Course Catalog

3 Unit(s)

Students will learn the principles, terminology, organizational roles, and application of data analytics in the business, along with the principles and challenges of data strategy and management. They will be introduced to the multi-faceted toolkit of data analytic tools, which will be presented in more detail via the curriculum. Time will be spent understanding the CRISP-DM methodology for developing usable analytics, and the implications of the Internet of Things. Corequisite: BUS 240 with a grade of "B" or better.

View Course Sections: Summer 2024 , Spring 2024 , Fall 2023

3 Unit(s)

In this course, students examine key Master Data Management concepts, methodologies, and processes including definitions, types of master data projects, and the data mastering process. In addition, students will explore what is needed today to utilize all data (historical, descriptive, and predictive) and to convert such data into metrics that have meaning for management. Students will learn and practice an integrated suite of enterprise-wide managerial methodologies and tools that link strategy objectives with tactics using data analysis. Corequisite(s): BUS 240 with a grade of "B" or better.

View Course Sections: Summer 2024 , Spring 2024 , Fall 2023

3 Unit(s)

Provides a comprehensive and in-depth coverage of design and implementation of Business Intelligence (BI) systems in a business enterprise context. Covers data integration (including ETL process), Data Warehousing (including OLAP and Big Data) and Business Analytics (Data mining, data visualization). A focus of this class will be to recognize business problems and business needs that can be addressed with BI methods, and introduce a variety of tools and processes necessary to implement BI systems from requirements definition and business justification to technical implementation. Hands-on exercises will strengthen student's ability to utilize contemporary BI tools such as MicroStrategy, Microsoft PowerBI, Alteryx, and model-based scenarios for descriptive, predictive, and prescriptive analytics. Assignments are designed to combine graduate level research with experience-building transition from theory into tacit knowledge. The term project, which is discussed and worked on throughout the course, allows students to apply what they learn in the class to a data set of their choice to demonstrate mastery of the subject. Prerequisite: MSBA 300 or ACCTG 336 or TA 336.

View Course Sections: Summer 2024 , Spring 2024

3 Unit(s)

Provides the basic knowledge needed to implement processes, tools and data analytics to assure real-time business visibility and control to detect fraud and assure integrity of key business transactions. The student will gain a strong footing to cope with the changes that are to come with the use and ever-growing reliance on computer technology, the evolution of the Internet of Things and the resulting explosion of data. In order to determine the veracity of information, students will examine emerging data analytics tools and emerging AI technologies to learn how to process information using various sources of knowledge and gain insights to predict risk and design methods for its mitigation. The students will be able to design and implement a new class of trust-but-verify business processes as overlays to current business process implementations. After the completion of the course, the student will be able to bring emerging AI technologies to improve enterprise risk identification and mitigation business processes. Prerequisite: MSBA 300 and MSBA 320.

View Course Sections: Fall 2023

3 Unit(s)

This course introduces students to advanced data analytics, an umbrella term referring to a range of exploratory and inferential statistical data analysis techniques used primarily for predictive purposes. Students will learn how to use Python programming language to explore the foundation of predictive analytics used in machine learning and artificial intelligence. Prerequisite(s): BUS 240 with a grade of "B" or better.

View Course Sections: Summer 2024 , Spring 2024 , Fall 2023

3 Unit(s)

Introduces students to advanced statistical theory, e.g. probability distributions, logistic regression, log transform, and time series, through the popular programming language R. Students will use R to perform complex data analysis using various statistical methods in a variety of business contexts. They will also have an opportunity to examine how R compares with commercial platforms such as SAS. This course is offered only to students enrolled in the GGU Worldwide program. Prerequisite(s): BUS 240 with a grade of "B" or better.

3 Unit(s)

Introduces students to advanced statistical theory, e.g. probability distributions, logistic regression, log transform, and time series, through the popular programming language Python. Students will use Python to perform complex data analysis using various statistical methods in a variety of business contexts. They will also have an opportunity to examine how Python compares with R as well as commercial platforms such as SAS. This course is offered only to students enrolled in the GGU Worldwide program. Prerequisite(s): MSBA 320A.

3 Unit(s)

Introduces students to data frameworks supporting the building and manipulation of data sets that do not fit the standard relational database structure, i.e. very large data files and unstructured data. Students will learn how data from these data sets can be extracted, and transformed for workable solutions. They will be introduced to a selection of the tools and languages associated with building and managing Big Data structures, such as Hadoop, Hive, Spark, MapReduce, NOSQL, MongoDB, and others. Prerequisite: MSBA 300 or ITM 300.

View Course Sections: Summer 2024 , Spring 2024 , Fall 2023

3 Unit(s)

Students will be introduced to data analytics, social network and media analytics and web analytics using the R language. They will learn skills, methods and tools necessary for data and social media analytical work in a broad variety of businesses situations with a range of data structures. Students will learn how to acquire information in a variety of forms, such as text (newspaper articles, tweets, blog posts, and social messages), numbers (from web analytics) and transform them into data, which they will be able to analyze applying statistical methods with the help of R. Upon completion of the course, students are expected to be able to complete basic media analysis as part of marketing or competitive research; to run methodologically sound analysis of social media; to report on web analytics; and to apply basic statistical concepts to a variety of analyses. Prerequisite(s): BUS 240 with a grade of "B" or better.

View Course Sections: Spring 2024 , Fall 2023

3 Unit(s)

Designed to teach students the key concepts of predictive analytics used for deriving value from business data. Students will begin this class with a brief introduction to some of the facets of Artificial Intelligence (AI) e.g., NLP, neural networks, deep learning, and robotics. Focus will then shift to gaining an understanding of the algorithms of machine learning and their application to building predictive models using Python. Topics include cleaning and prepping data, supervised learning, training vs. test data, forecasting numeric values with regression, unsupervised learning, and additional tools to simplify noise from data. Finally, students will revisit applications of deep learning, a subset of machine learning. Prerequisite: MSBA 300 and MSBA 320.

