Bachelor of Science Accelerated (BSA) in Data Analytics
Overview
The data analytics major offers a 36-semester-hour course of study that provides learners with technical and analytical skills necessary for data analytics professionals in all industries. The curriculum provides students with a comprehensive foundation in computer information systems, data governance, data modeling, and analysis tools, as well as data visualization tools and techniques for presenting data to multiple audiences. Students will develop the skills to communicate and interact effectively with administrators, stakeholders, and business units. This degree also prepares learners for graduate studies in data analytics and data science.
Bachelors of Science Accelerated (BSA) in Data Analytics credit requirements:
- Bachelor's degree requirements
- General Education (36 credit hours)
- Electives (18 credit hours)
- Major courses (36 credit hours)
Program Learning Outcomes
Upon successful completion of the Bachelor of Science in Data Analytics (BS, BSA) students will be equipped to:
- Articulate characteristics of commonly used applications and technology infrastructure that support the business environment.
- Conduct a business needs assessment and workflow analysis.
- Identify sources of data or information.
- Develop data collection techniques including extraction, exploration, cleansing, mapping, and validation of data.
- Complete data analysis including querying, determining, and applying the appropriate statistical methodologies, observing changes and variations in data and visualizing data for analysis.
- Conduct data interpretation and reporting, including creating visualizations and communication to stakeholders.
- Explain how personal and professional biases can inhibit data stories.
- Ensure data governance principles are followed, including security policies.
- Develop process models, diagrams, charts, and reports to clarify business processes and identify issues.
- Design and develop reporting tools and dashboards.
- Demonstrate effective use of data project methodologies.
Course Requirements
Students are required to obtain a minimum grade of C- in all courses taken for the major.
Major Requirements
Complete the following:
In this course, students will study fundamental concepts of data and information management with primary focus on database systems, including identifying organizational requirements, conceptual data modeling, logical and physical database design, SQL, and database administration tasks. Students will be introduced to Structured Query Language (SQL) and will learn how to use Data Definition Language (DDL) and Data Manipulation Language (DML) commands to define, retrieve, and manipulate data. This course covers differentiations of data—structured vs. unstructured and quasi-structured. It also covers aspects of data management, including quality, policy, and storage methodologies. Foundational concepts of data security are included.
In this advanced course, students will engage in an in-depth analysis of worldview formation, with a focus on its implications for personal identity, professional paradigms, interpersonal communication, ethical decision-making, and faith development. Students will evaluate diverse perspectives and theological frameworks, including a discipline-specific exploration of the foundations of the Christian faith and its historical and societal impact. The course will guide students in refining a personal worldview that shapes their understanding of work, fostering a deeper integration of faith, reason, and ethical responsibility.
In this course, students will learn how to apply basic data analysis tools using SAS and Python. Fundamental programming concepts are covered for each language. These include data types, variables, introduction to regular expressions, decisions, iteration, and introduction to collections using arrays, lists, and key-value pairs. Through this course, students will learn foundational concepts of data management and visualization. The importance of securing data is stressed throughout the course.
This course explores topics such as data process management, risk management, security, and data quality. Students will develop a sample data governance plan. This course also looks at data ownership and the issues of rights, responsibilities, and privacy related to the ownership of data. Legal and ethical issues are also discussed.
Students will learn and apply statistical methods to data through real world scenarios and data sets. Topics in the course will include, but are not limited to, advanced applications of the normal distribution, random variables, hypothesis testing, types of errors, analysis of variance (ANOVA), advanced regression analysis, correlation, and graphing/display methods.
Students will discover various approaches to design a comprehensive methodology that best suits a team, projects, and organizational needs. Students will examine the data science life cycle or methodology, including documentation and team dynamics.
This course provides a detailed overview of system analysis and design methodologies. Students will examine techniques to develop systems more efficiently, such as the system development life cycle and other processes. System requirements, functional design, display, and end-of project conclusions are studied and practiced through a variety of activities.
During this course, students will learn how to identify, understand, explain, demonstrate, evaluate, visualize, and present data in a meaningful way using real-world issues within a social science perspective. As a result, students will develop a better understanding of how data is created, used, and understood.
Complete the following:
This course provides a detailed overview of decision-making systems, models, and support in organizations. The course covers many fundamental topics, including analysis and development of decision support systems, knowledge acquisition and representation, knowledge management, and the strategic value of analytical knowledge. Students will use software tools to analyze data.
This course introduces students to the process and main techniques in data mining, data modeling, and machine learning, including exploratory data analysis, predictive modeling, descriptive modeling, and evaluation. Modeling techniques are examined and used to better understand current data to improve performance. Regression techniques, machine learning, and other tools are used to examine data and conduct predictive analysis. Real-world case studies are examined. Prerequisite: DATA 424: Statistical Data Analysis
This course focuses on how to prepare data that has been collected and analyzed for decision-making through the use of appropriate reporting formats, including graphs, charts, and diagrams. Data reporting and visualization tools are examined and evaluated. Through this course, students become more effective and engaged producers and consumers of information.
The capstone course aims to provide students with an opportunity to integrate and apply the algorithms, methods, and tools they have learned throughout the program to solve real-world data analysis problems. Students will conduct a project that involves the main aspects of the data analytics process. They will submit a consolidated report and give a presentation at the conclusion of the project. Students gain experience participating in project planning and scheduling, writing reports, giving presentations, and interpreting results in a professional manner.