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 662.

This course provides an introduction to the framework for utilizing the R programming language and the framework for developing advanced analytical models and statistical studies. The course features R Studio as 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 R programs and scripts.
• Use the R Studio software for performing complex numerical analysis tasks.
• Articulate the value of R language for analytical modeling.
• Develop applications that offer a functional, sophisticated interface to the user.
• Demonstrate the concepts and logic of structured computer programming.

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

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

This course focuses on the framework for utilizing the Python programming language and the 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

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

BIA 665 Decision Support Systems (3 credits)

Students 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 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 the structured query language (SQL).
• Articulate appropriate program management strategies.

Prerequisite: BIA 630 Data Analysis and Business Modeling

This course focuses on applying economic theory to examine how an organization can efficiently achieve its aims or objectives. The tools and applications organizations use 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 a firm’s competitiveness 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.

This course introduces advanced concepts in predictive modeling and techniques to discover patterns in data, identify variables with the most predictive power, and develop predictive models. Students are introduced to descriptive, predictive, prescriptive analytics, and optimization models. The course utilizes Microsoft Excel to engineer and analyze business models. Students identify the proper use of complete regression, optimization, and exponential smoothing models.

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

• Utilize datasets to develop statistics and probability to predict future outcomes.
• Implement appropriate models needed to analyze and critically evaluate business objectives.
• Develop written and oral communication skills required to report on data-intensive business situations.
• Organize data-intensive content in a professional setting.
• Execute advanced analytics techniques.

This course explores best-practice data visualization techniques. Professional storytelling and graphic design skills are introduced. Students study effective strategies and visualization tools to communicate with business decision-makers.

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

• Source appropriate data to create effective visualizations.
• Construct a compelling story with data to drive business action.
• Design effective data visualizations to communicate information to the viewer.
• Build visualizations that assist in error detection and data preparation.

This course focuses on explaining complex datasets, models, and analyses to various stakeholders, including internal and external organizations and personnel of various disciplines. Audience analysis and effective strategic communication are studied. Students identify and analyze problems. Professionalism in both oral and written communication is expected.

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

• Conduct stakeholder analysis and communications results.
• Identify business problems and create analytical approaches to solve them.
• Write reports based on best-practice data analysis frameworks.
• Demonstrate best practice communication techniques to visualize, explore, and act on data science findings.

Prerequisite: BIA 630 Data Analysis and Business Modeling, MBA 633 Ethics in Data Analytics, DIGA 605 Fundamentals of Geographic Information Systems, and BIA 640 Data Visualization and Storytelling

This course introduces the concepts of spatial data creation, editing, and analysis using GIS software. Emphasis is placed on spatial concepts and understanding and utilizing standard operating procedures. Topics covered include coordinate systems, data creation, derivation, editing, metadata, proximity and overlay analysis, and cartography. Technical proficiency is a primary objective of the course, reinforced by significant practical exercises utilizing GIS software. Examples of how the geospatial industry provides location intelligence to various disciplines are explored.

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

• Apply knowledge of principles, theories, and concepts of spatial data analysis.
• Demonstrate standard techniques for creating, editing, storing, querying, and analyzing geospatial data.
• Uses cartographic design principles for visual storytelling and effective communication.
• Implement practices to promote spatial data integrity based on an understanding of sources of error in spatial data.

This course introduces advanced concepts in predictive modeling and techniques to discover patterns in data, identify variables with the most predictive power, and develop predictive models. Students are introduced to descriptive, predictive, prescriptive analytics, and optimization models. The course utilizes Microsoft Excel to engineer and analyze business models. Students identify the proper use of complete regression, optimization, and exponential smoothing models.

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

• Utilize datasets to develop statistics and probability to predict future outcomes.
• Implement appropriate models needed to analyze and critically evaluate business objectives.
• Develop written and oral communication skills required to report on data-intensive business situations.
• Organize data-intensive content in a professional setting.
• Execute advanced analytics techniques.

Examine ethical and legal considerations when working with healthcare data, including HIPAA, decisions made by providers, payers, drug companies, pharmacies, and medical product manufacturers. Students study real-world cases illustrating data governance in health care organizations, issues in patient privacy, data ownership, and restrictions on data analysis.

