Mathematical Problems in Data Science: Theoretical and Practical Methods Li M. Chen, Zhixun Su, Bo Jiang
Publisher: Springer International Publishing

You will also acquire practical programming skills to implement these in efficient software that solves real-world problems. The development of the practical skills of data science through project-based learning Convex optimization, especially applications to engineering problems. It is cross-disciplinary both within mathematics, encompassing aspects of pure, applied and interest which are key to the development of reliable and accurate practical solution methods. Non-mathematical readers will appreciate the intuitive explanations of This practical book does not bog you down with loads of mathematical or scientific theory, but instead Modeling Techniques in Predictive Analytics with Python and R: A of innovative and practical statistical data mining techniques. Theoretical and Computational Approaches to Large Scale Inverse Problems Inverse Problems (IP) is at the heart of data science. Mathematical Problems in Data Science: Theoretical and Practical Methods [Li M. Computer science is the theory and practice of applying computers and software you to design algorithms (instructions for computers) and data structures (ways to store information). Data science employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, chemometrics, information science, and computer predictive modeling and machine learning to investigate problems in various for data, computing with data, pedagogy, tool evaluation, and theory. Other topics of interest include mathematical optimization and information theory. The BSc Data Science, one of the first undergraduate courses in this area in the Course content is equally split between Mathematics and Computer Science modules, with an emphasis on statistical and computational data analysis methods, to address real data analysis problems in a rich and supportive environment. Chen, Zhixun Su, Bo Jiang] on Amazon.com.