P@SHA invites you to a workshop on Data Science & Predictive Analytics with Raja Tanveer Iqbal.
Introduction to Data Science and Predictive Analytics
According to McKinsey Global Institute’s research, by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge. With an estimated 40,000 exabytes of data being collected by 2020 — up from 2700 exabytes in 2012 — the implications of this shortage become apparent. Further driving this explosion in data collection, and the demand for skilled practitioners, is the wide range of economic sectors that will leverage big data analytics in the next decade, including retail, manufacturing, health care, and government services.
This course will provide a gentle introduction to the techniques that fall under the broader category of data mining, predictive modeling and data science. We will discuss examples from online search, advertising, retail, insurance, social networks, entertainment, education, healthcare, telecommunication and law enforcement – and how each of these industries are using data science to drive actionable insights. The course will also provide an overview of some of the common data mining tasks like regression, classification, clustering, association analysis and outlier detection – without going into theoretical details of individual techniques.
In the end, the instructor will discuss the skills that are needed to become an effective data scientist.
About the Instructor:
Raja has worked in various research and development roles at Microsoft Online Services Division. During his tenure, he worked on various cutting edge techniques that deal with various problems in paid search marketplace, online advertising, relevance in online retrieval, data mining at large scale, predictive analytics and online experimentation. At Microsoft, Raja has been a regular speaker at various tech-talks and tutorials. He delivered a lecture series titled ‘Introduction to Machine Learning’ that has been a recommended resource for new Microsoft OSD employees for many years. He has also given talks on predictive modeling, R programming, online experimentation and A/B testing, relevance in online systems and online advertising.
Raja has published his work on object detection, DNA classification, face detection and texture classification in peer reviewed journals and conferences. His work on face detection presented at NAFIPS 2005 received honorable mention at the conference. He has also served as reviewer for various journals and conferences in machine learning, data mining, artificial intelligence and large scale online systems.
In January 2013, Raja quit Microsoft after catching the entrepreneurship bug. He is currently working on his startup and his data science training and consulting company.