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Master of Science in Business Intelligence & Analytics Course Descriptions
Built on the certification standards of SAS, Inc., the industry-leading Business Intelligence software provider, the curriculum of the online Business Intelligence & Analytics master’s degree from Saint Joseph’s University covers data mining, data warehousing, and data-driven communication to help graduates master front-to-back Business Intelligence and Analytics skills, something few other schools can offer. Graduates will receive not only the Business Intelligence & Analytics master’s degree, but a Business Intelligence Certificate endorsed by SAS, Inc., as well.
DSS 600 Foundations for Business Intelligence
DSS 610 Business Analytics
DSS 615 Python Programming Language
DSS 620 Concepts and Practice of DSS Modeling
DSS 630 Database Management Theory and Practice
DSS 640 Enterprise Data
DSS 650 Business Process Modeling & Analysis
DSS 660 Introduction to Data Mining
DSS 665 R Statistical Language
DSS 670 Critical Performance Management
DSS 680 Predictive Analysis
DSS 690 Special Topics in Business Intelligence
DSS 600 Foundations for Business Intelligence (3 credits)
This course is intended to provide an integrative foundation in the field of business intelligence at the operational, tactical, and strategic levels. Topics such as value chain, customer service management, business process analysis and design, transaction processing systems, management information systems, and executive information systems will be covered, along with other topics relevant to the field of business intelligence.
DSS 610 Business Analytics for BI (3 credits)
The aim of this course is to provide the student with an understanding of several management science techniques and to provide some insight into how these tools may be used to analyze complex business problems and arrive at a rational solution. The techniques to be studied are forecasting, linear planning, simulation, and modeling. Cases of increasing complexity will be used to emphasize problem description, definition, and formulation. The computer will be used extensively throughout the course, primarily by using available programs to perform the calculations after the problem has been correctly formulated. Emphasis will be placed on the interpretation and implementation of results. In addition, we will examine the future of analytics. Prerequisite: DSS 600.
DSS 615 Python Programming Language (3 credits)
Python is an open source programming language that focuses on readability, coherence and software quality. It boosts developer productivity beyond compiled or statically typed languages and is portable to all major computing platforms. This course is designed as an introduction to python programming and the characteristics that make it unique. Student will learn the use of the python interpreter, how to run programs, python object types, python numeric types, dynamic typing, string fundamentals, lists and dictionaries, and tuples and files. Prerequisites: DSS 600, DSS 610.
DSS 620 Concepts and Practice of DSS Modeling (3 credits)
Building on the background of previous courses, this course will extend the use of spreadsheet modeling and programming capabilities to explore decision models for planning and operations using statistical, mathematical, and simulation tools. Prerequisites: DSS 600, DSS 610.
DSS 630 Database Management Theory and Practice (3 credits)
Business Intelligence rests on the foundation of data storage and retrieval. In this course, students will be presented with the theory of operational database design and implementation. The concepts of normalization, database queries, and database application development will be introduced using contemporary tools and software for program development. Prerequisites: DSS 600, DSS 610.
DSS 640 Enterprise Data (3 credits)
Traditional database design concentrates on the functional areas of business and their database needs. At the strategic and value-chain levels, we look at data across the enterprise and over time. The issues of Enterprise Data in the Data Warehouse, Data Marts, Enterprise Resource Planning (ERP), Supply Chain Management (SCM), Customer Relationship Management (CRM), Online Analytical Processing (OLAP), and the concepts of Data Mining will be surveyed in this course. Prerequisites: DSS 600, DSS 610, DSS 630.
DSS 650 Business Process Modeling & Analysis (3 credits)
Using the case study approach in combination with contemporary software tools, students will apply the concepts of business process analysis and design, quality control and improvement, performance monitoring through performance dashboards, and balanced scorecards and process simulation. Prerequisites: DSS 600, DSS 610.
DSS 660 Introduction to Data Mining (3 credits)
This course in the Business Intelligence Program will extend the concepts of data mining to an exploration of a contemporary Data Mining toolset on a large live data set. In this course, students will be encouraged to find the patterns in the data and to prepare reports and presentations describing the implications of their findings. Prerequisites: DSS 600, DSS 610.
DSS 665 R Statistical Language (3 credits)
The goal of this course will be to use R’s command line interface (CLI) to build familiarity with the basic R toolkit for statistical analysis and graphics. Specifically, students will learn good programming practices to manage and manipulate data, become familiar with some of R's most commonly used statistical procedures, and apply knowledge of data mining techniques (Multivariate Statistics, Regression, ANOVA, Cluster Analysis, Logistic Regression) for complex data sets using R. Prerequisites: DSS 600, DSS 610, DSS 660.
DSS 670 Critical Performance Management (3 credits)
This course integrates the concepts of decision support, database management, critical performance measurement, and key performance indicators through the practical application development of performance dashboards. When completed, students will be able to design department level, user-oriented applications that capture data from transaction processing systems and present that data for business users in decision-compelling format. Prerequisites: DSS 600, DSS 610.
DSS 680 Predictive Analytics (3 credits)
This course extends the data mining process to the predictive modeling, model assessment, scoring, and implementation stages. In this course, professional data mining software and small and large data sets will be used to effectively analyze and communicate statistical patterns in underlying business data for strategic management decision making. Prerequisites: DSS 600, DSS 610, DSS 660.
DSS 690 Special Topics in Business Intelligence (3 credits)
Content of this course varies to allow for ongoing changes to business intelligence and related fields. The instructor will provide the course description for a given semester. Prerequisites: DSS 600, DSS 610 (Other prerequisites may be required, dependent on the course topic).
This 30 credit hour program can be finished in about two years upon successful completion of the courses above. To learn more about the online Business Intelligence & Analytics master’s degree from Saint Joseph’s University, call (866)758-7670 to speak with an admissions representative, or you can request more information.