environment, and operation of business and organizations. Study of foundations and functional areas of business and entrepreneurship. Analysis of technological, ethical, diversity, and global issues from business and organizational perspectives.

Introduction to modern business statistics, emphasizing problem solving applications through statistical decision making using case studies. Topics include organization and presentation of data, summary statistics, distributions, statistical inference including estimation, and hypothesis testing. prereq: minimum 30 credits, LSBE student, pre-business or pre-accounting or Econ BA major or Graphic Design and Marketing major or Graphic Design with Marketing subplan major or Econ minor or Accounting minor or Business Admin minor or Arts Administration; credit will not be granted if already received for Econ 2020, Stat 1411, Stat 2411, Stat 3611, Soc 3151, Psy 3020

Descriptive statistics; introduction to correlational analysis and regression; sampling techniques and statistical inference; applications of simple and factorial design analysis of variance and other parametric and nonparametric hypothesis-test statistics in the behavioral sciences. prereq: Math ACT 21 or higher or Accuplacer score of 237-300 or higher or MATH 1005

Descriptive statistics. Measures of central tendency, deviation, association. Inferential statistics focusing on probability and hypothesis testing. T-tests, Chi-square tests, analysis of variance, measures of association, introduction to statistical control. Statistical software (SPSS) used to analyze sociological data. Lab. prereq: 2155, crim major or soc major or URS major, min 30 cr

Statistical ideas involved in gathering, describing, and analyzing observational and experimental data. Experimental design, descriptive statistics, correlation and regression, probabilistic models, sampling, and statistical inference. prereq: Math ACT 21 or higher or a grade of at least C- in MATH 0103 or department approval

Graphical and numerical descriptions of data, elementary probability, sampling distributions, estimations, confidence intervals, one-sample and two-sample t-test. prereq: Math ACT 24 or higher or a grade of at least C- in Math 1005 or higher or department approval

Statistical considerations in data collection and experimentation. Descriptive statistics, least squares, elementary probability distributions, confidence intervals, significance tests, and analysis of variance as applied analysis of engineering data. prereq: MATH 1297 with a grade of C- or better, cannot be applied to a math or statistics major

Basic probability, including combinatorial methods, random variables, mathematical expectation. Binomial, normal, and other standard distributions. Moment-generating functions. Basic statistics, including descriptive statistics and sampling distributions. Estimation and statistical hypothesis testing. prereq: A grade of at least C- in Math 1290 or Math 1296

General description of U.S. economy and analysis of contemporary economic problems. Introduction to major economic issues and problems of the day, providing a simple framework used by economists for analysis. prereq: Cannot apply credit to economics major or minor or BAc or BBA majors

Analyzing overall performance of an economic system. National income accounting and theory, unemployment, inflation, fiscal policy, money, monetary policy, economic growth, international trade, non-U.S. economies, and real-world application of these concepts. prereq: Minimum 15 credits or department consent

Analyzing free enterprise system through study of product and resource markets. Supply and demand, utility, production and cost, market structure, resource use, market failures, regulatory role of government, and real-world application of these concepts. prereq: Minimum 15 credits or department consent

Concepts and structures relating to design, implementation, and administration of database management systems. Emphasis on relational databases and development of integrated applications. prereq: FMIS 2201 or MIS 2201 or CS 1121 or CS 1511, LSBE candidate or non-LSBE MIS minor or college consent; credit will not be granted if already received for FMIS 3220

Data visualization is the art and science of presenting data effectively in order to facilitate knowledge sharing and decision making. How to present and visualize data is an important skill for business professions to develop. This course will teach the principles and techniques that empower students to understand and interpret data, as well as make effective decisions based on data. Students will learn the benefits of effective data presentation and visualization, understand the principles and methods of visualization, and apply the principles using popular data visualization technologies. pre-req: FMIS 2201 or MIS 2201, LSBE candidate or Business Analytics minor, no grad credit, credit will not be granted if already received for MIS 3231

Data visualization is the art and science of presenting data effectively in order to facilitate knowledge sharing and decision making. How to present and visualize data is an important skill for business professions to develop. This course will teach the principles and techniques that empower students to understand and interpret data, as well as make effective decisions based on data. Students will learn the benefits of effective data presentation and visualization, understand the principles and methods of visualization, and apply the principles using popular data visualization technologies. Students enrolled in the 5410 version of the course will have to fulfill an extra assignment/project to earn graduate credit. pre-req: FMIS 2201 or MIS 2201, LSBE candidate or Business Analytics minor, credit will not be granted if already received for MIS 3231

This course introduces the basic elements of business analytics and how to analytically think about data and its role in business. The goal of the course is to provide students with the toolset and capabilities as they analyze data to ask the right questions that matter to businesses and help solve business problems. Topics include data preprocessing, exploratory data analysis (EDA), predictive analytics, modeling and model evaluation. The course is designed to trigger passion for analytics, develop data-analytic thinking demonstrate how analytics matter in different business domains, illustrate real-world examples in different business contexts while working hands-on using data analytics is as such an art as it is a science. pre-req: MIS 2201, MIS 3220, ECON 2030, LSBE candidate or Business Analytics minor, no grad credit. Credit will not be granted if already received for MIS 3241, MIS 4241, CIA 3760 or CIA 4761 or CIA 5761

