Carl H. Lindner College of BusinessCarl H. Lindner College of BusinessUniversity of Cincinnati

Carl H. Lindner College of Business

Business Analytics

BANA1050 Sports by the Numbers. This course will serve as an introduction to sports analytics for students of all backgrounds. We will use ideas popularized from such readings as Moneyball and similar references on sports analytics to examine questions such as: "Who is the best QB in the NFL? Is there such a thing as home field advantage? And if so, why? Should football coaches go for it more on 4th down?" This course will use very simple math and an assortment of popular readings to demonstrate the power of analytics to analyze sports. Students will not need any advanced skills above basic algebra to understand the concepts in this course. Credit Level:U Credit Hrs:3 Baccalaureate Competency: Critical Thinking.  Recent Syllabus

BANA2071 Fundamentals of Statistics. Principles and techniques of collecting, analyzing, and interpreting quantitative data. Topics include descriptive statistics, continuous probability distributions, interval estimation, hypothesis testing involving means, proportions, independence, and linear regression. This course is intended for the Associate of Applied Business (AAB) program, and does not apply toward a Bachelors in Business Administration (BBA). Credit Level:U Credit Hrs:3

BANA2080 Business Statistics. This course introduces statistical thinking and statistical methods to business students. Topics include descriptive statistics, data visualization, probability distributions, sampling, confidence intervals, hypothesis testing and linear regression. Credit Level:U Credit Hrs:5 Pre-req: See your college advisor for details. Baccalaureate Competency: Critical Thinking. 

BANA2081 Business Analytics I. This course develops fundamental knowledge and skills for applying statistics to business decision making. Topics include descriptive statistics, probability distributions, sampling, confidence intervals and hypothesis testing and the use of computer software for statistical applications. Credit Level:U Credit Hrs:3 Pre-req: See your college advisor for details. BoK:QR. TouchPoint:MidCollegiate Baccalaureate Competency: Critical Thinking, Effective Communication.     Recent Syllabus

BANA2082 Business Analytics II. This course is a continuation of BANA 2081. It further develops fundamental knowledge and skills for applying statistical and management science models to business decision making. Topics include simple and multiple linear regression, contingency tables, chi-square tests, ANOVA, decision analysis, simulation and risk models and optimization models, including the use of software for business applications. Credit Level:U Credit Hrs:3.  Pre-req: See your college advisor for details. BoK:QR. TouchPoint: MidCollegiate Baccalaureate Competency: Critical Thinking, Effective Communication.  Recent Syllabus

BANA3060 Sports Analytics. This course examines the use of analytics in sports. The course will introduce a variety of analytics methods and problem solving methodologies using sports applications as motivating examples. The goal is to help students become more familiar and more interested in problem solving and quantitative methods. Many students already spend much of their time following and participating in sports. We will use sports examples to introduce the power and relevance of formal problem solving and quantitative methods. We will use mathematical techniques from statistics, economics and operations research in our analysis. Previous background in statistics will be helpful. Credit Level:U Credit Hrs:3 Baccalaureate Competency: Critical Thinking, Effective Communication.

BANA4080 Data Mining and Analysis. The study of data mining and analysis techniques as applied to problems in business and industry. Topics may include, but are not limited to, data visualization, advanced regression techniques, neural networks, cluster analysis, classification, discriminant analysis, and predictive modeling. Credit Level:U Credit Hrs:3 Pre-req: See your college advisor for details. Baccalaureate Competency: Critical Thinking, Effective Communication, Information Literacy, Knowledge Integration, Social Responsibility. Recent Syllabus

BANA4085 Spreadsheet Analytics. This course is designed to advance analytical skills for business decision making in the spreadsheet environment. Topics include modeling techniques, spreadsheet auditing, advanced spreadsheet functions, data management, data visualization, optimization, risk analysis and predictive modeling in spreadsheet software. Credit Level:U Credit Hrs:3 Pre-req: See your college advisor for details. Baccalaureate Competency: Critical Thinking, Effective Communication, Information Literacy, Knowledge Integration, Social Responsibility. Recent Syllabus

BANA4090 Forecasting and Risk Analysis. A survey of analytical techniques used to assist in managing under uncertainty. Topics include time series and other forecasting techniques, as well as Monte Carlo simulation to assess the risk associated with managerial decisions. Credit Level:U Credit Hrs:3 Pre-req: See your college advisor for details. Baccalaureate Competency: Critical Thinking, Effective Communication, Information Literacy, Knowledge Integration, Social Responsibility.  Recent Syllabus

BANA4095 Decision Models. This course further develops fundamental knowledge and skills for applying analytical tools to business decision making. Topics include optimization models and simulation models including the use of software to develop these models. Credit Level:U Credit Hrs:3 Pre-req: See your college advisor for details. Baccalaureate Competency: Critical Thinking, Knowledge Integration.

