August 2013
MS in Business Intelligence and Analytics
COURSE CATALOG DESCRIPTIONS
MGT 615 - Financial Decision Making
Corporate financial management requires the ability to understand the past performance of the
firm in accounting terms; while also being able to project the future economic consequences of
the firm in financial terms. This course provides the requisite survey of accounting and finance
methods and principles to allow technical executives to make effective decisions that maximize
shareholder value.
MIS 630 Data Management
This course focuses on data and database management, with an emphasis on modeling and
design, and their application to decision support. The course is organized around the following
general themes: Strategic Data Planning, Data Governance, Enterprise Data Integration, Data
Management Approaches, Data Design for Transaction Processing vs. Decision Support, Data
Management Functions, Abstraction and Modeling, Data- and Information Modeling (ER,
Object-oriented), Database Schemas (Conceptual Schema), Database Design (Functional
Dependencies and Normalization), Query languages (SQL, DDL, QBE), Metadata Development
and Application, Data Quality Approaches, Master and Reference Data Management (e.g.,
Customer and Product Data), Temporal Data, Data, Analytics, and Business Performance,
Introduction to Data Warehousing, OLAP, OLTP, and Data Mining, Strategic Data Policies and
Guidelines (e.g. Enterprise Data and Integration, Governance, Markets, Customers, and
Competitors, Leadership, Analysts and Knowledge Worker Skills and Training, Communities of
Analysts). There are numerous case studies and modeling projects throughout the course.
MIS 636 Data Warehousing and Business Intelligence
This course focuses on the design and management of data warehouse (DW) and business
intelligence (BI) systems. The course is organized around the following general themes:
Analytics & Competitive Advantage (Internal and External Processes, Customer and Competitor
Intelligence), Business Intelligence and Industry Value Chains, BI Systems life-cycle, Enterprise
Planning, Project Management, Business Requirements, Architecture, Tool Selection, Data
Design (Star-schema, Surrogate Keys, ODS, Real-time, Partitioned Tablespaces, Aggregations,
MDDB (Cube Design), Conformed Dimensions), Streaming Data, Methods for Tracking
History, Implementation (ETL, Data Staging, and Physical Design), Data Visualization
Techniques and Applications, BI Application Development (includes Portal and Dashboard
Design), Complex Query Design, Deployment, Maintenance and Growth, and Emerging Issues.
There are numerous case studies, class exercises, homeworks, and an end-to-end BI design
project.
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MIS 637 Knowledge Discovery in Databases
This course focuses on data mining and knowledge discovery methods, models, and algorithms,
and their applications in solving real world business and operation problems. We concentrate on
demonstrating how discovering the hidden knowledge in databases will help managers make
near real-time intelligent business and operation decisions. The course is organized around the
following general themes: End-to-End System Approach to Data Mining and Knowledge
Discovery, Data Preprocessing (advanced), Linear Regression, Logistic Regression, Business
and Operations Applications, Data Preprocessing (advanced), Min-Max Normalization, Z-Score
Standardization, Linear Regression, Logistic Regression, Association Analysis, k-Nearest
Neighbor Algorithm, k-Means Clustering Algorithm, Model Evaluation Techniques, and case
studies.
BIA 650 Process Analytics and Optimization
This course covers basic concepts in optimization and heuristic search with an emphasis on
process improvement and optimization. This course emphasizes the application of mathematical
optimization models over the underlying mathematics of their algorithms. While the skills
developed in this course can be applied to a very broad range of business problems, the practice
examples and student exercises will focus on the following areas: healthcare, logistics and
supply chain optimization, capital budgeting, asset management, portfolio analysis. Most of the
student exercises will involve the use of Microsoft Excel’s “Solver” add-on package for
mathematical optimization.
BIA 652 Multivariate Data Analytics
This course focuses on understanding the basic methods underlying multivariate analysis through
computer applications using R. Multivariate analysis is concerned with datasets that have more
than one response variable for each observational or experimental unit. Topics covered include
principal components analysis, factor analysis, structural equation modeling, multidimensional
scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant
function analysis, logistic regression, and other methods used for dimension reduction, pattern
recognition, classification, and forecasting. Through class exercises and a project, students apply
these methods to real data and learn to think critically about data analysis and research findings.
