The University of Tennessee, Knoxville

Business Analytics & Statistics Department in the College of Business Adminstration at the University of Tennessee Dreamweaver Templates. College of Business Administration at the University of Tennessee


Academics


The Ph.D. in analytics has five learning objectives aimed at training candidates to perform research consistent with the college’s mission to generate nationally and internationally recognized outcomes that improve the world.

1) Gain deep knowledge of the literature and developments in a specific area within analytics in order to further the field’s appropriate methodological tools.
2) Fulfill the growing need for faculty that can teach business analytics at the undergraduate and graduate levels.
3) Conduct research that addresses real-world problems using quantitative and methodological models to visualize and interpret data.
4) Develop and test research hypotheses by acquiring data from primary and secondary sources.
5) Clearly communicate research findings and be able to present work at national and international conferences.

Coursework

The Ph.D. in analytics requires 48 credit hours of coursework, excluding dissertation hours. Students with adequate preparation prior to starting the program may be able to reduce the requirement by as many as 15 hours.

  • General Prerequisites (0 credits)

    Technical Writing
    Computer Programming

    Prerequisites must be completed prior to the start of the first fall semester. The courses do not count towards credit hours and can be undergraduate level, graduate level and approved Coursera courses.
  • Business Analytics Prerequisites (15 credits)

    BZAN 550 - Captsone
    BZAN 552 - Multivariate and Data Mining Techniques
    BZAN 553 - Design of Experiments

    Students who have completed graduate level courses similar to these courses can request to have them waived. They courses do not have to be completed at UT. The total credits required will be reduced accordingly.
  • Application Electives

    Choose from courses in economics, information systems, finance, marketing, etc.

Required Courses (33 credits)

Ph.D. Core (15 credits)
    • BZAN/STAT 610 Probability and Stochastic Processes
      Foundation in the theory and application of probability and random, time-dependent processes for analyzing system behavior, moment generating functions and Laplace transforms, the Poisson process and exponential distribution, Markov chains and Markov processes for modeling time-dependent behavior, queueing theory.
    • BZAN/STAT 615 Statistical Inference
      Univariate and multivariate distributions, transformations, inequalities, convergence. Likelihood and the MLE for various models, Fisher information matrix, asymptotic results. Hypothesis testing. Wald, score, likelihood ratio and randomization tests. Interval estimation. The bootstrap. Simultaneous inference. Lifetime distributions, censoring. Biased sampling.
    • BZAN 620 Prescriptive Analytics
      Identification and formulation of linear, discrete, non-linear and recursive optimization models. Basic theory of feasible regions and optimal solutions. Exposure to exact and approximate solution methods.
    • BZAN/STAT 625 Bayesian Modeling and Computations
      Bayes theorem, prior and posterior distributions, inference methods such as posterior means and HPD regions, Monte Carlo inference including Gibbs sampling, the Metropolis-Hastings algorithm, and importance sampling. Bayesian analysis of linear and non-linear regression models, model selection using Bayes factors. Predictive inference based on posterior distributions, and selected applications drawn from business and scientific settings.
    • BZAN 630 Decision and Operations Analytics
      Decision making from a systems perspective, asking the right question for business analytics problems, root-cause analysis and conflict resolution, constraint management in manufacturing and project execution, analytical and simulation models for process modeling and evaluation, value of information in managing demand and supply systems, models for capacity planning and inventory management in supply chains.
  • Research Apprenticeship (3 credits)
  • Concentration - Choose 3 (9 credits)
    • BZAN 640 - Advanced Prescriptive Analytics
      Prescriptive analytics based on convex, large-scale, and stochastic optimization. Polyhedral theory, decomposition, projection, mathematical treatment of simplex algorithm, primal and primal-dual interior point methods, convex programming, goal programming, introduction to stochastic programming.
    • BZAN 641 - Advanced Stochastic Analytics
      The analysis and modeling of stochastic processes and Markov decision processes. Topics include renewal processes, Brownian motion, martingales, Markov decision processes and their solution using policy iteration methods and dynamic programming. Applications to queueing, inventory, scheduling, and financial decision models.
    • BZAN 642 - Advanced Supply Chain Analytics
      : Supply chain network design models including facility location and capacity allocation, warehousing and transportation, aggregate planning, matching demand with supply, inventory modeling under deterministic and uncertain scenarios, multi-echelon and risk-pooling models, price optimization, value of information in managing demand and supply systems, role of operations flexibility, role of coordination and effective contracting on supply chain performance.
    • BZAN/STAT 645 - Advanced Topics in Data Mining
      Selected topics in data mining. Read and critique current literature. Solve research problems motivated by real applications.
      Repeatability: May be repeated. Maximum 6 hours.
    • BZAN/STAT 646 - Modern Multivariate Techniques
      An overview of Random Vectors and Matrices, Matrix Norms, Eigen-analysis and condition numbers. Multivariate normal distribution, Advances in Principal Component Analysis, Advances in Partial Least Squares, Factor Analysis, Canonical Correlation Analysis, Discriminant Analysis and Cluster Analysis, Sliced Inverse Regression and related methods.
    • BZAN 647 - Customer Analytics
      Advanced data mining and machine learning with applications in areas such as acquisition modeling, up-sell modeling, cross-sell modeling, churn modeling, and customer lifetime modeling. Topics will include regularized logistic regression, decision trees, neural networks, random forests, advanced performance measures, and bias-variance tradeoffs. Students are expected to have had previous experience with programming in R, or with machine learning and data mining.
    • BZAN/STAT 648 - Advanced Topics in Design of Experiments and Linear Models
      Current topics in design of experiments and linear models, enabling students to understand and critique the literature and to utilize this literature in challenging applications.
    • BZAN 649 - Observational Studies and Causal Models in Business Analytics
      The course will explore methods to make valid inferences from data that is collected without a formal randomized design. Econometric methods such as propensity scores, difference in difference estimators, selection models, and instrumental variables methods will be explored as well as specialized methods for panel data. Applications in marketing, accounting and finance will be highlighted.
    • BZAN 683 - Special Topics in Business Analytics (1-3 credits)
      Presentation of specialized analytics topics
      Repeatability: May be repeated. Maximum 6 hours.
      Registration Restriction(s): Minimum student level – graduate.
  • Other courses (6 credits)

Business Analytics & Statistics ~ 255 Stokely Management Center ~ Knoxville, TN ~ 37996 - 0532
Phone: 865-974-5544 ~ Fax: 865-974-2490 ~ Email:bas@utk.edu

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