BANA5099 Independent Study in Business Analytics. This is a self-managed course during which student independently pursues topics and/or completes a project of personal interest within this subject area. Students must obtain a faculty supervisor and appropriate permission prior to registration. Credit Level:U Credit Hrs:1 - 6 Pre-req: See your college advisor for details. Baccalaureate Competency: Critical Thinking, Effective Communication, Knowledge Integration.

BANA5137 Data Visualization. This course provides an introduction as well as hands-on experience in data visualization. It introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making.  BANA 5137 is the undergraduate course-credit equivalent of BANA6037. Credit Level:U Credit Hrs:2 Pre-req: See your college advisor for details. Baccalaureate Competency: Critical Thinking, Effective Communication, Information Literacy. Recent Syllabus

BANA5143 Statistical Computing. This is a course on the use of computer tools for data management and analysis. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. Data management and manipulation techniques including queries in SQL will be covered. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses. BANA 5143 is the undergraduate course-credit equivalent of BANA6043. Credit Level:U Credit Hrs:2 Pre-req: See your college advisor for details. Baccalaureate Competency: Critical Thinking, Information Literacy. Recent Syllabus

BANA5144 Applications Development using VBA. The use of visual basic for applications for the development of applications of management science models for planning and decision support in a spreadsheet environment. BANA 5144 is the undergraduate course-credit equivalent of BANA6044. Credit Level:U Credit Hrs:2 Pre-req: See your college advisor for details. Baccalaureate Competency: Critical Thinking, Information Literacy.

BANA5150 Bracketology. This course is designed for students with comfort in Excel spreadsheets, probability and statistics and a keen interest in NCAA basketball. We will study the NCAA basketball tournament and use the actual tournament as a test site. We will discuss the history of the NCAA tournament and how it currently operates. We will include the business side of the tournament as well. Discussions of how teams are currently evaluated will include the make-up of the selection committee and their deliberations. Analytics that have previously been applied to the tournament will focus on simulation capabilities, counting and probability models and decision analysis. Students will be broken into groups with the ultimate goal of coming up with actual brackets of teams that will be in the tournament. Groups will be asked to present and justify their brackets. Group brackets will be compared to the actual bracket. BANA 5150 is the undergraduate course-credit equivalent of BANA6050. Credit Level:U Credit Hrs:2 Pre-req: See your college advisor for details. Baccalaureate Competency: Critical Thinking, Effective Communication, Information Literacy.

IS6030 Data Management. This course provides an introduction to the use and design of databases to store, manipulate and query data. The course introduces the structured query language (SQL) used to manage data. Students who complete this course should understand how to use SQL for basic data manipulation and queries. This course is intended for users of existing databases to extract needed information and should not be taken by MSIS students or those students who wish to learn detailed database design techniques. Credit Level: G Credit Hrs:2 Recent Syllabus

BANA6037 Data Visualization. This course provides an introduction as well as hands-on experience in data visualization. It introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making. Credit Level: G Credit Hrs:2. Pre-req: See your college advisor for details. Recent Syllabus

BANA6043 Statistical Computing. This is a course on the use of computer tools for data management and analysis. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. Data management and manipulation techniques including queries in SQL will be covered. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses. Credit Level: G Credit Hrs:2. Pre-req: See your college advisor for details. Recent Syllabus

BANA6044 Applications Development using VBA. The use of visual basic for applications for the development of applications of management science models for planning and decision support in a spreadsheet environment. Credit Level: G Credit Hrs:2 Pre-req: See your college advisor for details.  Recent Syllabus

