This will only take a moment.
This will only take a moment.
The Online MSBA program from Seattle University is designed to prepare you for the realities of messy data sets and unclear business obstacles. Develop key quantitative skills, including R and Python programming, Apache Hadoop and Spark, SQL, and more.
At Albers, we know that your success in business analytics requires that you hone your communication and leadership skills alongside your technical capabilities. The Online MSBA will challenge you to master written, oral, and visual communication so that upon completing the program, you will emerge as an ethical and adaptive leader of business analytics, able to translate data into decisive action.
Each Online MSBA course incorporates one or more Data Translation Challenges designed to help you develop a methodological approach to data analysis geared toward its eventual presentation to a diverse business audience.
In each of these assignments, you will follow a three-step approach for analyzing and understanding complex data and synthesizing that data to develop informed business strategies:
While Data Translation Challenges may vary in size and significance to your grade from class to class, each one is designed to model the business challenges you will likely face in an analytical role and help you grow into a data-fluent, multifaceted professional who can overcome them.
All new students in the Online MSBA program are required to complete a prep course in the R and Python programming languages prior to the beginning of their first term. This six-hour course will be offered online and may be completed before or during your New Online Student Orientation.
Designed for those with or without coding experience, the course helps students develop the beginner skills and experience needed so they are fully prepared for their MSBA coursework. During the prep course, students will:
Some Online MSBA students may be required to complete some or all the following corequisite courses in order to graduate from the program:
These corequisite courses will be offered online and must be completed prior to beginning the Capstone course. They will be graded on a pass/fail basis, and they will not count toward the credits required to complete the Online MSBA degree.
Students may be eligible to waive some or all of these courses depending on their prior undergraduate coursework or work experience. Contact an Admissions Advisor for more information.
This course reviews key statistical concepts and provides a conceptual introduction to regression analysis.
This introductory-level course uses the Python programming language. Topics include expressions, control logic, data structures, and functions. We will demonstrate usage of several Python data analytics modules. Students will learn how to design programs to solve problems drawn from real-world examples.
This course will examine the opportunities and challenges introduced by business analytics through the perspectives of the law and ethics. Rapidly evolving technologies that permit the collection, storage, aggregation, analysis, and use of data create opportunities for financial benefit and the common good, but also create challenges to legal rights such as privacy, equality, and dignity, and to ethical values such as autonomy, trust, and virtue. The course will be framed as a contextual examination of business analytics to facilitate learning about legal and ethical standards for private organizations using data analytics techniques in various stages of the data life cycle. This is a dynamic course which presents a rich basis for student learning and contemplation of central questions for “big data,” including issues related to acquisition and use of data, professional and social responsibility in the application of modern technologies, the efficacy of management by algorithm, and the loss of human control in using artificial intelligence. The following are examples of legal and ethical issues that may be included, subject to time constraints: In law: information privacy law such as U.S. tort law, federal statutory and administrative law, and constitutional protection of civil liberties; European Union data privacy regulation; cyber intelligence and cybersecurity regulation; contractual liability, specifically with respect to third-party reliance on data analysis; the law of negligence; and agency law. In ethics: adverse effects of data collection on vulnerable populations; transparency and honesty in the cleaning, processing, and visualization of data; introduction of the machine equivalent of implicit bias in feature selection; and responsibilities when using data analysis as a tool to guide human decision-making. Registration restrictions may be bypassed by the department with permission of instructor.
This course teaches the essential and practical skills necessary to communicate information about data clearly and effectively through written, oral, and graphical means. Students will learn and practice with advanced visualization tools to effectively communicate. The course will build from the understanding of data to the presentation of the analysis. Data visualization “storytelling" will provide tools to effectively: communicate ideas, summarize, influence, explain, persuade, and provide evidence to an audience. Visualization can convey patterns, meaning, and results extracted from: multivariate, geospatial, textual, temporal, hierarchical, and network data. During the course, students will deliver presentations using these techniques, and they will also learn to critically evaluate other presentations.
