The computer science program includes a balanced mixture of theory and application. Students learn to effectively use the technology of today and create the technology for tomorrow with a thorough understanding of a programming language and structures as well as foundations of computer architecture and networks. Throughout the major, emphasis is placed on the ability to apply understanding of fundamental concepts to solve problems.

Beyond traditional offerings in programming, networking, databases, data structures and algorithms, our major has included some interesting and challenging courses such as Machine Learning, Data Mining, Neural Networks and Computational Biology. Coursework within the major responds to current trends in the field. The newest versions of software are used for teaching, and special courses are offered to introduce new topics.

Why Major in Computer Science?

• Learn computer science by doing computer science in our research-based, hands-on program.
• Develop and hone technical skills through building and updating desktop machines in the science computer lab from raw components.
• Spend the summer doing real, cutting-edge research—and get paid for it.
• Gain practical experience in Houghton’s Technology Service Office supporting the network infrastructure and addressing computer hardware and software issues.
• Work collaboratively with your professors on research projects aimed at solving real-world problems, then publish and present your research at national conferences.
• Join 100 percent of Houghton computer science graduates who are employed or attending graduate school within one year of graduation.
• Develop close mentoring relationships with Christian faculty members who care about your academic, emotional and spiritual growth.

Major Requirements

Computer Science (BS) major

Faculty

Our computer science program allows professors and students to develop close working relationships as we strive together toward the goal of academic excellence and spiritual growth. Student office spaces are right next to professors’ offices, allowing for collaboration outside of the classroom.

Undergraduate Research in Computer Science

A prominent feature of our program is extensive collaborative research between faculty and students, which is offered in various ways including summer research, Honors projects and computer science courses.

Students are actively involved in the whole research process, from reading papers in a particular area and formulating problems to solve to implementing the solutions in various computer languages and, finally, writing a scientific paper to publish. Fourteen papers by our students have been published in different peer-reviewed journals. These papers cover a wide array of topics including siRNA prediction, chemosensitivity, HIV, lung cancer, influenza, computer network security and social networks. Such opportunities can be significant in choosing career direction and getting internships or jobs.

Honors Projects

Students who excel in the standard computer science curriculum are frequently encouraged to take part in an honors project, an extended, multi-semester study that culminates in the production and defense of a final thesis. Some recent project examples include:

• The Impact of Twitter Sentiment on Stock Price
• Condon Bias of Influenza Viruses
• Identification of a Novel Gene Signature for Lung Cancer
• Clustering HIV-1 Sequences using the Ant Colony Algorithm
• Support Vector Machines for Optimizing Multiclass Image Classification Training Time
• Quantum Reinforcement Learning

Summer Research Institute

Each summer, there is an opportunity for a group of computer science students and faculty participate in the Houghton College’s Summer Research Institute. Students in the sciences work closely with faculty mentors on research projects in a variety of topics for a period of five weeks before giving a presentation of their findings. Some recent project examples include:

• Characteristic Sites in the Internal Proteins of Avian and Human Influenza Viruses
• Anomalous Network Packet Detection Using Data Stream Mining
• Twitter Spammer Detection Using Data Stream Clustering
• Community Detection in Dynamic Social Networks