BANA6050 Bracketology. For non-MS-BANA graduate students with comfort in Excel spreadsheets, probability and statistics and a keen interest in NCAA basketball. We will study the NCAA basketball tournament and use the actual tournament as a test site. We will discuss the history of the NCAA tournament and how it currently operates. We will include the business side of the tournament as well. Discussions of how teams are currently evaluated will include the make-up of the selection committee and their deliberations. Analytics that have previously been applied to the tournament will focus on simulation capabilities, counting and probability models and decision analysis. Students will be broken into groups with the ultimate goal of coming up with actual brackets of teams that will be in the tournament. Groups will be asked to present and justify their brackets. Group brackets will be compared to the actual bracket. This course is not eligible for credit in the MS BANA program. Credit Level: G Credit Hrs:2. Prereq: See your college advisor for details. TouchPoint:MidCollegiate Baccalaureate Competency: Critical Thinking, Effective Communication, Information Literacy, Knowledge Integration.

BANA7011 Data Analysis. Introduction to data analysis and statistical methods with focus on practical decisions using quantitative models in a spreadsheet environment. Topics include sources of data, descriptive and graphical statistical methods, probability, distributions, sampling and sampling distributions, estimation, confidence intervals, and hypothesis testing. BANA 7011 should not be taken for credit by MS-Business Analytics students.  Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details. Recent Syllabus

BANA7012 Decision Modeling. Continuation of BANA 7011. Topics include regression modeling and analysis including simple and multiple regression, decision analysis for making decisions under uncertainty, risk analysis and simulation of complex models in a spreadsheet environment, what-if models and spreadsheet engineering, optimization models and solving them with spreadsheet tools, optimization models in business applications such as marketing and finance. BANA 7012 should not be taken for credit by MS-Business Analytics students. Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details. Recent Syllabus 001    Recent Syllabus 002

BANA7015 Advanced Health Care Data Analytics, Business Intelligence and Reporting. This course teaches the use of healthcare data to make decisions and transform healthcare delivery and the health of individuals and populations. The course concentrates on big and small data, and structured and unstructured data. Tools, applications and approaches for health data analytics are taught. This course covers topics such as statistical approaches; data, web and text mining; data visualization, simulation, modeling and forecasting. Key regulatory health and healthcare reporting requirements are taught. Credit Level:G Credit Hrs:3    Recent Syllabus

BANA7019 HR Analytics. This course will serve as an introduction to Human Resource Analytics. We will explore the use of analytics within the Human Resource functions of employee benefits, compensation, employee and labor relations and workforce development through guest speakers and class case studies. We will also explore the importance of technology to the overall analytic effort and how the right tools and talent help the effort to be successful. This course is not eligible for credit in the MS BANA program. Credit Level: G Credit Hrs:2. Prereq: See your college advisor for details.

BANA7020 Optimization. An introduction to modeling, solving with state-of-the-art software, and interpreting the results for real-world linear, integer, and nonlinear optimization applications. Solution techniques and analyses covered include graphical approaches, the simplex method, duality, and sensitivity for linear optimization; branch-and-bound and cutting plane techniques for integer optimization; and Newton’s method and gradient search for nonlinear optimization. Credit Level:G Credit Hrs:3 Pre-req: See your college advisor for details. Recent Syllabus

BANA7021 Advanced Optimization Analysis I. Additional development of skills for modeling and solving real-world optimization models. Solution techniques and analyses for linear optimization models including optimization criteria, simplex routines, duality, sensitivity, complexity analysis, decomposition, and the projective algorithm. An introduction to integer linear programming and solution techniques including the branch-and-bound and implicit enumeration approaches. An introduction to nonlinear optimization analyses. Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.  Recent Syllabus

BANA7022 Advanced Optimization Analysis II. Additional development of skills for modeling and solving real-world optimization models. Solution techniques and analyses for linear integer optimization models including Lagrangian relaxation, cutting plane methods, and the branch-and-cut routine. Optimization criteria, Lagrangian duality, search, gradient, and penalty methods for unconstrained and constrained nonlinear models. Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.

