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“…The concluding chapters will then help you use SignalR effectively to add real-time functionality to your applications, before demonstrating how to implement MongoDB in your apps. …”

“…As if that's not enough, you'll also learn some useful Machine Learning algorithms with the help of Spark MLlib and integrating Spark with R. We'll also make sure you're confident and prepared for graph processing, as you learn more about the GraphX API. …”

“…The book avoids algebra and proofs, but it does supply code written in R for those readers who are motivated to work out examples. …”

“…Some session to note: Machine Learning in R Part I--Jared Lander Machine Learning in R Part II--Jared Lander Machine learning and statistics: Don't mind the gap--Thomas Wiecki, PhD Deep Learning for Developers--Julien Simon Target Leakage in Machine Learning--Yuriy Guts Ensemble Models Demystified--Kevin Lemagnen Towards Interpretable Deep Learning--Dr. …”

“…Introduce yourself to the deployment and usage of SparkR. Walk through the importance of Graph computation and the graph processing systems available in the market Check the real world example of Spark by building a recommendation engine with Spark using ALS. …”

“…Since 1995, he has held several Microsoft certifications that currently include MCT, MCSE for SharePoint, MCSA for Office 365, MCITP for SQL Server(R), MCSA and MCSE for Windows Server(R), and also his PMP certification. …”

“…Drawing on her unsurpassed experience, Madeira offers expert advice on every facet of QuickBooks accounting: data files, banking, A/R, undeposited funds, employee advances, inventory, A/P, sales tax, payroll, reporting, sharing data with accountants, and much more. …”

“…Syed Natif Nawaz, CCIE No. 8825 (R&S, SP), has more than 10 years' experience in providing networking design, deployment, and escalation assistance to service provider customers. …”

“…Throughout, the author demonstrates techniques using many of today’s leading data analysis tools, including Hadoop, Hive, Shark, R, Apache Pig, Mahout, and Google BigQuery. Coverage includes Mastering the four guiding principles of Big Data success—and avoiding common pitfalls Emphasizing collaboration and avoiding problems with siloed data Hosting and sharing multi-terabyte datasets efficiently and economically “Building for infinity” to support rapid growth Developing a NoSQL Web app with Redis to collect crowd-sourced data Running distributed queries over massive datasets with Hadoop, Hive, and Shark Building a data dashboard with Google BigQuery Exploring large datasets with advanced visualization Implementing efficient pipelines for transforming immense amounts of data Automating complex processing with Apache Pig and the Cascading Java library Applying machine learning to classify, recommend, and predict incoming information Using R to perform statistical analysis on massive datasets Building highly efficient analytics workflows with Python and Pandas Establishing sensible purchasing strategies: when to build, buy, or outsource Previewing emerging tre..…”