Dr. Suresh Kalathur
Professor relishes training students for field that “never stops changing”
Dr. Suresh Kalathur
Assistant Professor of Computer Science; Director of Analytics Programs
PhD, MA, Brandeis University; MS, Indian Institute of Technology; BS, National Institute of Technology (India)
What is your area of expertise?
My main areas of expertise are programming languages and analytics. I help 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.
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?
Currently I am teaching Foundations of Analytics with R (MET CS 544) on campus and online.
Can you highlight a particular project within these course(s) that most interests your students?
For Foundations of Analytics, I ask the students to pick their own data sets for class projects, and students have developed some interesting projects. Students pick datasets depending on their passion, and they execute projects using the tools that are covered in class.
What specific professional skills are gained from the curriculum?
Our data analytics courses are geared towards applied concepts. Starting with the two courses Foundations of Analytics and Data Analysis and Visualization with R (MET CS 555), students gain the required expertise in statistics, probability, data description, data distributions, errors, performance estimation, visualization, model evaluation, data mining techniques, web analytics, and web mining.
What are you currently researching?
My current research topics include building better tools to get students to participate interactively in class with the help of on-the-spot labs and giving real-time feedback.