SRM AP’s intra-faculty Data Science course now in collaboration with UC Berkeley

This year SRM AP introduces Data Science for all students in the School of Liberal Arts and Basic Sciences (SLABS). Supported by intellectual capital of colleagues at the University of California- Berkeley, SRM AP’s Data Science course comprises lectures and labs that encourage discussion as students get comfortable in simple programming using Python.

“Recognizing that data science is an essential building block for critical applications of today and the future we are offering it as a foundation course for all SLABS students”, says SLABS Associate Dean, Dr. Shailender Swaminathan.

 “We would like students to understand the scope and role of data in both understanding various facets of the world we live in such as poverty, hunger and disease and problems such as predicting the probability of an individual developing a chronic disease or building a recommendation engine based on past data.”

Data can both broaden and deepen our understanding of the way in which we (firms, society, world, individuals) function – world population, poverty, education and health. Data from publicly available sources can help plot the relationships between some of the global economic/health indicators. Resulting tables/graphs lead to reports on the possible correlates of global poverty. With simple exercises students visually observe the data using plots/histograms.

Dr. Swaminathan uses the example of the recent global financial crisis to point out how Python programming helps with the probability calculations where the basics build up to the main question—Why and how did the global financial crisis actually occur? “Simply put, data science can help us begin to understand the global financial crisis of 2008.”

“Perhaps one of the most exciting aspects of data science is the use of machine learning tools in prediction. Many statistical concepts will be introduced such as linear algebra, nearest neighbour classifications but again the concepts introduced will naturally be right at the tip of the iceberg,” explains Dr. Swaminathan.  “The tools discussed in this class will be used in projects such as predicting the probability that, for example, an individual develops a chronic disease later in life.”

A vast field, data science draws upon several disciplines including information science, mathematics, statistics, and chemometrics. Today, data science also has implications in applied research domains like machine translation, speech recognition, and the digital economy.

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