DATA Course Descriptions

In this course, students will learn how to apply basic data analysis tools using their choice of SAS or Python or both. Students will begin to develop research skills by developing a research question and conducting a literature review. Students will develop skills in generating testable hypotheses, understanding large data sets, managing data, conducting statistical analyses, and presenting results to expert and novice audiences. Through this course, students will learn foundational concepts of data management and visualization.

In this course, students will develop and test hypotheses about data through the use of various statistical tests, utilizing data analysis tools of SAS or Python. Students will continue the refinement of their research questions to explore data analysis using statistical tests and tools.

This course focuses on machine learning processes of developing, testing, and applying predictive algorithms to achieve a goal. Students will learn about basic classification, decision trees, and clustering. Students will continue the refinement of their research questions to explore data analysis using statistical tests and tools including machine learning.

In this course, students will apply and refine the data analytic techniques learned from previous certificate courses to address a research question of choice. Using real world data, students will complete a project and present their findings to the class. Prerequisite: DATA 210: Introduction to Data Analysis and DATA 224: Data Analysis Tools

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 course, students will investigate concepts of worldview as it relates to personal identity, cultural assumptions, interpersonal communication, individual decision-making, and faith. Students will explore the roots of the Christian faith and the influence of Christianity on society, seeking to construct a personal worldview that informs their understanding of the meaning of life.

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.

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

Supervised experience in the discipline including internships and practica required for professional programs. This advanced experience must have an on-site supervisor and/or a departmental instructor overseeing, designing, and evaluating the content of the course.

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.