This course focuses on the complex nature of analytics at the enterprise level. Emphasis is placed on the techniques companies use to turn information into an asset.  Leadership and communication techniques are examined.  Additional topics include leveraging proprietary data, technology and organizational performance.  Best practices in project management are explored.

• Upon completion of this course, students are expected to be able to do the following:
• Differentiate company products and services by monitoring and analyzing usage patterns.
• Assess the financial aspects involved in a company’s analytics maturity cycle.
• Integrate an enterprise perspective in coordinating the work of analysts to gain the greatest business value.
• Apply business analytics strategy to complex scenarios.

Supervised and unsupervised machine learning is explored. Discussion covers standard data mining techniques using machine learning algorithms, including correlation and association, discriminant analysis, nearest neighbor, cluster analysis, and neural networks.

Upon completion of this course, students are expected to be able to do the following:

• Comprehend the mechanics of machine learning, and multiple techniques such as pattern recognition, or statistical hypothesis testing.
• Apply the data requirements for regressions, classification, and clustering machine learning activities.
• Implement data cleansing, normalization, and standardization techniques.
• Evaluate model accuracy and implement improvements.
• Apply advanced modeling techniques to a variety of business activities.

Prerequisite: MBA 618 Business Statistics and BIA 630 Data Analysis and Business Modeling

NOTE: Students will choose either this course OR BIA 661.

This course focuses on the framework for utilizing the Python programming language and structure for developing advanced analytical models and statistical studies. The course features an interface and application for performing, creating, and scripting. Students practice importing external data to study a business problem and produce a method for communicating an analysis.

Upon completion of this course, students are expected to be able to do the following:

• Write high quality, maintainable Python programs.
• Articulate the value of Python programming language for analytical modeling.
• Develop applications that offer a functional sophisticated interface to the user.
• Demonstrate the concepts and logic of structured computer programming.

Prerequisite: MBA 618 Business Statistics

*Must take Programming for Data Science BIA 661 or BIA 662

Student learn how to build, assess, and support decision support systems such as data warehouses and data marts for data science. Students build a data warehouse. Project management strategies are discussed.

Upon completion of this course, students are expected to be able to do the following:

• Articulate fundamental database concepts.
• Explain the value of different types of databases, such as cloud computing versus localized databases.
• Communicate architecture requirements via industry standard diagrams.
• Evaluate relational table design and pitfalls of poor design.
• Implement an operational data store with the fundamentals of structured query language (SQL).
• Articulate appropriate program management strategies.

Prerequisite: BIA 630 Data Analysis and Business Modeling

This course focuses on the application of economic theory to examine how an organization can efficiently achieve its aims or objectives. The tools and applications used by organizations to make decisions and assess their outcomes in a global context are covered. Topics include advanced supply and demand analysis and estimation, production and cost analysis, market structure and price analysis, regulation and risk analysis, and global pricing practices.

Upon completion of the course, students are expected to be able to do the following:

• Forecast market trends in prices and industry profitability in a global context.
• Assess the competitiveness of a firm within an industry from a production and cost perspective in a global environment.
• Analyze market demand from a competitive and profitability perspective in a global environment.
• Analyze the impact of global forces on market structure and firm behavior.

This course is designed to give students a foundation in applied math and statistics. The tools and applications used in graduate business courses and by individuals in managerial positions are covered. Topics include ratio analysis and comparisons, descriptive and inferential statistics, correlation, analysis of variance, and regression.  Additionally, concepts in algebra, trigonometry, and pre-calculus as used in the analysis of business problems are covered.

Upon completion of this course, students are expected to be able to do the following:

• Apply descriptive statistics.
• Organize, interpret, and analyze data to obtain a quantitative basis to make decisions.
• Utilize algebraic functions to evaluate quantitative situations in business.
• Solve business problems through equations and matrices.
• Apply correlations, analysis of variance, and simple linear regression to business situations.