Bachelor of Science in Data Science

3 Credits

This course introduces practices in data analytics foundational to the industry and the field of data analytics. Covered are theories of analytics, the phases of the analytics lifecycle, analysis and exploration of data, relevant statistics for data modeling, data visualization, and the tools used for data analytics.

3 Credits

This course provides students with a basic understanding of programming development practices. Concepts covered include the application of algorithms and logic to the design and development of procedural and object oriented computer programs to address the problem solving requirements associated with business information systems. This course will cover procedural programming concepts, including data types, controls structures, functional decomposition, arrays, files, classes, and objects.

3 Credits

This course covers the three most popular data programming languages: Python, Java®, and Ruby. Students will be introduced at a high level to the programming languages as they relate to database design, development, and management.

3 Credits

This course provides an overview of advanced spreadsheet functions used in data analysis. Topics include filters, sorts, lookups, formulas, functions, macros, pivot tables, graphs and charts, dashboards, forms, reports, queries, statistical analyses, and data validation.

3 Credits

This course provides a foundational workshop on data structures, which is a functional component of business intelligence (BI) within an organization. Building upon basic programming and database structure, this course builds competencies for the student to recognize the structure of data contained in deployed enterprise information systems.

3 Credits

This course provides an introduction to the Python programming language. Students will review the function and use of variables, data types, lists, tuples, functions, classes, files, exceptions, search/sort algorithms, memory management, and code testing. Additional topics may include Integrated Development Environments (IDEs), data visualization, global data sets, and Web Application Programming Interfaces (APIs).

3 Credits

This course covers fundamental database concepts including SQL, non-relational databases, distributed databases and database architectures. Students are provided an overview for how to manage, display, sort, group, retrieve, and organize data for the application or tool associated with the software development taking place within an organization

3 Credits

This course provides an overview of R, which is a programming language commonly used for data science, statistical analysis, and data visualization. Topics include syntax, variables, operators, functions, data structures, statistical data sets, and graphical presentation.

3 Credits

This course provides an overview of applied business research, statistical tools, and data analytics used to support strategic decision making. Students will examine methods used to frame business problems, conduct research, collect and evaluate data for validity and reliability, determine patterns and trends in performance, and effectively present conclusions.

3 Credits

Structured Query Language (SQL) is a domain-specific language used to store, query, and manipulate data within databases. This course provides an overview of SQL and relational database applications with a focus on SQL syntax, data types, keywords, functions, structured and unstructured data, and data modeling.

3 Credits

This course provides an overview of database administration, including database implementation, configuration, authentication, monitoring, backup, and security. Students will examine database architecture with a focus on business requirements, data integrity, and the ethical handling of private data.

3 Credits

This course provides an overview of Exploratory Data Analysis (EDA), including the cleaning, preparation, exploration, and visualization of data. Students will review machine learning algorithms, neural networks, and Artificial Intelligence (AI) concepts used to analyze data sets and build intelligent applications.

3 Credits

This course will introduce students to data analytics and modeling through contemporary business tools. Students will examine how data is architected and organized. After completion of this course, students will be able to understand the different data models used to store data, several schemas that drive how data is structured, and provide other database concepts relating to the design and architecture of data.

3 Credits

This course covers data mining techniques for structured and unstructured data. Learn programming techniques and algorithms to transform large quantities of data into usable Business Intelligence (BI).

3 Credits

This course introduces students to tools and techniques needed to present data, both simple and complex, to gain support and recommend business decisions. Student will use graphical tools such as graphs, animation, and presentation techniques to communicate the message behind the data and improve decision making. Topics include requirement gathering, graphical representation of data sets, visualization techniques and tools, and data validation.

3 Credits

This course reviews the origin and applications of big data within the framework of current businesses. Topics include data validation, processing, and predictive analytics used in a timely manner to make informed decisions.

3 Credits

This course provides an overview of machine learning, including the use of algorithms, neural networks, and supervised, unsupervised, and reinforcement learning to automate advanced computer tasks.

3 Credits

This course provides an overview of predictive modeling techniques, with a focus on statistical analyses, data mining, and machine learning tools used to find patterns and trends, identify potential risks, and forecast probable outcomes within various data sets.

3 Credits

This capstone course combines the skills and applies the knowledge from various courses in the Bachelors of Science in Data Science program. Student teams working with large data sets combine the concepts of statistics, programming, data mining, data analysis, and data visualization to design, implement, and present a data science project representing a real-world application to decision-making executives.