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

By taking BIA620 Business Analytics, students will become a member of the International Institute for Business Analytics (IIBA). Taking this class will also help students prepare for an IIBA exam.

Our partnership with IIBA allows students and faculty access to a suite of textbooks, professional resources, exam preparation guides, and case studies.

In this course, students learn about advanced business intelligence techniques for data transformation and modeling. The course covers various methods for cleaning, transforming and integrating large datasets, and how to apply models to make strategic, data-driven business decisions.

Student Learning Outcomes:

• Understand the concepts of data transformation, cleaning, and integration and be able to apply these techniques to real-world problems
• Develop skills in using data engineering principles to make strategic, data-driven business decisions
• Perform business intelligence activities through marketing, financial, accounting, and economic analysis
• Develop the ability to critically evaluate and interpret results from advanced business intelligence techniques and be able to communicate these results effectively to stakeholders

Prerequisite:BIA 630 Data Analysis and Business Modeling

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

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 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 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

• BIA 630 Data Analysis and Business Modeling (3 credits)
• BIA 640 Data Visualization and Storytelling (3 credits)
• BIA 645 Communications and Content Strategies (3 credits)
• DIGA 605 Fundamentals of Geographic Information Systems (GIS) (3 credits)

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• BIA 630 Data Analysis and Business Modeling (3 credits)
• BIA 670 Data-Driven Culture (3 credits)
• BIA 671 Information Systems for Healthcare Analytics (3 credits)
• BIA 672 Artificial Intelligence for Healthcare (3 credits)

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Note: Students may choose either BIA 645 or BIA 681.

• BIA 630 Data Analysis and Business Modeling (3 credits)
• DIGA 620 Data Engineering (3 credits)
• BIA 680 Artificial Intelligence Fundamentals (3 credits)
• BIA 645 Communications and Content Strategies (3 credits)

-or-

• BIA 681 Deep Learning Methodology (3 credits)

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This course culminates all the knowledge and skills learned in this program. Students may work with a company to complete a significant project. Students may create their project of significance with a proposal approved by your program director.

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

• Articulate a business problem and apply the data analysis framework to develop a solution
• Apply best-practice data analytics techniques to a business problem
• Apply best-practice business intelligence techniques to a business problem
• Professionally present findings to a project sponsor or another audience or committee
• Implement an analytical solution according to the data analysis framework

Prerequisite: MBA 618 Business Statistics and BIA 650 Data Mining for Decision Making