BANA7030 Simulation Modeling and Methods. Building and using simulation models of complex static and dynamic, stochastic systems using both spreadsheets and high-level simulation software. Topics include generating random numbers, random variates, and random processes, modeling systems, simulating static models in spreadsheets, modeling complex dynamic stochastic systems with high-level commercial simulation software, basic input modeling and statistical analysis of terminating and steady-state simulation output, and managing simulation projects. Applications in complex queueing and inventory models representing real systems such as manufacturing, supply chains, healthcare, and service operations. Credit Level:G Credit Hrs:3. Pre-req: See your college advisor for details.    Recent Syllabus

BANA7031 Probability Models. Events, probability spaces and probability functions; Random variables; Distribution and density functions; Joint distributions; Moments of random variables; Special expectations; Moment generating functions; Conditional probability and conditional moments; Probability inequalities; Independence; Special probability distributions including: binomial, negative binomial, multinomial, Poisson, gamma, chisquare, normal, beta, t, F, mixture distributions, multivariate normal; Distribution of functions of random variables; Order statistics; Asymptotic results including: convergence in distribution, central limit theorem, convergence in probability. Credit Level:G Credit Hrs:4 Prereq: See your college advisor for details.  Recent Syllabus

BANA7032 Stochastic Modeling I. Additional development of skills for modeling and analyzing real-world discrete and continuous time stochastic processes with state-of-the-art software. Gambler's ruin problem, random walk analyses, and other applications. Additional solution techniques and analyses for stochastic models including branching processes, time reversibility, Monte Carlo methods for discrete and continuous time Markov processes; Markov decision processes and hidden Markov chains and Brownian motion. Continuous time birth-death and Poisson processes will be introduced. Credit Level:G
Credit Hrs:2 Pre-req: See your college advisor for details.

BANA7033 Stochastic Modeling II. As a topics course, the format will have the character of a seminar with topics that may change with each offering. Previous topics have focused on stochastic calculus and included: Second order processes, Mean, covariance, and cross-covariance function, Gaussian processes, Wiener process and Brownian motion, Continuity, integration, and differentiation of second order processes, Continuity in mean square, Continuity of sample functions, Stochastic integration, Stochastic differentiation, White noise process, Stochastic differential equations, Stochastic differential equations of order 1, 2, and n, Estimation theory, Optimal prediction, Spectral distribution, Applications of stochastic differential equations Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.

BANA7035 Simulation Analysis. Probabilistic and statistical underpinnings of simulation modeling and analysis. Topics include advanced modeling techniques, advanced methods for modeling input processes, random-number generators, generating random variates and processes, design and analysis of simulation experiments, variance-reduction techniques, gradient estimation, and optimizing simulated systems. Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.

BANA7036 Financial Engineering. This is a course in Financial Engineering with an introduction to quantitative methods in financial economics. Emphasis is on probability and statistical techniques used most often in the analysis of financial markets and how they are applied to actual market data.Topics include: Return, Volatility, Random Walk, Brownian Motion, GARCH, Portfolio Analysis, Option Pricing, Diffusion Models, Value at Risk, Fixed Income, Asset Pricing, Term Structure of Interest Rates. Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.

BANA7038 Data Analysis Methods. This course covers the fundamental concepts of applied data analysis methods. Various aspects of linear and logistic regression models are introduced, with emphasis on real data applications. Students are required to analyze data using major statistical software SAS and R. BANA 7038 should not be taken for credit by MS-Business Analytics students.  Pre-req: See your college advisor for details. Recent Syllabus

BANA7041 Statistical Methods. Basic estimation, hypothesis testing, and data analysis. Point and interval estimation. One factor ANOVA. Fitting and drawing inferences from simple and multiple linear regression models. Variable selection procedures. Residual diagnostics and model correction procedure for linear regression. Credit Level:G Credit Hrs:4 Pre-req: See your college advisor for details. Recent Syllabus

BANA7042 Statistical Modeling. Nonlinear regression and generalized linear model. Logistic regression for dichotomous and polytomous responses with a variety of links. Count data regression including Poisson and negative binomial regression. Variable selection methods. Graphical and analytic diagnostic procedures. Overdispersion. Generalized additive models. Limited dependent variable regression models (Tobit), Panel Data models. Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.   Recent Syllabus

BANA7043 Statistical Aspects of Sample Survey. This is a course in the design and analysis of sample surveys with emphasis on appropriate choice of survey design and estimation methods to maximize precision and minimize cost. Simple random sampling with and without replacement, Stratified random sampling, Systematic sampling Cluster sampling, Probability-Proportional-to-Size sampling, Two-stage sampling; estimation methods for population means, totals, proportions, ratios and variances using Means per unit, Ratio estimates, Regression estimates, Probability-Proportional-to-Size estimates, Two-stage mean estimates, Post-stratification Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.

