This guide is designed to help connect Data Science students and faculty with Data Science resources in the Gangwish Library and on the web.
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Advances in Big Data Analytics by 
Big Data Analytics by 
R for Data Science by
If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of data science projects, the steps in the data science pipeline, and the programming examples presented in this book. Since the book is formatted to walk you through the projects with examples and explanations along the way, no prior programming experience is required.
Scala Data Analysis Cookbook by
Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipesAbout This Book- Implement Scala in your data analysis using features from Spark, Breeze, and Zeppelin- Scale up your data anlytics infrastructure with practical recipes for Scala machine learning- Recipes for every stage of the data analysis process, from reading and collecting data to distributed analyticsWho This Book Is ForThis book shows data scientists and analysts how to leverage their existing knowledge of Scala for quality and scalable data analysis.What You Will Learn- Familiarize and set up the Breeze and Spark libraries and use data structures- Import data from a host of possible sources and create dataframes from CSV- Clean, validate and transform data using Scala to pre-process numerical and string data- Integrate quintessential machine learning algorithms using Scala stack- Bundle and scale up Spark jobs by deploying them into a variety of cluster managers- Run streaming and graph analytics in Spark to visualize data, enabling exploratory analysisIn DetailThis book will introduce you to the most popular Scala tools, libraries, and frameworks through practical recipes around loading, manipulating, and preparing your data. It will also help you explore and make sense of your data using stunning and insightfulvisualizations, and machine learning toolkits.Starting with introductory recipes on utilizing the Breeze and Spark libraries, get to grips withhow to import data from a host of possible sources and how to pre-process numerical, string, and date data. Next, you'll get an understanding of concepts that will help you visualize data using the Apache Zeppelin and Bokeh bindings in Scala, enabling exploratory data analysis. iscover how to program quintessential machine learning algorithms using Spark ML library. Work through steps to scale your machine learning models and deploy them into a standalone cluster, EC2, YARN, and Mesos. Finally dip into the powerful options presented by Spark Streaming, and machine learning for streaming data, as well as utilizing Spark GraphX.Style and approachThis book contains a rich set of recipes that covers the full spectrum of interesting data analysis tasks and will help you revolutionize your data analysis skills using Scala and Spark.
Spark for Data Science by
Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0About This Book- Perform data analysis and build predictive models on huge datasets that leverage Apache Spark- Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges- Work through practical examples on real-world problems with sample code snippetsWho This Book Is ForThis book is for anyone who wants to leverage Apache Spark for data science and machine learning. If you are a technologist who wants to expand your knowledge to perform data science operations in Spark, or a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience who wants to learn about Big Data Analytics, this book is for you!What You Will Learn- Consolidate, clean, and transform your data acquired from various data sources- Perform statistical analysis of data to find hidden insights- Explore graphical techniques to see what your data looks like- Use machine learning techniques to build predictive models- Build scalable data products and solutions- Start programming using the RDD, DataFrame and Dataset APIs- Become an expert by improving your data analytical skillsIn DetailThis is the era of Big Data. The words 'Big Data' implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages.Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R.With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects.Style and approachThis book takes a step-by-step approach to statistical analysis and machine learning, and is explained in a conversational and easy-to-follow style. Each topic is explained sequentially with a focus on the fundamentals as well as the advanced concepts of algorithms and techniques. Real-world examples with sample code snippets are also included.
