Business Analytics | Curriculum

Curriculum

The curriculum for the Master of Science Business Analytics Program consists of 36 credit hours, to be completed over approximately 10 months. All students in the program begin coursework in the fall session. View course information by selecting the relevant tab below.

Required Courses

  • MIS 381N: Data Analytics Programming
    General introduction to programming and scientific computation using an object-oriented programming language and statistical software. Topics to be covered include:

    Data structures and algorithms in Java

                    -Asymptotic analysis

                    -Stacks, queues, linked lists, arrays

    Algorithms

                    -Divide-and-conquer

                    -Dynamic programming

                    -Greedy

                    -Backtracking

    Big Data

                    -Concurrency, key primitives

                    -In-memory data management

                    -Hadoop and the map-reduce framework

  • MIS 381N: Optimization and Decision Analysis

    The course is an introduction to quantitative decision-making. Quantitative decision making adds value to data by using it to build models that can help in the decision making process. We will cover decision analysis, Monte Carlo simulation and mathematical programming including linear programming, non-linear programming and integer programming. The focus will be on formulation and intuition behind the solution techniques rather than the mathematical theory. Formal math skills like calculus, linear algebra, and probability/statistics will be used some but not much. You do need the ability to think logically and systematically, but improving this ability is a course goal. The course will also introduce students to some advanced commercial software and some scientific computing languages that support the development of these models.

  • BA 385T: Financial Management
    • Topics to be covered include:
    • Maximizing shareholder value
    • Time value of money, and its application
    • Risk and return
    • Cost of capital
    • Capital budgeting:  NPV, IRR, Payback, Discounted payback, Breakeven
    • Free Cash Flow and Capital Budgeting
    • Valuing a firmCost behavior
    • Cost allocation
    • Activity-based costing
    • Joint Production profitability, costing, and strategy
    • Product costing
    • Transfer pricing
  • MIS 381N.1: Introduction to Data Management

    Topics to be covered include:

    • Database design
    • Data quality
    • Data transfer
    • Current definitive DB languages (SQL)
    • Other DB tools
      • NoSQL, Hadoop, Excel, R, SAS, Visualization
    • Security and privacy
  • STA 380: Introduction to Business Data Analytics 

    In this course, we will examine how data analysis technologies can be used to improve decision-making.  We will study the fundamental principles and techniques of data mining, with real-world examples and cases to place data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.  In addition, we will work "hands-on" with data mining software.

    Topics covered will include:

    Introduction to Data mining, Machine Learning and Artificial Intelligence: Concepts and Definitions, The Data Mining Process, Predictive and Descriptive tasks

    Classification: Information and Attributes, Recursive partitioning and Decision Trees, Class-probability Estimation, Logistic Regression

    Software: R, Matlab, and WEKA

  • MIS 382N.9: Advanced Data Analytics I: Predictive Modeling 

    In this course we will study a variety of advanced techniques for predictive analytics. Particular emphasis will be given to approaches that are scalable to very large data sets and/or those that are relatively robust when faced with a large number of predictors, and algorithms for heterogeneous or streaming data. Many of these capabilities are essential for handing BIG DATA. Connections to relevant business problems shall be made via several example studies.  We will mostly be using the R language for statistical modeling. SAS Enterprise Miner will also be used towards the latter part of the course to illustrate the overall data analytics process as well as modeling choices.  

  • STA 380.18: Advanced Data Analytics II: Unsupervised Learning and Time Series 
    This course is about unsupervised statistical machine learning for all kinds of data and about supervised learning for time-sequenced data. The topics covered in unsupervised learning include data reduction, cluster analysis, principal components, factor analysis, multidimensional scaling and other data analytic techniques for understanding the structure of data when there is no outcome variable (no dependent variable). The topics covered in supervised time-sequenced learning include modeling and forecasting of time series, autoregression, moving averages, and other techniques. A typical problem for supervised time-sequenced learning is forecast future sales, given past sales and other related past data. Familiarity with the mathematics of linear algebra is essential; but calculus is not. Real data sets will be used throughout the course for motivation, illustration of techniques, and practice. SAS software will be used. No previous experience with SAS will be assumed, but it would be helpful to have had some experience writing simple software code in some programming language.
  • MIS 382N.11: Business Intelligence Capstone
    Brings together foundations of business analytics related to database management, data analysis techniques and business decision making to solve a business problem of a real-world client.

