This course provides an introduction to 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 and 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 developing supervised and unsupervised machine learning applications. The course explores performing, creating, and scripting. Students gain experience in programming python scripts and data science applications and identify how artificial intelligence tools can be used to aid in business decision making.

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

• Write high quality, maintainable programs for machine learning
• Import specialized libraries for artificial intelligence development
• Articulate and demonstrate the value and concepts of artificial intelligence business decision making
• Apply mathematical operations and code aligned with common machine learning techniques
• Evaluate common approaches to artificial intelligence application development

Prerequisite(s): BIA 630

This course introduces students to the complex nature of deep learning. Studying supervised and unsupervised models, students explore the benefits that deep learning offers to businesses. Using Python, students build a neural network model using a specialized 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 neural network
• Construct a neural network model using specialized libraries and tools
• Explain neural network model results for stakeholders

Prerequisite(s): BIA 680

The course utilizes data processing requirements necessary to implement technology-based analytics. The course explores 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 structure 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

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

This course focuses on explaining complex datasets, models, and analysis to a variety of 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