Data Engineering and Data Science: Concepts and Applications

The field of Data Science is incredibly broad, encompassing everything  from cleaning data to deploying predictive models. However, it is rare  for any single data scientist to be working across the spectrum day to  day. Data scientists usually focus on a few areas and are complemented  by a team of other scientists and analysts. Data engineering is also a  broad field, but any individual data engineer doesn’t need to know the  whole spectrum of skills. Data engineering is the aspect of Data Science  that focuses on practical applications of data collection and analysis.  For all the work that data scientists do to answer questions using  large sets of information, there have to be mechanisms for collecting  and validating that information.

Basically, R programming  language has been used, along with some Python libraries to perform  exploratory data analysis on the datasets which have been used.  Different packages or libraries which are available in R and Python have  been explored. Data pre-processing has been performed using Python  libraries.

In this exciting new volume, the team of editors and  contributors sketch the broad outlines of data engineering, then walk  through more specific descriptions that illustrate specific data  engineering roles. Data-driven discovery is revolutionizing the  modeling, prediction, and control of complex systems. This book brings  together Machine Learning, engineering mathematics, and mathematical  physics to integrate modeling and control of dynamical systems with  modern methods in Data Science. It highlights many of the recent  advances in scientific computing that enable data-driven methods to be  applied to a diverse range of complex systems, such as turbulence, the  brain, climate, epidemiology, finance, robotics, and autonomy. Whether  for the veteran engineer or scientist working in the field or  laboratory, or the student or academic, this is a must have for any  library.