Location
BELC, South Wing, S243
Data Science Lab
As the practical application of data science and technology continues to evolve, Bryant University is preparing students to lead in this rapidly growing field. Through the Data Science Lab, students gain hands-on experience with professional tools and work with real datasets generated by industry partners.
The 36-seat lab is designed to support technology-driven courses and active learning for students studying Data Science, Information Systems, and Applied Analytics. The space is highly reconfigurable, allowing for various seating options that encourage group collaboration and adapt to diverse teaching and learning styles. Large touchscreen monitors, whiteboard walls, and adjustable student monitors make the space ideal for broadcasting different materials and fostering an interactive learning environment.
By leveraging advanced technology and a flexible setup, the Data Science Lab equips students with the critical skills and analytical mindset needed to excel in the field.
Highlights
Students benefit from technology and equipment such as:
- Two large touchscreen monitors
- Presenter podium with tech for annotating
- Whiteboard walls
- Moveable furniture to accommodate different settings
- Monitors with screencast capabilities
- Student group monitors with adjustable arms
- Additional moveable monitors for smaller group work
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“The Data Science Lab offers a dynamic learning environment where students work with real-world datasets to tackle business challenges for industry partners, gaining practical experience and delivering impactful results for organizations. Its flexible setup fosters active learning, engagement, collaboration, and skill development in data science and analytics.”
Suhong Li
Department Chair, Information Systems and Analytics
36
seats in the Lab
2
large touchscreen monitors
Selected Courses Offered in the Lab
AA 304: Managing Information for Applied Analytics
This course focuses on the management of information, how it is acquired, stored, and deployed effectively, and how it may be analyzed for applications in a wide variety of domains.
This course focuses on the management of information, how it is acquired, stored, and deployed effectively, and how it may be analyzed for applications in a wide variety of domains.
AA 306: Data Mining for Effective Decision-Making
In this course, students apply analytics to create useful information that provides insights, fosters inquiry, and supports effective decision-making and problem-solving.
In this course, students apply analytics to create useful information that provides insights, fosters inquiry, and supports effective decision-making and problem-solving.
ISA 340: Introduction to Machine Learning
This is an introductory course on machine learning. Students focus on using Python and machine learning libraries such as the scikit-learn library, while also working through all the steps to create a successful machine learning application.
This is an introductory course on machine learning. Students focus on using Python and machine learning libraries such as the scikit-learn library, while also working through all the steps to create a successful machine learning application.
ISA 490: Data Science Capstone
Students complete a comprehensive, real-world data science project and present an analysis of the results to industry partners.
Students complete a comprehensive, real-world data science project and present an analysis of the results to industry partners.
MSDS 620: Market Analytics
Students will explore tools for generating marketing insights in areas like segmentation, targeting, positioning, customer satisfaction, lifetime analysis, choice modeling, and product and pricing decisions using conjoint analysis.
Students will explore tools for generating marketing insights in areas like segmentation, targeting, positioning, customer satisfaction, lifetime analysis, choice modeling, and product and pricing decisions using conjoint analysis.
MSDS 620: Natural Language Processing
This course teaches students how to handle unstructured text for machine learning and deep learning models. Students learn to create text representations and features for tasks like sentiment analysis, topic modeling, text generation, and named entity recognition.
This course teaches students how to handle unstructured text for machine learning and deep learning models. Students learn to create text representations and features for tasks like sentiment analysis, topic modeling, text generation, and named entity recognition.