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Course Criteria
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3.00 Credits
The first course in a two-semester sequence studying user experience design principles, research, and testing. In this first course, students will learn what drives product usability, the basics of User Experience design and research, and how to build wireframes and prototypes. Topics include human cognition, design thinking, evaluating designs, and accessibility and universal design principles
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3.00 Credits
This course builds upon User Experience I and teaches students how to build effective user experiences through a rigorous process of implementing best practices, testing designs and iterating. This course will also cover topics such as branding, color palettes, user journeys, and designing for multiple platforms. To successfully complete this course, students will need to build a mobile app or website prototype and iterate upon it based on user feedback
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1.00 Credits
This course introduces students to the Carlow University curriculum, vision, mission, and resources. It focuses on academic preparation for transitioning to college, and transitioning to Carlow specifically. It promotes intellectual engagement with the liberal arts and seeks to deepen a student's skills in reflective self---exploration. In this course, students will analyze their own academic and career goals and consider the connection between the liberal arts, their major, and career---readiness
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3.00 Credits
In this introductory course, students will learn basic terminology and an introduction to several fundamental aspects of data analytics, including sampling, cleaning, managing, predicting, and exploring data. Students will perform basic statistical analyses on a variety of data sets and will use these statistics to draw conclusions and make data-driven predictions about future events. Students will gain experience expressing these conclusions in oral and written reports to their peers. An introduction to the ethical issues involved in data analysis, storage, and acquisition will also be covered
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3.00 Credits
This course introduces students to R, a widely used statistical programming language, using the RStudio integrated development environment. Students will learn to manipulate data objects, produce graphics, read in tabular datasets, and generate reproducible reports aggregating data into summary tables and appropriate visualizations. Students will also gain experience in applying these acquired skills to various real-world datasets
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3.00 Credits
This course introduces students to Python, a widely used general purpose programming language, using the JupyterLab integrated development environment. Python is a language with a simple syntax, and a powerful set of libraries. As an interpreted language, with a rich programming environment, students will be able to learn to manipulate data objects, produce graphics, read in tabular datasets, and generate reproducible reports aggregating data into summary tables and appropriate visualizations, using a notebook-style development environment. Students will also gain experience in applying these acquired skills to various real-world datasets
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3.00 Credits
Data visualization is a key component of analytics, in which we effectively communicate the meaning of data to an observer through visual perception. This course will cover different types of quantitative and qualitative data and how they can be properly displayed to be perceived well by the reader. We will also discuss some design elements for effective visualization and data storytelling, and we will assess published visuals in the media to determine what separates a good visual from a bad one
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3.00 Credits
This course provides an overview of big data and the types of analytics used to process this data, as well as the associated technical, conceptual, and ethical challenges of dealing with big data. Advantages and disadvantages of big data research are discussed using real-world examples and case studies. This course includes hands-on exercises working with big data in Python
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3.00 Credits
An independent, professional experience for senior data analytics majors within their field, designed in consultation with a faculty mentor. May involve research, an internship, or an independent project. Requires weekly meetings with mentors, plus additional work outside of class to complete the project. Open to data analytics majors only
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6.00 - 12.00 Credits
An independent, professional experience for senior data analytics majors within their field, designed in consultation with a faculty mentor. Student internships must meet departmental and university requirements. Students must meet with the course instructor to discuss and obtain approval for the internship experience. Open to data analytics majors only. 6-12 credits
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