View Course Sections: Summer 2024 , Spring 2024 , Fall 2023

3 Unit(s)

Course will cover practical techniques and strategies for analyzing text data to extract meaningful information, discover new patterns, and support decision making and hypothesis generation. It will introduce several text mining applications that apply to domain specific problems. Students will learn the complete set of steps involved in working with text data, from reading the text data to creating categories for additional analysis, and examining the relationships discovered using the text components of SPSS Modeler and other tools. The course will emphasize the importance of finding new ways to extract meaning from text through an "accelerated discovery" process implemented by the emerging IBM Watson cognitive systems. Prerequisite(s): MSBA 300 and MSBA 320.

View Course Sections: Fall 2023

3 Unit(s)

Addresses the need for presentations that report data analytics findings in a clear, actionable format. Multiple formats for presentation are reviewed for appropriateness to the audience. Students will be introduced to the design process and have the opportunity to learn design techniques. Students will learn techniques of storytelling through the development of storyboards. Additionally, they will learn how to design and implement dashboards in a business environment, based on sound data visualization principles and techniques. Students will work on a hands-on project for designing and developing visualizations using Tableau software.

View Course Sections: Summer 2024 , Spring 2024 , Fall 2023

3 Unit(s)

Decision making is a critical part in any business. Prescriptive Analytics provides solutions to businesses worldwide with the advanced analysis techniques and tools. Optimization and simulation are two such methods that are the foundations of prescriptive analytics. In this course students will be able to examine and identify the classical and modern optimization techniques used in today's business environment. Focused on linear and nonlinear programming techniques and their application in the business environment and modern simulation and optimization techniques, this course helps students understand the need and use of decision-making using these techniques. Corequisites: BUS 240 with a grade of "B" or better, MSBA 300, and MSBA 320.

View Course Sections: Spring 2024

3 Unit(s)

This course introduces students to self-service analytics (SSA), which aims to make business users more productive and less dependent on IT for their reporting and analytics needs. The course will appeal to business users as well as IT professionals. Topics covered in this course include an introduction to SSA, relationship with BI, capabilities of a data analytics platform, as well as the benefits for the organization, business users and IT. The course will teach how to assess if an organization is ready for an SSA platform and, also, how to plan and implement an SSA project. The course will enable students to identify the different types of users expected to use the SSA platform and how they can be mapped to the architecture. Since data is the lifeblood of an SSA platform, various data-related topics will also be covered, such as metadata, data pipelining, and governed data flow. Also covered will be the SSA architecture, as well as its components and tools. Other topics covered include data governance, security, training, data and user onboarding, and barriers to adoption, as well as challenges, common mistakes, best practices, lessons, and tips.

View Course Sections: Spring 2024

3 Unit(s)

Artificial Intelligence (AI) is seeping into every sector and increasingly transforming and innovating a variety of businesses. This course introduces students to the application of AI across many industries and how different businesses view the role and strategic value of AI. The students will examine business applications of AI in a range of vertical business segments, such as finance, healthcare, transportation, among others, and identification of key business values and benefits afforded by AI across each of these verticals. The students will also review the ethical consideration of AI and its promise and perils to both business and society at large. Prerequisite(s): MSBA 300, MSBA 301, MSBA 305, MSBA 320, MSBA 326, and ITM 304.

View Course Sections: Summer 2024

3 Unit(s)

Provides the students an opportunity to demonstrate knowledge and skills gained through the degree program by analyzing and developing solutions to case studies representing real situations. In addition, each student is required to complete a field research assignment (practicum) in order to graduate. The Program Director will work with each student to determine their assignment. Prerequisite: Students must complete 27 units of the program, including the following: MSBA 300, MSBA 301, ITM 304, MSBA 305, MSBA 320. Students should be aware that any practicum opportunities may be dependent on courses already completed.

View Course Sections: Summer 2024 , Spring 2024 , Fall 2023

3 Unit(s)

Provides the students an opportunity to demonstrate knowledge and skills gained through the degree program by analyzing and developing solutions to case studies and/or simulations representing real situations. In addition, each student is required to complete a field research assignment under the guidance of faculty.

3 Unit(s)

Addresses significant, topical and practical problems, issues, and theories in business analytics. Topics are compiled and selected by the department chair. This course may be taken more than once, provided the same topic is not repeated. Prerequisites will vary based on topic.

3 Unit(s)

Human Resources, as an organization, is in a unique place to use massive amounts of company data to drive HR performance which can impact both the employee and the organization. Still, given the vast amount of data available within HR, companies are struggling to use data driven analytics and metrics to drive such performance. This course will focus on identifying the most useful sources of data, how to collect information in a transparent way such that data privacy is protected while analytics changes data to tangible insights. The student will cover all key elements of HR including recruitment, employee engagement, well-being, and training. Through various analytical analyses, the student will examine ways data can contribute by optimizing processes and strengthening HR decision-making leading to a more competitive organization.

View Course Sections: Fall 2023

3 Unit(s)

Provides an opportunity for the advanced student with a specific project in mind to do reading in a focused area and to prepare a substantial paper under the direction of a faculty member. Only one directed study course may be taken for credit toward a master's degree. Prerequisite: consent of the department.

3 Unit(s)

Introduces students to advanced statistical theory, e.g. probability distributions, logistic regression, log transform, and time series, through the popular programming language R. Students will use R to perform complex data analysis using various statistical methods in a variety of business contexts. They will also have an opportunity to examine how R compares with commercial platforms such as SAS. Prerequisite(s): BUS 240 with a grade of "B" or better.