7 courses with the subject DTAN, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DTAN 101Introduction to Data Analytics3
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.
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.
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.
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.
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.
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.
