Definition – Big Data and Data Science – Facets of data – big data ecosystem and data science- Data Science Ethics – Doing good data science – Owners of the data – Valuing different aspects of privacy
Overview- research goals – retrieving data – cleaning, integrating and transforming data-exploratory data analysis – build models – present finds
Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms
Introduction – Deep Feedforward Networks – Regularization – Optimization of Deep Learning – Convolutional Networks – Recurrent and Recursive Nets – Applications of Deep Learning
Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools – Creating an interactive dashboard with dc.js-dashboard development tools
Reference Book:
1 Ian Goodfellow, YoshuaBengio, Aaron Courville , “Deep Learning”, MIT Press, 1st edition, 2016 2 Joel Grus, “Data Science from Scratch: First Principles with Python:, O’Reilly, 1st edition, 2015 3 Cathy O'Neil, Rachel Schutt , “Doing Data Science, Straight Talk from the Frontline”, O’ Reilly, 1st edition, 2013 4 D J Patil, Hilary Mason, Mike Loukides , “Ethics and Data Science”, O’ Reilly, 1st edition, 2018
Text Book:
Davy Cielen, Arno D. B. Meysman, Mohamed Ali , “Introducing Data Science”, Manning Publications Co., 1st edition, 2016
