Assistant Professor, Computer Science Director, Analytics
Dr. Kalathur’s interests include autonomous agent systems, enterprise Java applications, object-oriented design and analysis, and operating systems security. He served as a lecturer and adjunct faculty at Tufts University and Worcester Polytechnic Institute, and he developed software and systems for several organizations and agencies. Kalathur teaches courses in data analytics, programming languages, and web technologies.
- MET CS 544 – Foundations of Analytics
- MET CS 602 – Server-Side Web Development
- MET CS 701 – Rich Internet Application Development
Kalathur, S. “Mining a Web Security Portal—A Case Study.” Proceedings of the 2011 International Conference on Information and Knowledge Engineering (Las Vegas, Nev., 2011).
Burstein, L., Chitkushev, L., Haines, E. M., Kalathur, S., Saito, M., Schudy, R., Willett, S., and Zlateva, T. “Dimensions of Course Design and Delivery and Their Effect on Student Satisfaction/Perception in Online Learning.” Proceedings of the 7th Annual International Conference on Computer Science and Education (Sofia, Bulgaria, 2011).
Brusic, V., Chitkushev, L., Kalathur, S., Zhang, G. L., and Zlateva, T. “Visualization Tools for Presenting and Analysis of Global Landscapes of Vaccine Targets.” Proceedings of the Bioinformatics and Biomedicine Workshops on Informatics Applications in Therapeutics (Atlanta, Ga., 2011): 683–88.
“Assessment Methodologies in Information Structures Online Course.” 6th International Conference on Frontiers in Education: Computer Science and Computer Engineering (Las Vegas, NV, July 2010).
Zlateva, T., Saito, M., Kalathur, S., Schudy, R., Temkin, A., and Chitkushev, L. “A Unified Approach for Designing, Developing, and Evaluating Online Curricula.” Proc. 6th Annual International Conference on Computer Science and Education in Computer Science (Fulda, Germany, June 2010).
“Enriching student experience with student driven content while teaching an online data mining class.” In Proceedings of the 9th ACM SIGITE conference on Information technology education, 125-130. Cincinnati, OH. October 16-18, 2008.
“Trends in Computer Science.” Geethanjali Institute of Science and Technology, Nellore, India, August 2010.
“Assessment Methodologies in Information Structures Online Course.” 6th International Conference on Frontiers in Education: Computer Science and Computer Engineering, Las Vegas, Nev., July 2010.
“A Unified Approach for Designing, Developing, and Evaluating Online Curricula.” 6th Annual International Conference on Computer Science and Education in Computer Science, Fulda, Germany, June 2010. Co-presented with Chitkushev, L., Saito, M., Schudy, R., Temkin, A., and Zlateva, T.
What is your area of expertise?
My main areas of expertise are programming languages and analytics. I helped to coordinate the analytics curriculum here at Metropolitan College. There is a great need for data analysts, data scientists, business analytics and business intelligence developers, and others who can apply skills in data analytics. There is no shortage of opportunity, and in fact there are more jobs available than there are people to take them.
What about teaching inspires you?
The field of computer science never stops changing, and as soon as the students see something new it always excites them. As a professor, I am also learning new techniques and technologies for myself that I am able to incorporate into the coursework so that the students can apply them in a practical way. Most of our students are working in the industry, so a big motivator for them is to apply the techniques that they have learned from their classes in their careers. They immediately see the benefit, and are often recognized by their peers as well.
What courses do you teach at MET? (online and/or on campus)
I teach many courses, including Data Mining (MET CS 699), Web Analytics and Mining (MET CS 688), Foundations of Analytics with R (MET CS 544), and Rich Internet Application Development (MET CS 701).
Can you highlight a particular project within these course(s) that most interests your students?
For Foundations of Analytics with R (MET CS 544), I ask the students to pick their own data sets for class projects, and students have developed some really interesting projects. It could be weather data or exercise data, depending on their passion, and they execute projects using the tools that are covered in that class.
What specific professional skills are gained from the curriculum?
Our data analytics courses are geared towards asset programming language. We started with Java as the foundation course, and now we also have Python as a foundation course, and those are the new skills that students would learn as they go through the data analytics program. Students will gain the required knowledge of statistics, probability, data description, data distributions, errors, performance estimation, visualization, model evaluation, data mining techniques, web analytics, and web mining. At MET, courses are practical and application-based, so we use tools such as R, SQL Server, Oracle, Google Analytics, and so forth.
What are you currently researching?
My current research topics are programming languages on the server side, as well as tools and techniques for data mining. Some of the work that we did includes analyzing Turing evaluations and comparing them against the online materials that we post for the courses.
What advice do you have for new students?
Computer science is a constantly evolving field. The courses are designed to keep you abreast of the latest technologies being pursued in this area. Along with the theory and practical industry applications, take advantage of these courses to stay ahead in the field.