This course is designed for adults who have identified prior experiential learning equivalent to outstanding degree requirements through a systematic process of goal-setting and self-assessment in the context of degree-planning. Students use the conceptual framework of critical inquiry to demonstrate this learning in an eportfolio. For each course-equivalent credit request, the portfolio will identify the context for the student's learning in the subject matter, trace the progression of the learning over time, and explain how the learning is equivalent to a degree requirement. The student will demonstrate mastery of each learning outcome for the credit request and integrate the learning outcomes in a relevant personal case study in decision-making. Two credit requests that meet the criteria to be submitted to Academic Affairs for evaluation are required to pass CRIT 603. NOTE: Students within 16 credits of their projected degree completion date are not eligible to take CRIT 603.

This course will cover data mining, cleaning, and visualization preparation, including what data mining is and how it pertains to data analytics. Data cleaning and preparation for data analysis will also be covered. Students will have the opportunity to learn about data visualizations, which includes data modeling, mapping data attributes to graphical attributes, and using data visualization tools.

This course will examine the role of data analysis through the lens of multiple business disciplines such as business, health care, and marketing. Students will have the opportunity to explore key areas in the analytical process, including how data are created, stored, and accessed. The course covers how businesses and organizations work with data to create environments in which analytics can drive effective and efficient decision making.

In this class, students will build their foundational toolbox in data science: upon completion, students will be able to use the computer from the command line; practice version control with GIT & GitHub; gain a mastery of programming in Python; data wrangling with Python and perform an exploratory data analysis (EDA) in Python. All learning objectives are achieved through active application of these techniques to real world datasets.