BANA7044 Analysis of Variance and Covariance. Analysis of variance for one factor and multifactor treatment structure in a completely randomized design; estimation and testing hypotheses about treatment means and estimable functions of effects; fixed, random, and mixed models; graphical and analytic checking of model assumptions; coping with violations of assumptions; variance components analyses; multiple comparisons; sample size and power analyses; matrix approach to the models; 2k factorial models; Analysis of randomized complete block design; analysis of covariance for one factor and multifactor treatment structure in a completely randomized design and simple blocked design. Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.

BANA7045 Design of Experiments. Analysis of data from designed experiments including: Randomized complete block, Balance incomplete block, Partially balance complete block, 2k factorial arrangements in completely randomized designs (CRDs) and blocked designs, Latin, Graeco-Latin, and Youden square, Nested and crossed-nested, Split plot, split-split plot and other variations, Strip plot, Repeated measures, Cross-over; fixed, random, and mixed models; graphical and analytic checking of model assumptions; coping with violations of assumptions and unbalanced data; generation of and analysis of fractional factorial arrangements with CRDs and blocked designs Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.  Recent Syllabus

BANA7046 Data Mining I. This is a course in the statistical data mining with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. Topics include: Data Processing. Variable Selection for linear regression and generalized linear regression. Out-of-sample Cross Validation. Generalized Additive models. Nonparametric smoothing methods. Classification and Regression Tree. Neural Network. Monte Carlo Simulation. Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.
Recent Syllabus  

BANA7047 Data Mining II. This is a course in the statistical data mining II with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. Topics include: Missing Data Imputation, Bootstrapping, Boosting and Multiple Additive Regression Trees, Bayesian Trees, Support Vector Machine, Discriminant Analysis, Cluster Analysis, Factor Analysis,Principle Component Analysis. Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.
Recent Syllabus   

BANA7048 Multivariate Statistical Methods. This is a course in the analysis multivariate data with emphasis on appropriate choice of estimation and testing methods. Vectors and matrices, Multivariate probability distributions and their parameter, Multivariate normal distributions, Maximization and minimization of multivariate functions, The "shape" of multivariate normal data, Correlation, prediction and regression, Sample statistics and their sampling distributions for multivariate normal data; Estimation and tests for correlation, Tests of independence, Estimation and tests for multivariate means and covariance matrices, ower of multivariate tests, multivariate linear models, canonical correlation analysis, Principal components analysis, Factor analysis, Classification and discrimination analysis. Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.  Recent Syllabus

BANA7049 Topics in Multivariate Methods. This is a course in which the topics may vary from time to time. The central theme is the analysis multivariate data with emphasis on appropriate choice of estimation and testing methods. Among the topics one may be exposed to are Cluster analysis, Canonical variate analysis, Canonical discriminant analysis, Multidimensional Scaling, MANOVA, LISREL, graphical analysis of multivariate data Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.

BANA7050 Forecasting and Time Series Methods. This is a course in the analysis of time series data with emphasis on appropriate choice of forecasting, estimation, and testing methods. Univariate Box-Jenkins methodology for fitting and forecasting time series. ARIMA models, Stationarity non-Stationarity, auto-correlation functions, partial and inverse autocorrelation functions, Estimation and model fitting, Diagnosing time series models, Forecasting: Point and interval forecasts, Seasonal time series models,Transfer function models, Intervention models, Modeling volatility with ARCH, GARCH, and other methods, Modeling time series with trends, Multiequation time series models: Vector Auto Regression (VAR), Cointegration and error correction models, Nonlinear time series models, State space time series models, Bayesian time series and forecasting Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.  Recent Syllabus

BANA7061 Statistical Inference. This course in the concepts of statistical inference will cover the topics contained in the following outline:Point estimation-maximum likelihood and method of moments principlesConfidence intervalsTests of statistical hypothesesChi-square testsSufficient statistics-measures of quality and properties of estimators-Rao-Blackwell theoremComplete families of distribution-exponential family of distributions-functions of a parameter and several parameters-minimal sufficient and ancillary statistics-location and scale parameters-sufficiency, completeness & independence (Basu's theorem)Bayesian methodsFisher Information and Rao-Cramer Inequality-efficient statistics-limiting distribution of MLE'sTheory of statistical testsbest tests and Neyman-Pearson theory-uniformly most powerful testsLikelihood ratio testsSequential methods Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.