Elective Courses

The number of electives offered each year will vary and will be determined by demand.

  • EE 380L: Advanced Data Mining and Web Analytics
    Description coming soon
  • MKT 382: Marketing Analytics I (offered Spring 2014)

    Marketing Analytics refers to the science of identifying patterns and relationships within primary and secondary data to develop, implement, and assess marketing strategies.  This first of the two-course sequence in Marketing Analytics will introduce you to a quantitatively oriented view of marketing strategy.  Topics will include company, customer, and competitive analysis, segmentation, targeting and positioning, the marketing mix and mix response analysis.  The goal of this course is to introduce you to these topics. 

  • RM 392.5: Computational Finance
    Description coming soon.
  • LIN 386M/CS 395T: Data-intensive Computing for Text Analysis
    Description coming soon.
  • MKT 382.18: Marketing Analytics II (offered Spring 2014)

    The second course in the Marketing Analytics sequence will provide you with tools and methods to leverage data to inform marketing strategies. The following topics will be addressed: 1) The role of qualitative research in marketing, 2) Experimental design 3) Sampling theory and application, 4) Demand models and optimal pricing, 5) Models of consumer choice, 6) Models for new product development, 7) Market segmentation and positioning, and 8) Direct-targeting; customer retention and acquisition. Emphasis will be on ‘learning by doing.’ Topics will be addressed theoretically and applied in projects on real world applications.

  • OM 386: Pricing and Revenue Optimization
    Description coming soon.
  • MIS 382N.12: Social Media Analytics (offered Spring 2014) 

    Topics covered include:

    • Strategic aspects and business value of social media analytics
    • Metrics for assessing the effectiveness of social media strategies
    • Measuring social influence and customer network value
    • Collecting, analyzing, deriving insights from, and dashboarding social media chatter
    • Techniques and social media applications of sentiment analysis and text mining
    • Evaluation and visualization of the outputs of exploratory and predictive data analysis
    • Using distributed processing frameworks for analyzing massive amounts of streaming data
    • Tools
      • Radian6, NodeXL, Pajek, SAS Text Miner, SAS Sentiment Analysis, Hadoop, Spark, and other open source
  • OM 380.17: Supply Chain Analytics (offered Summer 2014) 
    Supply chain analytics combines analytical tools with technology to identify trends, compare performance and highlight improvement opportunities in supply chain function by analyzing data. It helps decision makers in supply chain areas including sourcing, inventory management, manufacturing, quality, sales and logistics.

    Topics covered will include:

    • Process Analysis
      • Capacity, Bottlenecks, Batch sizing
      • Statistical Process Control
    • Procurement
      • Total Cost of Ownership
      • Valuing risk
    • Logistics and Inventory
      • Forecasting and uncertainty of demand
      • Transportation and network design

Curriculum Sequence

This is a tentative curriculum sequence and is subject to change.

Fall Semester

MIS 385: Data Analytics Programming
MIS 381N: Decision Analysis and Optimization
BA 385T: Financial Management
MIS 381N.1: Introduction to Data Management
STA 380: Introduction to Business Data Analytics

Spring Semester

MIS 382N.9: Advanced Data Analytics I: Predictive Modeling
STA 380.18: Advanced Data Analytics II: Unsupervised Learning and Time Series
3 Electives:
MKT 382: Marketing Analytics I
MKT 382.18: Marketing Analytics II
MIS 382N.12: Social Media Analytics

Summer Semester

MIS 382N.11: Business Intelligence Capstone
1 Elective:
OM 380.17: Supply Chain Analytics

Page last updated: 3/5/2014