Introductory Courses

CSC-105: Introductory Computer Science at Furman

At Furman, every student’s ability to find, manipulate, analyze and produce information using a variety of sophisticated problem-solving techniques and computing technologies is a high priority. You have several options for initiating such a study – through different themes of the course CSC-105: Introduction to Computer Science. Each section of the course applies fundamental principles of computing to a different real-world problem.

FALL 2026

CSC-105-01 – Data, AI, and Society: How to Read the World Through Visualizations (with Prof Saugat Pandey)

MWF @ 10:30 a.m.

Visualization is everywhere in today’s digital world. From climate reports and public health dashboards to election coverage, sports analytics, and AI-generated insights, visualizations shape how people interpret data and make decisions. Yet many people are not trained to critically read, question, or design these visual representations. This course will introduce students from all majors to the foundations of computer science through the lens of data, visualization, and artificial intelligence. Students will learn how data is collected, analyzed, and communicated visually, while also developing the skills to critically evaluate charts, dashboards, and AI-generated visualizations they encounter in everyday life.

 

Potential topics could include:

The course will emphasize hands-on exploration, including analyzing real-world visualizations (e.g., climate dashboards, public health data, sports analytics), experimenting with AI tools, and creating simple visualizations using introductory programming tools.

 

 

CSC-105-02 – Thinking With Machines: Prompt Engineering and AI Fluency (with Professor Mark Johnson)

T/Th 2:30-3:45

A student pastes a prompt into ChatGPT, gets a confident answer with a plausible-looking citation, and hands it in… only to discover the citation was hallucinated. A small-business owner hooks an AI assistant up to their calendar and watches it quietly double-book three clients. A teacher builds a tutoring bot that works beautifully for the first ten students and starts giving away answers to the eleventh. None of these stories are about a broken AI. They are about people using a powerful tool without a mental model of how it works.

This course introduces students from all majors to the foundations of computer science through the lens of large language models and the practice of building with them. Students will learn how modern AI systems actually work — tokens, context, training, and the reasons they sometimes fail — while developing the computational-thinking skills to design, test, and critically evaluate AI tools they use in their own fields.

 

Potential topics could include:

  • How LLMs actually work under the hood (tokens, context windows, training, and why models hallucinate)
  • Prompt design as computational thinking: decomposition, iteration, and evaluation
  • Building a simple chatbot for a specific domain (a class, a club, a workflow)
  • Automating a real task with AI and measuring whether it actually works
  • Retrieval-augmented generation (RAG): grounding AI answers in real data
  • Ethics, bias, academic integrity, and thoughtful use of AI across disciplines

The course will emphasize hands-on exploration, including experimenting with current AI tools, writing and refining prompts against real evaluation criteria, and building small end-to-end projects — a tutoring assistant, a domain chatbot, or a workflow automation of the student’s choosing. By the end of the semester, every student will leave with a portfolio of working projects and the vocabulary to discuss AI thoughtfully from their own major’s perspective.

SPRING 2027

CSC-105-01 – Biowearables and Human Data (with Prof Haorui Sun)
MWF @ 9:30 a.m.

You wake up after one of your best nights of sleep, and your watch confirms it with a perfect sleep score. As you start your day, you put on your smart glasses and a wristband. With a simple pinch of your fingers in mid-air, you scroll through an inbox right in front of you. When it’s time to respond, you “handwrite” your message in the air and watch the characters appear. This isn’t science fiction but the era we are living in.

Have you ever wondered how these biowearables (devices worn on the body to track biological data) actually work? This course demystifies the technology behind the trend. We will explore the sensors used in these devices and some common physiological signals they collect, such as ECG (heart activity), EMG (muscle signals), and blood oxygen levels. From there, we’ll dive into fundamental computer science concepts and machine learning algorithms that translate raw biological signals into meaningful “smart” features. Finally, we will engage in critical discussions about data privacy: how personal biological data should be stored and regulated, and how it is actually handled in practice.