BANA7062 Applied Inference Methods. Topic may vary depending on the interest of the instructor and students. Example topics are: resampling methods including bootstrap procedures, jackknifing, and cross validation; Monte Carlo methods and Accept-Reject Algorithms for integration; Bayesian computations including, Gibbs sampler, Monte Carlo Markov Chain, and Metropolis-Hastings. Missing data problems via EM algorithm and multiple imputation Emphasis is on applications of these methods. Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.

BANA7063 Bayesian Analysis and Decision Theory. This is a course in statistical decision theory and Bayesian analysis with most emphasis on concepts but some time devoted to computation. Game theory approach to decision theory, Expected loss, decision rules and risk, decision principles, Utility functions and loss functions, Prior information and subjective probability, Bayesian statistical inference including: point estimation, interval estimation, hypothesis testing, and prediction, all from a Bayesian point of view, Bayesian decision theory with applications Credit Level:G Credit Hrs:2 Pre-req: See your college advisor for details.

BANA 7095 Graduate Case Studies in Business Analytics. Real organizational problems or challenges will be presented to students by client companies. Students in groups will work with a client to develop a solution or solutions to the problems using advanced analytic techniques. Students will present the solutions to the client in both oral and written reports. Credit level is G, Credit Hrs: 2 Pre-req: See your college advisor for details.  Recent Syllabus

BANA8080 Independent Study in Business Analytics. This is a self-managed course during which student independently pursues topics and/or completes a project of personal interest within this subject area. Students must obtain a faculty supervisor and appropriate permission prior to registration. Credit Level:G Credit Hrs:1 - 6 Pre-req: See your college advisor for details.

BANA8083 MS Capstone. This course is associated with the required MS Business Analytics Capstone. The Capstone experience will be described in an essay that is reviewed and approved by two faculty members. The essay can describe: (1)a research project based onan idea proposed independently by the student or with faculty input; (2)an extension of a case analysis or project completed in a class such as BANA7095, Graduate Case Studies in Business Analytics. The essay must describe the student's contribution to the research or case. Credit Level: G Credit Hrs: 1. Pre-req: See your college advisor for details.  Recent Syllabus

BANA8084 MS Capstone – Internship. This course is associated with the required MS Business Analytics Capstone. The Capstone experience will be described in an essay that is reviewed and approved by two faculty members. The essay will describe a project or projects completed during an internship taken as part of the student's MS-Business Analytics course work. The essay must describe the student's contribution to the project(s). Credit Level: G Credit Hrs: 3 Pre-req: See your college advisor for details.

BANA 8090  Special Topics in Business Analytics.  This course is used to explore topics of current interest in the BANA domain, that do not fall within the scope of any of the regularly scheduled courses. By the nature of the course, specific topics covered will vary with each offering. - Prerequisite Definition: To take this course you must: Be enrolled in the following Plan BABA-MS.  Recent Syllabus

BANA9071 Research in Business Analytics. This is a variable credit course reflecting research activity for pre-comprehensive exam students. Students in their first and second years of doctoral study will accumulate QA9071 credits as they progress in their doctoral studies. Credit Level:G Credit Hrs:1 - 15 Pre-req: See your college advisor for details.

BANA9085 Seminar in Business Analytics I. Research topics in business analytics are the focus of this course. The course may consider advanced topics not covered in other courses or new research methods. Credit Level:G Credit Hrs:1 - 6 Pre-req: See your college advisor for details. Recent Syllabus

BANA9086 Seminar in Business Analytics II. Research topics in business analytics are the focus of this course. The course may consider advanced topics not covered in other courses or new research methods. Credit Level:G Credit Hrs:1 - 6 Pre-req: See your college advisor for details.  Recent Syllabus

BANA9087 Seminar in Business Analytics III. Research topics in business analytics are the focus of this course. The course may consider advanced topics not covered in other courses or new research methods. Credit Level:G Credit Hrs:1 - 6 Pre-req: See your college advisor for details.

BANA9091 PhD Research in Business Analytics. This is a variable credit course reflecting a doctoral student's engagement in dissertation research and progress toward completion of the dissertation. Doctoral students will accumulate a minimum of 30 semester credit hours of dissertation credit in order to be eligible for graduation. Credit Level:G Credit Hrs:1 - 15 Pre-req: See your college advisor for details