Course Search Results

  • 3.00 Credits

    Success in a global workforce requires the ability to design with and for various communities, groups, and cultures. This course will prepare students to look at application and web design principles through a global lens to create appropriate and culturally responsible designs. This course will also explore accessibility issues in modern web design and identify methods by which applications can become more accessible to wider audience to improve user experience
  • 3.00 Credits

    This course, built in collaboration with Google, will teach you how to understand and use data structures. Data structures are used by almost every program and application to store, access and modify the vast quantities of data that are needed by modern software. By the end of this course you'll learn what data structures are and learn how to use them in the applications you build. This online course has optional live sessions
  • 3.00 Credits

    This course explores algorithms from a coding-focused perspective, using Python. Students will learn about the issues that arise in the design of algorithms for solving computational problems and will explore a number of standard algorithm design paradigms and their applicability. Students will also become familiar with concepts of runtime, recursion, implementation and evaluation. This course features a heavy emphasis on practical application of algorithms to common development and engineering challenges
  • 3.00 Credits

    In this course, students will propose and build a website from scratch, based on the demands of a fictional client base. Students will utilize all components of proper web design: intent, design, and execution. This project-based course will combine skills from previous courses in user experience, graphic design, backend development, and project management
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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