There is much debate among scholars and practitioners about what data science is, and what it isn't. Does it deal only with big data? Is data science really that new? How is it different from statistics and analytics? One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics.
In an image recognition application, the raw input may be a matrix of pixels; the first representational layer may abstract the pixels and encode edges; the second layer may compose and encode arrangements of edges; the third layer may encode a nose and eyes; and the fourth layer may recognize that the image contains a face. Importantly, a deep learning process can learn which features to optimally place in which level on its own.
Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. This course teaches the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modelling (e.g., linear and non-linear regression).