Undergraduate Computer Science Capstone Projects: Experiences and Examples in Data Science

Undergraduate Computer Science Capstone Projects: Experiences and Examples in Data Science

Li Chen
DOI: 10.4018/978-1-7998-9016-4.ch012
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Abstract

The CS&IT senior capstone project at the University of the District of Columbia has three components: 1) Senior Seminar, 2) Senior Project I, and 3) Senior Project II. The purpose of Senior Seminar (one credit) is to expand the students' scope of knowledge through reading cutting-edge materials in CS&IT. Students in the class often learn from each other, while the instructor organizes talks given by professors at the university as well as industry professionals. Students give formal presentations as their final exam. The main purpose of Senior Project I (two credits) is to teach students how to start a research project. Students are asked to complete a simple research proposal after doing background research to select a research project. The final report is a short report. In Senior Project II (three credits), many students choose to continue the project Senior Project I. For this course, students complete a well-written report ranging from 20 to 25 pages. This paper explains how we teach these courses and include five examples in data science.
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Basic Components Of Udc-Csit Capstone Projects

In this section, we will describe the objectives and functions of the three course components of our capstone project: (1) Senior Seminar, (2) Senior Project I, and (3) Senior Project II.

The Senior Seminar Class

There are three primary objectives for the Senior Seminar: 1) Learn cutting-edge technology in CS&IT, 2) Refine written communication skills, and, most importantly, 3) Develop public speaking skills.

Course grading is based on: 1) In-class presentations (30%), 2) Writing (30%), and 3) Participation (40%). The most important aspect of the course is the 10–15-minute final presentation.

In the first two to three weeks, the instructor covers general areas of CS&IT, focusing on topics that may be useful to students in their project interests. Students are then divided into groups based on their interests and encouraged to learn about various projects.

In the Spring Semester 2020, students were interested in mobile computing, web programming, cognitive security, scalable energy with AI, and robotics. (I always said that this class mainly talk about “other’s research work.” It is not necessary to make a close connection to your own research in the senior project class.)

In the following weeks, instructors usually choose to give formal presentations on certain popular topics in computer science. For instance, for the previous two semesters, I have given talks on data science. We also invite other faculty members and industry professionals to give talks. Throughout the semester, there are usually 2-3 faculty members and two industry professionals who come and speak to the class.

To improve their presentation skills, students are asked for suggestions on topics they are interested in and video examples of good presentations are posted to Blackboard. Students are also assigned to watch related YouTube videos and write summary essays as homework.

Student are asked to produce a one-page PPT with their title and abstract during class and for the midterm, each student is asked to give a 3–5-minute presentation of their topic for practice. In previous years, there was only one final presentation required. However, this “rehearsal” is a great opportunity for students to receive feedback on their presentation before their final and gain more public speaking experience.

For the final project, students prepare 10-12 PPT slides to give as a formal presentation.

Key Terms in this Chapter

Cloud Service Providers: Are companies that provide cloud computing services. Amazon (AWS), Google (Google Cloud Platform), and Microsoft (Azure) provide cloud computing services.

Machine Learning: Usually means to using data samples to obtain a mathematical model(s) for prediction and classification of unknown data. It is a branch of artificial intelligence.

Data Science: Is emerging research disciplinary area that relates to big data, machine learning, and cloud computing.

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