## Data Science and Analysis (40)

By Li M. Chen, Zhixun Su and Bo Jiang This book contains state-of-the-art knowledge for researchers in data science. It also presents various problems in BigData and data science. We first introduce important statistical and computational methods for data analysis. For example, we discuss the principal component analysis for the dimension reduction of massive data sets. Then, we introduce graph theoretical methods such as GraphCut, the Laplacian matrix, and Google PageRank for data search and classification. We also discuss efficient algorithms, the hardness of problems involving various types of BigData, and geometric data structures.

By DAVY CIELEN, ARNO D. B. and MEYSMAN MOHAMED AL This book is an introduction to the field of data science. Seasoned data scientists will see that we only scratch the surface of some topics. For our other readers, there are some prerequisites for you to fully enjoy the book. A minimal understanding of SQL, Python, HTML5, and statistics or machine learning is recommended before you dive into the practical examples.

By Deborah Nolan and Duncan Temple Lang Data Science in R A Case Studies Approach to Computational Reasoning and Problem Solving. The books will appeal to programmers and developers of R software, as well as applied statisticians and data analysts in many fields. The books will feature detailed worked examples and R code fully integrated into the text, ensuring their usefulness to researchers, practitioners and students.