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

• Compare the relationship between healthcare data collection laws and analytic needs.
• Evaluate project needs to determine the level of sensitivity required in data set analysis.
• Explain the ethical and legal considerations with healthcare data and personal records.
• Analyze the needs that different organizations have with data governance.

This course explores performance management and technology management for the healthcare industry. This includes patient records, billing, operations, and organizations that support the healthcare industry. Students study healthcare informatics, patient portals, ERP systems, and data repositories such as data marts, data warehouses, and cloud computing. Common software used to collect and query data is also explored.

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

• Evaluate different decision support systems used in healthcare.
• Differentiate between the various data requirements needed in healthcare organizations for analytics projects.
• Execute simple SQL queries to pull data needed for an analytics project.
• Create performance management metrics and measurements within the balanced scorecard framework.
• Identify common entry points and technology in which users can access patient and operational data.

This course covers the collection of data and the use of information as an asset in a healthcare organization. Students learn how to collect and transform data for use in an artificial intelligence project using R-Studio. Students study patterns using machine learning techniques and incorporate business strategies in their analysis.

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

• Assess data quality in patient and operational healthcare data.
• Evaluate standards of excellence in data collection methods for artificial intelligence.
• Build a quality dataset from raw data for use in machine learning models.
• Create data mining predictions with categorical regression and data transformation.
• Implement statistical models to study and improve model accuracy.

Prerequisite: BIA 630 Data Analysis and Business Modeling

This course introduces advanced concepts in predictive modeling and techniques to discover patterns in data, identify variables with the most predictive power, and develop predictive models. Students are introduced to descriptive, predictive, prescriptive analytics, and optimization models. The course utilizes Microsoft Excel to engineer and analyze business models. Students identify the proper use of complete regression, optimization, and exponential smoothing models.

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

• Utilize datasets to develop statistics and probability to predict future outcomes.
• Implement appropriate models needed to analyze and critically evaluate business objectives.
• Develop written and oral communication skills required to report on data-intensive business situations.
• Organize data-intensive content in a professional setting.
• Execute advanced analytics techniques.

This course focuses on the framework for utilizing the Python programming language for artificial intelligence applications. The course explores performing, creating, and scripting. Students gain experience in programming python scripts and data science applications.

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

• Write high-quality, maintainable Python programs for deep learning.
• Import Python libraries for artificial intelligence development.
• Articulate and demonstrate the value and concepts of Python programming language for analytical modeling.
• Apply mathematical operations and code aligned with common machine learning techniques.
• Evaluate common approaches to artificial intelligence application development.

Prerequisite: BIA 630 Data Analysis and Business Modeling and DIGA 620 Data Engineering

This course introduces students to the complex nature of deep learning. By studying supervised and unsupervised models, students explore the benefits deep learning offers businesses. Using Python, students build a neural network model using the Keras framework and interpret the results for stakeholders.

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

• Communicate a conceptual understanding of why neural networks are gaining popularity.
• Compare neural networks with other data structures and the situations that neural networks should be used over other structures.
• Visualize and interpret the components of a shallow neural network.
• Construct a basic deep neural network model using Python and the Keras library.
• Explain deep neural network model results for stakeholders.

Prerequisite: BIA 680 Python for Artificial Intelligence

The course utilizes the data processing requirements necessary to implement technology-based analytics. The course explores the strengths and limitations of various data formats to make better decisions. The importance of structured and unstructured data formats, as well as performing methods of data extraction, transformation, and loading, are covered. Data wrangling methodologies explore constructing custom data pipelines to support efficient analysis. These methods include cleaning, filtering, standardizing, and categorizing data. Processes to review data for accuracy, consistency, and completeness are covered as well as techniques to mitigate error and improve data integrity. The course also investigates legal and ethical considerations of data management.

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

• Perform extract, transform, and load (ETL) processes using structured and unstructured data formats.
• Assess data for error and implement techniques to improve data integrity.
• Determine appropriate data formats for given situations.
• Design and document processes for converting raw data into a product suitable for analysis.
• Identify legal and ethical issues related to the processing and dissemination of data.

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: MBA616 Principles of Economics and Marketing, MBA618 Business Statistics, and BIA650 Data Mining for Decision Making