Endorsements: I am very grateful for the opportunity to have been part of the pioneering class in the first implementation of your Data Intuition and Insight class. To be frank I was confused at first about the actual goals of the class and how the reading, homework, and case studies related to each other. But over time I became more acquainted with making it personal to me by relating the course work to what I want to accomplish in my life. I learned to love making visualizations. I loved getting a glimpse about what it means to be a data scientist. The case studies scared me at first, but I realized how important it was to work with real data. In fact, I am glad we used realistic data from real sources in order to solve real-world scenarios. It gave me a bird’s eye view into the type of career I want. -Trevor Neri-

Welcome to CSE 150. This course will introduce you to data science and provide insight into how to use data to make decisions using visualization and statistical inference. Data scientists spend a significant amount of their time cleaning and manipulating data for use in decision making. In this course, we have crafted real-world data for use in our learning to offset much of the work around data cleaning and manipulation. CSE 250, CSE 350/Math 335, CIT 111, CIT 225 and other advanced analytics courses like CSE 450 and Math 488 can help you build those skills.

We will learn the principles of data storage and management for data analysis and visualization through Google Sheets and Tableau. Neither tool requires the use of a programming language for their use in our class. Both are used heavily in the data science space, and they have many connections to R and Python.

The course follows these principles of teaching Data Science

  • Organize the course around a set of diverse case studies
  • Integrate computing into every aspect of the course
  • Teach abstraction, but minimize reliance on mathematical notation
  • Structure course activities to realistically mimic a data scientist’s experience
  • Demonstrate the importance of critical thinking/skepticism through examples

See here for other great quotes about data science and learning. This Medium article titled, ’Beyond Data Science: Advancing Data Literacy highlights some important goals of this course as well.

Course Outcomes

As a successful learner, you will be able to;

  1. Organize and store tabular data for time-series, spatial, and measured variables.
  2. Calculate data summaries and produce visualizations from data.
  3. Communicate about data with people of varied backgrounds (e.g., novices, database administrators, data scientists, business decision-makers).
  4. Describe the implications of data visualization and summaries in the decision-making process.