12 courses with the subject MSA, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
MSA 500Introduction to Business Analytics (3 cr.)
Business Analytics uses data sources and quantitative techniques to generate insights and valuable information used in business decision making. The course provides an overview of the business analytics process. The three areas of descriptive, prescriptive and predictive analytics will be presented. Topics covered include problem definition, data preparation, technical analysis and modeling, evaluation of results and implementation. The development and use of data warehouses and data marts to support business analytics is discussed. Examples of applications of analytics in different business domains (finance, marketing, healthcare, etc.) will be discussed. Also, students will be introduced briefly to the SAS and IBM analytical tools used throughout the program.
Knowledge of introductory statistics, introductory algebra and some coding experience (e.g., in SAS, R or Python).
MSA 520Management of Information Assets (3 cr.)
The course introduces students to the fundamentals of data management and data warehousing. Data is an integral component of Analytics. The appropriateness, accuracy and timeliness of data used in analytics is key to good decisions. This course will expose students to the design and querying of relational databases as well as the design, set up and use of Data Warehouses and Data Marts. The topics covered in this course include DBMS design, implementation and management, data retrieval, data integration, ETL, dimensional modeling, OLAP, etc.
knowledge of introductory statistics, information systems, and some coding experience (e.g., in SAS, R or Python). Semester(s) Offered: Fall
MSA 530Data Visualization (3 cr.)
Rendering data clearly and effectively with appropriate visual analytics reduces the time required to achieve understanding and helps manage the growing amount of available digital data. This course teaches the essential and practical skills in visualization, including computer graphics, visual data representation, physical and human vision models, numerical representation of knowledge and concepts, animation techniques, pattern analysis, and computational methods. Students will learn to use various software tools including R, ggplot, Tableau 8, and Oracle APEX 5 during the class.
MSA 500 and MSA 520 (MSA520 may be taken concurrently) Semester(s) Offered: Fall
MSA 540Data and Text Mining (3 cr.)
This course will provide the basic framework for conducting various data and text mining methodologies, including logistic regression analyses, classical discriminant analyses, association rule, decision tree, support vector machine, neural networks, variable reduction, cluster analyses, text analytics, and web mining. Emphasis will be on the appropriateness of methods, data visualization, how to resolve common issues, and use of data for generating output and interpretation, all in an attempt to give you hands-on experience and to aid you in the decision making process with applications in business intelligence, customer relationship management, fraud and lie detection, improvement of resource utilization, clickstream web mining, and credit scoring for loan applications. The ultimate goal is to enhance your appreciation of the idea that companies must be analytics competitors in order to be and stay competitive.
MSA 500 , MSA 520 (MSA 520 may be taken concurrently). Semester(s) Offered: Fall
MSA 550Predictive Analytics (3 cr.)
This course covers the fundamental predictive analytics and data mining approaches applied in Business. It introduces basic concepts and techniques to discover patterns in data, identify variables with the most predictive power and develop predictive models. Topics covered in this course include multivariate data analysis, logistic regression, factor analysis, data mining, discriminant analysis, cluster analysis, decision trees and neural networks. Best practices on the selection of methods and tools to build predictive models will also be covered. The JMP Pro statistical software for SAS will be used in this course. JMP Includes an exploratory, graphical approach to both traditional statistics and data mining techniques.
This course focuses on formulating and solving mathematical optimization models and building and solving simulation models. First the course discusses how optimization tools are used to find the optimal-or near optimal solutions to constrained optimization problems. This part covers commonly used optimization models in business, namely, linear, integer, mixed-integer, and goal programming problems, with an emphasis on model formulation and construction. The second part of this course introduces the field of computer-based simulation with a focus primarily on discrete-event modeling methods in which the state of the model is dynamic and the state of the model changes only at countable, distinct points in time. Also, the system being modeled is considered as a process, i.e. a sequence of operations being performed across entities. Cases in manufacturing, telecommunications, transportation, military and health care will be used. The models will be analyzed using SAS/OPTMODEL and Simulation Studio software.
Web analytics deals with methods and tools to collect, analyze, and report website usage data by visitors, emphasizing the nature of the visits to websites and visitors’ demographics. This course introduces the metrics, contents, user behaviors, and reporting of web sites by using specific Web Analytic tool (e.g., Google Analytics) to collect web site data and conduct the analysis. This course also discusses the major Key Performance Indicators (KPI) that can be measured and tracked continuously for website optimization with a focus on organizational implementation. In addition, this course investigates related topics such as web mining, social web mining, and game metrics.
MSA 530 , knowledge of web search engines, knowledge of web site development. Semester(s) Offered: Spring
MSA 570Big Data Analytics (3 cr.)
This course provides the fundamental knowledge to students to handle the challenges brought up by Big Data. This course introduces the overview applications and market trend of Big Data, describes the fundamental platforms, such as Hadoop, Spark, and other tools, such as IBM System G for Linked Big Data, discusses Big Data storage and processing methods, and covers different Big Data analytics algorithms on different platforms. Additional topics include visualization and mobile issues on Big Data Analytics.
This course offers concepts, tools, tutorials, and case studies that business managers need to extract and analyze the seven layers of social media data, including text, actions, networks, apps, hyperlinks, search engine, and location layers. We will see how social network analysis, web mining and statistical methods can be used to study these topics. By the end of this course, students will have mastered the concepts, techniques, and tools used to extract business insights from social media that help increase brand loyalty, generate leads, drive traffic, and ultimately make good business decisions. While there are no prerequisites, students are expected to be proficient in SAS programming, data structures, and web analytics and have taken college level or above courses in statistics and mathematical modeling.
This course is designed as a special topic of business analytics to explore how mining and quantitative methods can help us solve healthcare-related problems. It provides a framework to understand a range of health data standards and privacy concerns on information management policy, explore the types of health information systems prevalent in healthcare organizations, and analyze health information to support decision making in various healthcare problems. In this course, students will learn how the core competencies of healthcare analytics can be developed and applied using real-world case studies. Students will be exposed to specific concepts related to IT architecture and governance, health information systems, electronic medical records (EMR), health data and standards, big data sourcing, and mining methods for decision making and health improvement.
MSA 585Special Topics in Business Analytics (3 cr.)
The course will present selected topics in Business Analytics that are of interest. These topics may include emerging areas not covered elsewhere in the MSBA program, or topics covered in much greater depth in this course than in other existing courses in the program.
In this course, students get the opportunity to integrate the knowledge and apply the skills acquired in the core curriculum learned throughout this program to a real-world business project. In most cases, projects will be submitted by business partners or identified by the faculty advisor. Depending on the business sponsor, projects could be done on-site as internships. Students in small teams will tackle the business problem from data collection and model construction through analysis and presentation of results to recommendations for specific business decisions. Faculty advisors will supervise the teams through all the phases of the project. Each team is expected to provide effective solutions for the client while demonstrating their extensive analytical. Commercial and open source software tools will be used to build models and conduct analyses. In addition, throughout the semester, project management concepts will be discussed and applied by students to manage their project.
