CIS 4567 Big Data Analytics

Essential reading for students and practitioners alike. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. The third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

Get a 360-degree view into the world of Big Data, data science and machine learning. Get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and learn how to write R code for neural networks. For existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.

Combine the power of Apache Spark and Python to build effective big data applications. For Python developers looking for hands-on recipes for using the Apache Spark 2.x ecosystem. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.

Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. Walk through hands-on examples to understand how popular graph algorithms work and how they're applied. Use sample code and tips from more than 20 graph algorithm examples and learn which algorithms to use for different types of questions. Explore examples with working code and sample datasets.