BIA 654 Experimental Design
This course covers fundamental topics in experimentation including hypothesis development,
operational definitions, reliability and validity, measurement and variables, as well as design
methods, such as sampling, randomization, and counterbalancing. The course also introduces the
analysis associated with various experiments because designing good experiments involves
thinking about how to analyze the obtained data. Experiments test cause-effect relationships; this
course has very broad applications across all the natural and social sciences. At the end of the
course, students present a project, which consists of designing an experiment, collecting data,
and trying to answer a research question.
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BIA 656 Statistical Learning and Analytics
This course introduces the most relevant algorithms of generative and discriminative estimation.
Main topics include autoregressive and moving average models, seasonality, long memory
ARMA and unit root test, volatility modeling, linear methods for classification, kernel methods,
support vector machines, Bayesian and Markovian graphical models, EM algorithm, inference,
sampling methods, latent variables, hidden Markov models, linear dynamical systems,
reinforcement learning, and ensemble methods (boosting, bagging and random forests.) The
course will also explore applications of the learning algorithms to finance, marketing, and
operations.
BIA 658 Social Network Analytics
In this course, students will learn how to analyze social network data and apply the analyses to
develop marketing strategies. The course focuses on network concepts, including graph-theoretic
fundamentals, centrality, cohesion, affiliations, equivalence, and roles, as well as design issues,
including data sampling and hypothesis testing. Theoretical areas covered include
embeddedness, social capital, homophily, and network growth. Another focus of this course is on
marketing applications of social network analysis, in particular the use of knowledge about
network properties and behavior, such as hubs and paths, the robustness of the network, and
information cascades, to better broadcast products and search targets After taking this course,
students should be able to statistically analyze and describe large scale networks, model the
evolution of networks, and apply the network analyses to marketing research.
BIA 660 Web Analytics
In this course, students will learn through hands-on experience how to extract data from the web
and analyze web-scale data using distributed computing. Students will learn different analysis
methods that are widely used across the range of internet companies, from start-ups to online
giants like Amazon or Google. At the end of the course, students will apply these methods to
answer a real scientific question or to create a useful web application.
BIA 670 Risk Management: Methods and Applications
This course covers an introduction to the theory and practice of risk management with
emphasis on current techniques and applications. We consider Blck-Scholes Options
Pricing theory, real options and pricing,portfolio optimization, value at risk, and coherent
risk measures. This course emphasizes the use of mathematical models to analyze risk
phenomena and the implementation of risk-aware solutions. The skills developed in this
course can be applied to a broad range of business problems. The examples and
student exercises will focus on the following areas: real options, energy, drug discovery,
and portfolio optimization & analysis.
BIA 672 Customer Analytics
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Covers marketing analytics techniques such as segmentation, positioning, and
forecasting, which form the cornerstone of marketing strategy in industry. Students will
work on cases and data from real companies, analyze the data, and learn to present
their conclusions and make strategic recommendations.
BIA 674 Supply Chain Analytics
Introduces the tactical and strategic issues surrounding the design and operation of
supply chains, to develop supply chain analytical skills for solving real life problems.
Topics covered include: supplier analytics, capacity planning, demand-supply matching,
sales and operations planning, location analysis and network management, inventory
management and sourcing.
BIA 686 Applied Analytics in a World of Big Data
Business intelligence and analytics is key to enabling successful competition in today’s world of
“big data”. This course focuses on helping students to not only understand how best to leverage
business intelligence and analytics to become more effective decision makers, making smarter
decisions and generating better results for their organizations. Students have an opportunity to
apply the concepts, principles, and methods associated with four areas of analytics (text,
descriptive, predictive, and prescriptive) to real problems in an application domain associated
with their area of interest.
BIA 702 Curriculum Practical Training
This course involves an educationally relevant practical assignment that augments the
academic content of the student’s program. Students engage in a project in a company
project related to the focus of their academic program. The project is conducted under
the supervision of a faculty advisor and an industry mentor. During the semester, the
student must submit written progress reports and at the end of the semester, a detailed
written report that describes his/her activities and knowledge gained during that
semester. This is a one-credit course that may be repeated up to a total of three credits.
BIA 800 Special Topics in BI&A
With permission of the instructor. Limit of six credits for the degree of Master of Science in
Business Intelligence and Analytics. The objective of this course is to allow BI&A students an
opportunity to obtain knowledge about a specialized topic within the field of business
intelligence and analytics through research or practical experience in a real world setting.
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