Professors at WPI often relay how their experience working at an institution with expertise in project-based learning not only impacts the students, but themselves as well.
GQP is a capstone course and most Data Science students take it at the very last semester of their master’s program. Thus, by the time they take the GQP course, they have already taken the course works necessary for their degree. This capstone experience helps them apply their skills learned through course work in a real-world situation.
Students have been very cooperative in many aspects of the GQP process and they have been able to adjust to the level and the need of the projects in a very short time after starting the project and working in a team; however, they need to continue to practice critical thinking, and independently brainstorming solutions to the real-world problems they encountered in these GQP projects.
I have seen a significant change in almost every student as they transition from the beginning of the project to its completion. The amount of learning, contribution, and teamwork is enormous. Many of the students do not have any industry experience when taking the GQP capstone. This course helps all the students in a unique way to get industry experience while at school, and it is an exceptional opportunity provided to our students to work on real data sets and solve real problems in a real company. By the end of the semester, students think like experts in their GQP fields and the GQP experience helps them practice their professional presentation skills, publication skills, and how to perform professionally in a real work situation. I believe our GQP curriculum is not only essential to the students’ individual learning but allows them to hone their skills in applying their learning to real-world situations through collaboration, preparing them to undertake similar projects in their future careers.
GQP success includes effective planning, professional team work, smart, responsible, and caring contributions, continuous communication, coordination among the team and sponsors, and working to meet deadlines. If the students finish their day satisfied that they have learned something new, useful, and impactful, this must be counted as success.
Strong starts and strong project closure combined by great team work, applying fresh data science perspective and technical skills toward the project while explaining their problem-solving skills to the sponsors is a key to the success of the project. We never give up when solving any problem, no matter how hard or complicated the project might be. I think it is a great success when the students learn that with applying their knowledge, willingness to learn new skills from the sponsor relevant to the organization, work as a team, and persisting, they can deliver the product by the end of the semester. We learn in our class that giving up is not an option, and this is the key perspective all students will leave GQP with.
Success is when I see that our GQP work is adding value to our sponsoring companies, as well as the students’ life and the lives of other people in a positive way. Success is when I see that our DS graduate students have contributed in a significant way to finish a challenging real-life team project, meeting all deadlines with high-quality deliverables. We are trying to make a difference in other people’s lives with our GQPs and it has all been a great success.
A faculty advisor makes sure that during the 14 weeks of the project, the students understand the need and the goal of the project and are able to reflect and report the progress gradually. The faculty advisor monitors each one of the projects closely to make sure they are moving in the right direction and if there is a need for change, the student apply the changes so the deliverable is high quality and reflects the impact of their work and what they have learned during their course work.
I love all the work and the experience gained through GQPs. Students gain a unique experience working closely with stakeholders on their real problems. They learn how to tackle a real-world problem with messy and sparse data sets. They learn to give professional presentations, communication, and reporting—which are rewarding and essential for their successful future careers. My day is complete when I see students learn more and more every day and they gain more confidence because of it. These are some of the great things about advising GQP projects.
As founding Director of the interdisciplinary Data Science program here at WPI, I take great pleasure in doing all in my power to support the Data Science community in all its facets from research collaborations, new educational initiatives to our innovative Graduate Qualifying projects at the graduate level.
I completed my Ph.D. in Information Technology from the Department of Computer Science at George Mason University. Before that, I received MBA in Management Information Systems from California State University, Chico and BEng in Electronic Engineering from the Hong Kong University of Science and Technology respectively. My teaching interests include Database Management Systems, Data Mining, Artificial Intelligence, Machine/Deep Learning, Data Analytics, Decision Support System, Data Structures, Computational Algorithms, and Software Application Programming.
