Research on high-performance computing for big data analytics
Shirin Tavara is a PhD student in the Data Science and AI division at the Department of Computer Science and Engineering.
What do you teach?
"I'm involved in several courses within Data Science and Artificial Intelligence, that can be chosen as electives by students at different programmes."
What are you currently working on?
"My research is about high-performance computing for big data analytics. The analysis of big data is commonly utilizing support vector machines, and therefore a large focus of my research is on the parallelization of related data mining algorithms and the implementation of efficient tools for data analysis. Many refer to machine learning algorithms as a black box. In simple words, I work inside the black box and try to improve the performance of the ML algorithms in terms of time, convergence and accuracy."
Why did you choose to go into Data Science?
"I think the beauty of data science is in attracting professionals with diverse backgrounds ranging from mathematics and statistics to economics, psychology, and other professions."
"I have a background in mathematics, and it has helped me to apply pure theories into practice, which has been helpful in my data science career. But the interesting point is that data science is a multidisciplinary field and you might not necessarily have a pure mathematics background. You can enter this interesting world by learning the fundamentals and then apply them to your area of expertise."
Why should students specialise in Data Science?
"We live in the digital world in which terabytes of data are produced every second and we face the natural transition into a data-driven culture. Therefore, being prepared and develop skills to work with data is essential. Students can combine data science knowledge learned in the program with their skill sets that they already have. The combination of the skill sets will provide them opportunities and options to choose from for their future career and opens many doors for high-demand data science jobs in industry or academia."