Business and information technology professionals will learn how to better leverage analytics for their organization’s survival and growth.
Next virtual class: January 25-29, 1:00-3:00 pm New York time (ET), each day
Currently we are in the midst of the next disruptive age of Information Technology. In 1945 electronic computers appeared ushering in what one could call the first disruptive age of hardware. Starting with the mainframes of the 60s to current cloud computing we have seen various hardware instances such as minicomputers, supercomputers, personal computers, handheld computers, and wearable computers. Paralleling advances in hardware, there have been many advances in software: programming paradigms (imperative, object-oriented, functional, concurrent), development methodologies (CMM, agile), and algorithms for solving a range of problems (e.g., systems, networking, AI, machine learning, analytics). Starting around the late 1960s to the explosion of the Web in the early 90s the third disruptive age was in communication—the ability for computer systems around the world to transmit and share data. The combined advances in hardware, software, and communication forms the basis of our current disruptive age of Data. Massive amounts of data (terabytes and beyond) are available in a range of domains: science, commerce, finance, healthcare, social media, real-time sensors etc. At historically unprecedented levels we are able to collect, transmit, curate, and process huge amounts of data at enormous speeds resulting in our ability to do ongoing tasks better and to do tasks we couldn’t do before.
Organizations and businesses need data driven actionable insights. For example, a casino may want to identify whether there is a certain group of customers from which more business occurs—a task known as customer segmentation. A cell phone company may want to know if there is a risk of customers leaving for another carrier—a business situation known as customer churn. A pharmaceutical company may want to build better patient profiles and predictive models to more effectively anticipate, diagnose and treat ailments. Analytic tasks that facilitate such actionable insights include prediction, optimization, recommendation, classification, clustering, etc.
In these series of talks I will introduce techniques and tools for analyzing and distilling actionable knowledge from data.
About Dr. Raja Sooriamurthi
Dr. Raja Sooriamurthi is a Teaching Professor with the Information Systems Program at Carnegie Mellon University, Pittsburgh. His research and teaching interests span the fields of artificial intelligence and software development with a current focus on data-driven decision making. His current courses focus on data management, data science, big data, and introduce students to effective ways of using data for actionable insights.
Paralleling Raja’s interest in machine problem solving is his interest in human problem solving. Along with his co-authors, he has investigated a novel approach to teaching critical thinking and problem-solving termed puzzle-based learning resulting in the book Guide to Teaching Puzzle-based Learning (Springer, 2014).
Since his undergraduate days in India, Raja has been passionate about outreach. For the past 20+ years, he has been active in educational efforts outside his university classroom from elementary school to
professional education. With colleagues from academia and industry, for the past five years, he has been running a multi-month data science outreach effort for local high school students in Pittsburgh,
called Data Jam. Raja is also on the board of directors of Osher @ CMU an adult lifelong learning institute.
In addition to his university courses, Raja has taught several conference and industry workshops in the US, Australia, Kazakhstan, the Middle East (Qatar, The United Arab Emirates), and India. Over the years, since a graduate student, his pedagogical efforts have been recognized with several awards for teaching excellence.
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