DS books for the newest ones
1. Data science. John Kelleher, Brendan Tierney - the book covers the main aspects, from the moment of setting up data collection and analysis, to addressing the ethical revelations that are growing due to privacy policies. The reader will walk you through how to run neural networks and machine learning, and guide you through case studies of business problems and how to solve them. Additionally, they will talk about technical requirements that can be transferred to a greater extent.
2. Practical statistics for Data Science specialists. Peter Bruce, Bruce Bruce - A hands-on textbook presented for data scientists with programming language skills and familiarity with the definition of mathematical statistics. Here, in an accessible way, the main points from the statistics of data science are presented, as well as an explanation of what are the important needs and sides of data analysis.
3. We study the spark. Holden Karau, Matei Zachariah, Patrick Wendell, Andy Konwinski - The authors of the books are the developers of the Spark system. They will talk about the analysis of the execution of tasks with a few lines of code, as well as understand the scheme through examples.
4. Data science. Data science from scratch. Joel Gras - Joel Gras talks about the Python language, elements of linear algebra, mathematical statistics, methods for collecting, normalizing and processing data. Additionally, it provides an information base for machine learning. Describes mathematical models and ways to develop them according to the "k" recipe.
5. Fundamentals of Data Science and Big Data. Davy Silen, Arno Meisman, Mohamed Ali - Readers are introduced to theoretical framework, machine learning sequencing, working with large datasets, NoSQL, detailed text analysis and computational information. Examples are Data Science scripts in Python.