This course introduces the basic elements of business analytics and how to analytically think about data and its role in business. The goal of the course is to provide students with the toolset and capabilities as they analyze data to ask the right questions that matter to businesses and help solve business problems. Topics include data preprocessing, exploratory data analysis (EDA), predictive analytics, modeling and model evaluation. The course is designed to trigger passion for analytics, develop data-analytic thinking demonstrate how analytics matter in different business domains, illustrate real-world examples in different business contexts while working hands-on using data analytics is as such an art as it is a science. Students enrolled in the 5420 version of the courses will have to fulfill an extra assignment/project to earn graduate credit. pre-req: MIS 2201, ECON 2030, LSBE candidate or Business Analytics minor. Credit will not be granted if already received for MIS 3241, MIS 4241, CIA 3760 or CIA 4761 or CIA 5761

This course is a practical introduction to mathematical spreadsheet models with an emphasis on predictive and prescriptive analytics for making business decisions. Concepts covered include data exploration and slicing and diving data using spreadsheets, optimization, sensitivity analysis, network modeling, simulation, regression, decision analysis, cluster analysis, and time series forecasting. Students are expected to communicate insights from the analysis in written and oral formats appropriate for a general audience. pre-req: MIS 2201, ECON 3020, LSBE candidate or Business Analytics minor or instructor approval, no grad credit

This course is a practical introduction to managing big data in the enterprise and covers aspects of technology infrastructure, data warehousing and structuring data for use in the organization. Using state-of-the-art open source big data ecosystems and cloud resources for data acquisition, extraction, cleansing, transformation and loading, the course demonstrates how the ecosystem integrates with other analytic tools to provide solutions for practical use cases. pre-req: MIS 3220 or equivalent, LSBE candidate or Business Analytics minor or instructor consent, no grad credit

A second introductory statistics course including more advanced topics. Topics include hypothesis testing, analysis of variance, and introduction to correlation and regression. pre-req: LSBE Candidate and one of the following courses: ECON 2030, POL 2700, PSY 3020, SOC 3155, STAT 1411, STAT 2411, STAT 3411 or STAT 3611.

Introduction to the types, use, and analysis of data in health services delivery and research. This includes electronic health record, claims, and patient satisfaction data, as well as publicly available data sets. Topics include data organization, data sources available in the health services, conceptualizing analysis, sampling, data validity and reliability, qualitative and quantitative data analysis, applying research results, and communicating findings. prereq: 4520 or instructor consent, no grad credit

This course is intended for mathematics or statistics majors. The focus of the course is solving industrial mathematics research problems. Students will work in teams of three to five on a semester-long research problem from business, industry or government. Students will acquire specialized mathematical knowledge specific to the research problems posed for the semester. In addition, students will develop problem solving, teamwork, and communication skills as they design and implement a solution strategy for one of the research problems. A business, industry or government partner will serve as a liaison for project teams. Presentation to professional partners will occur throughout the semester. A final solution product will include oral, written and video presentations. pre-req: Minimum 2 courses in MATH or STATS at about about the 3xxx level, with a minimum of 3 credits each, instructor consent; no grad credit

Human Resource (HR) analytics is a sector within the field of human resource management that aims at using measurement and analysis techniques to understand, improve, and optimize the people side of the business. HR analytics adds value to businesses by improving vital decisions about talent and how it is organized in organizations. This course will teach the analytical foundations of HR decisions, the connections between data analytics and strategic HRM, and the applications of analytic logic and processes of various HR functions and workplace trends. Students will learn how to gather and analyze pertinent HR metrics and how to properly communicate findings to support HR decisions and drive organizational decisions. pre-req: MGTS 3801, LSBE candidate or HRM minor; no grad credit

Sales Analytics introduces students to the foundation metrics used in Business to Business and other sales environments. Students use excel to manage and summarize data sets, analyze product category and brand trends, and assess the impacts of various trade promotions. Students develop business insights from the data sets and use these insights to build compelling sales presentations. The course focuses on the use of data sets typical to consumer packaged goods industries but will also integrate data from other sources including: the US Census, other government surveys and Experian Simmons Oneview. pre-req: MKTG 3701 and Professional Sales Major

Statistical, graphical and numerical data analysis using modern statistical software. Database management and statistical modeling including regression and categorical data analysis. Topics in data generation and simulation. prereq: A grade of at least C- in STAT 3411 or 3611 or instructor consent.

Introduction to statistical methods applicable to biological and biomedical data. Analysis of bioassay, case-control, and disease/expose data. Introduction to statistics in clinical trials. Use of regression and logistic regression in analyzing biological/biomedical data. Categorical data analysis with application to the life sciences. Basic survival analysis. prereq: Math 1290 or 1296 or 1596 and STAT 2411 or 3411 or 3611 with grade of C- or better or consent of instructor.