This course provides an introduction to computer programming intended for people with no programming experience. This course is recommended for non-majors in order to get an overview of programming principles and techniques. This course covers the basics of programming in Python including elementary data types (numeric types, strings, lists, dictionaries and files), control flow, functions, objects, methods, fields, and mutability.

This course covers the basic elements of database management systems. It introduces students to the concepts of logical and physical relationships in a data model and the concepts of inner and outer joins. Students will use a computer aided software engineering (CASE) tool to design, create, and query a database.

In this course business students will study graphical representation of data, measure of center and spread, elementary probability, sampling distributions, correlation and regression, statistical inference involving means, proportions, and contingency tables.

Good data analysis can make or break a company. Data should drive innovation, pricing, resources, and even culture. In this course, you will learn how to capture the right data and then know what to do with it. You will learn how to use both free and paid tools to capture and analyze data from various online platforms. The value of data and analytics is that you are much better positioned to make the right decisions; this class will give you a life-long standout skill. This course was developed with the subject matter expertise and oversight of the LDS Business College (LDSBC). See the Course Sharing Policy in this catalog.

This course provides an introduction to business intelligence, including the processes, methodologies, infrastructure, and current practices used to transform business data into useful information and support business decision-making and strategy. Business intelligence requires foundational knowledge in data storage and retrieval, thus this course will review logical data models for both database management systems and data warehouses. Students will learn to extract and manipulate data from these systems and assess security-related issues. Data mining, visualization, and statistical analysis along with reporting options such as management dashboards are addressed.

This course also provides an introduction to analytics, or the automation of analysis, including an overview of qualitative and quantitative analysis methods and methods used to automate these processes for speed, interactivity, and quality (reliability and validity). Several examples of modern types of analytics will be introduced and explored such as descriptive, diagnostic, discovery, predictive, and prescriptive approaches.

Each semester we seek out opportunities to work with real world companies, organizations on campus, or public data to examine how they and/or we can more effectively leverage existing data and how to redesign their data flows to set up improved data analysis.