DS 110 course syllabus is found at this link.

Course description:
 This course introduces the student to the emerging field of data science through the presentation of basic math and statistics principles, an introduction to the computer tools and software commonly used to perform the data analytics, and a general overview of the machine learning techniques commonly applied to datasets for knowledge discovery. The students will identify a dataset for a final project that will require them to perform preparation, cleaning, simple visualization and analysis of the data with such tools as Excel and R. Understanding the varied nature of data, their acquisition and preliminary analysis provides the requisite skills to succeed in further study and application of the data science field.
Prerequisite: comfort with pre-calculus topics and use of computers.
Fulfills: N requirement


Students will build:

  • an understanding of the data analytics lifecycle
  • skills in transformation and merging of data for use in analytic tools
  • an overview of simple statistical models and the basics of machine learning techniques of clustering, associations, classification, regression and text analysis
  • an understanding good practices of data science, and conversely recognizing bad practices and why
  • skills in the use of tools such as Excel and R to explore and data mine simple data sets.
  • understanding of the basics of the ethical use of data science