Mathematical and Statistical Methods for Data Science and Machine Learning by 
Leadership in Statistics and Data Science by 
Machine Learning and Data Science by 
Digital Political Participation, Social Networks and Big Data by 
Data Science Ethics by 
Ace the Data Science Interview by 
Learning Spark by
Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark's powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables
Statistics for Data Science by
Get your statistics basics right before diving into the world of data scienceAbout This Book* No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs;* Implement statistics in data science tasks such as data cleaning, mining, and analysis* Learn all about probability, statistics, numerical computations, and more with the help of R programsWho This Book Is ForThis book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful.What You Will Learn* Analyze the transition from a data developer to a data scientist mindset* Get acquainted with the R programs and the logic used for statistical computations* Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more* Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis* Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks* Get comfortable with performing various statistical computations for data science programmaticallyIn DetailData science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.Style and approachStep by step comprehensive guide with real world examples
Data Science with Java by
Get your statistics basics right before diving into the world of data scienceAbout This Book* No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs;* Implement statistics in data science tasks such as data cleaning, mining, and analysis* Learn all about probability, statistics, numerical computations, and more with the help of R programsWho This Book Is ForThis book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful.What You Will Learn* Analyze the transition from a data developer to a data scientist mindset* Get acquainted with the R programs and the logic used for statistical computations* Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more* Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis* Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks* Get comfortable with performing various statistical computations for data science programmaticallyIn DetailData science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.Style and approachStep by step comprehensive guide with real world examples
