Discover the joy of programming: It's not restricted to computer scientists!

Learn Python and R: Two of the most widely used languages, these are the Swiss army knives in your toolbox (for the good).

Acquire some additional tools: Tools like Gephi (for network analysis) are valuable for interdisciplinary studies. No GIS, however (check out an array of DCS courses on GIS).

Course Description:

Computational tools, including programming, are increasingly important across the liberal arts. Such tools, however, cannot be effectively created or used without a fundamental understanding of computation. This course provides a foundation for the use of these tools in conjunction with the critical framework of DCS. A major goal of the course is to teach introductory programming in Python and R, but with a focus on how programming can be used to complement and even to implement methodologies including text analysis, network analysis, and visualization. Students will use these methods in the service of critically engaging with data. E.g., where computer science focuses mainly on problem solving, this course is fundamentally about exploration and often problem discovery. No prior programming knowledge is required.

Prerequisites:

None. If you already know pogramming well, this course is not for you.

Even after 5 years:

• Know: the fundamentals of programming.
• Able to do:
  (1) exercise computational thinking,
  (2) code in Python and R with some reference, and
  (3) understand others' code, including code generated by AI tools.
• Find value in: whatever major you're pursuing now or job you'll do in the future, you'll do it better by utilizing computational methods.

• This is an active-learning course with in-class activities designed for student engagement and learning. Most often, I'll code examples in Python and R, and you'll follow along.
• Due to active learning, you need to come to the class to succeed in this course. Notes from classmates will not help.
• The work for the course consists of two major chunks - labs and projects. The labs are formative in nature, designed to help you build basic skills and practice programming. The projects are summative in nature, designed to require more planning and design.
  • In a normal course week we will have one major topic. Each topic will have an associated lab. That lab will normally be due the following week.
  • There will be three projects, including a final project. The projects will require a lot more work than the labs.
• There is no final exam. Students will do a final project.

• We'll approach this class as a group. We'll help each other and collaborate along the way in ways that are not allowed in Intro to CompSci. You are not competing with your classmates, you should be pulling each other along.
• Our objective is different, too. Whereas Intro to CompSci prepares students for the next CompSci course, goal of this course is to learn computational methods to excel in whatever major you're pursuing.

Unit 1: Computational thinking using Python (9 weeks)

We'll start with computational thinking and its applications using Python programming. Our focus will be on a variety of data, including network data. We'll finish this unit by connecting Python with Gephi for network analysis and visualization (9 weeks). There will be two projects on this unit.

The social network and color-coded communities among the dolphins living in Doubtful Sound, New Zealand. Gephi visualizations like this often requires working with data using a language like Python.

Unit 2: "Data science" using R (5 weeks)

We'll learn R pretty quickly,thanks to our knowledge of Python. We'll then see how R can be used to extract information from raw data. It's true that whatever we can do using R, we can do the same thing with Python. The reason we'll learn R is twofold.
(1) Oftentimes, we must work in an existing echosystem where different languages are used to code different components, and related to this point,
(2) some stuff is just easier to do using R (e.g., plotting, which looks prettier in R, too).

A snapshot of some plots showing the impact of our social influence on contagious disease dynamics. The study was done by Juliana Taube '21 and Profs. Irfan and Zeeman. Complicated plots like these can be conveniently created using R.