This is an intermediate course in computational problem-solving using Python with a focus on using medium to large datasets to inform business decisions and strategy. Skills developed include information visualization, simulations to model randomness, computational techniques to understand data, and informative statistical techniques. Some exposure to optimization problems and dynamic programming, computational statistics, and machine learning.
Techniques for applied business forecasting with emphasis on time-series methods. A survey of regression-based and time-series methods, models for stationary and non-stationary time series, estimation of parameters, computations of forecasts and confidence intervals, and evaluation of forecasts.
“Big data” is a term applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process within a tolerable elapsed time. Big data tools have been evolved to the application of analytic techniques to very large, diverse data sets that often include varied data types and streaming data, i.e., parallel computing, MapReduce, NoSQL, etc. This class will discuss big data tools, analysis, and use cases.
This course introduces several important modeling approaches for decision-making problems. The first part of the course focuses on deterministic optimization problems including linear, integer, and possibly dynamic programming. Applications that may be used during the course include: allocation of advertising and sales effort, artificial intelligence models, revenue management, and production and distribution systems planning. The second part of the course addresses decision-making under uncertainty. Topics include: a brief review of probability theory, an introduction to stochastic processes, and Monte Carlo simulation. Illustrations used during this part of the course may be drawn from areas such as consumer behavior (learning, purchase timing, purchase incidence, or brand choice), scheduling of operations, performance of computing systems, and sales forecasting. The emphasis throughout is on understanding the problem, formulating a suitable model, finding a solution, interpreting it, and performing sensitivity analysis. The course seeks to provide an intuition for how different techniques work, along with experience in applying them to real problems and in presenting results and recommendations in a clear and persuasive manner to specialists and non-specialists alike. Students will complete a short, collaborative term project that requires them to recognize, model, and solve a real-world problem using the methods learned in the course.
This course introduces the relational database systems and SQL for analyzing data for business analytics. Specific topics include data definitions, data manipulations, integrity constraints, scripts, procedures/functions, triggers, and file/index designs.
This course is a continuation of BUAN 5140 (Data Management for Business Analytics I). It introduces the management and analysis of structured, semi-structured, and unstructured data for business analytics. Specific topics include distributed database systems, NoSQL, information retrieval, data warehousing, OLAP, and data security.
Fundamentals of econometrics and use of econometric techniques in financial and economic research and decision-making. Topics include simple linear regression, residual analysis, multivariate regression, and the generalized linear model. The course will stress computer applications.
This course focuses on basic principles of artificial intelligence and on applications of AI in business settings, such as customer service, sales, and marketing. An introduction to core AI concepts, systems, and applications, such as natural language processing, voice recognition, robotics, vision, and machine learning, is followed by discussions—and in some cases applications—of key enabling technologies. Case studies are introduced to generate business insights and to help determine when investments in AI produce significant returns. Upon conclusion, students should have a broad understanding of several AI enabling technologies, and how they can be used to support business needs.
Fundamental concepts and technologies for developing machine-learning models to solve business problems, and to provide business intelligence by analyzing massive amounts of data to find interesting patterns that can be used to assist decision-making or provide predictions. Emphasis on choosing an appropriate solution technology for a problem, and evaluating and presenting the solution. Topics covered include regression, decision trees and ensemble methods, and neural learning models; unsupervised learning for clustering and dimensionality reduction; and text processing. Students are expected to analyze real-world data in business using data mining software.
Study of the data mining and machine processes and the knowledge representation techniques that underlie data analysis toolkits. Students will learn to analyze the results from machine learning algorithms, compare and contrast various data mining and learning techniques, and select appropriate algorithms for solving problems. Techniques for results validation and analysis will be examined and ethical issues related to data mining will be discussed.
The Capstone is an application of data analytics in the planning and execution of a real-life development project for an industry partner. Students will work individually or in small project teams to define and carry out an analytics project from beginning to end. Key steps include: scoping the project, locating an industry partner, formalizing a question, finding data sources, determining the method of analysis, implementing the analytical procedure, and communicating the results to the client. This process will help students integrate what they have learned in multiple courses and apply their expertise to solve a problem for a real-world enterprise. This course is typically taken during the last quarter of the student’s program of study.