Hadoop Fundamentals for Data Scientists [Online Code]

$119.99

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  • Learn Hadoop Fundamentals for Data Scientists from a professional trainer on your own time at your own desk.
  • This visual training method offers users increased retention and accelerated learning.
  • Breaks even the most complex applications down into simplistic steps.
Number of Videos: 6 hours – 33 lessons

Author: Jenny Kim,Benjamin Bengfort

User Level: Beginner

Get a practical introduction to Hadoop, the framework that made big data and large-scale analytics possible by combining distributed computing techniques with distributed storage. In this video tutorial, hosts Benjamin Bengfort and Jenny Kim discuss the core concepts behind distributed computing and big data, and then show you how to work with a Hadoop cluster and program analytical jobs. You’ll also learn how to use higher-level tools such as Hive and Spark. Hadoop is a cluster computing technology that has many moving parts, including distributed systems administration, data engineering and warehousing methodologies, software engineering for distributed computing, and large-scale analytics. With this video, you’ll learn how to operationalize analytics over large datasets and rapidly deploy analytical jobs with a variety of toolsets. Once you’ve completed this video, you’ll understand how different parts of Hadoop combine to form an entire data pipeline managed by teams of data engineers, data programmers, data researchers, and data business people.

– Understand the Hadoop architecture and set up a pseudo-distributed development environment

– Learn how to develop distributed computations with MapReduce and the Hadoop Distributed File System (HDFS)

– Work with Hadoop via the command-line interface

– Use the Hadoop Streaming utility to execute MapReduce jobs in Python

– Explore data warehousing, higher-order data flows, and other projects in the Hadoop ecosystem

– Learn how to use Hive to query and analyze relational data using Hadoop

– Use summarization, filtering, and aggregation to move Big Data towards last mile computation

– Understand how analytical workflows including iterative machine learning, feature analysis, and data modeling work in a Big Data context

Benjamin Bengfort is a data scientist and programmer in Washington DC who prefers technology to politics but sees the value of data in every domain. Alongside his work teaching, writing, and developing large-scale analytics with a focus on statistical machine learning, he is finishing his PhD at the University of Maryland where he studies machine learning and artificial intelligence. Jenny Kim, a software engineer in the San Francisco Bay Area, develops, teaches, and writes about big data analytics applications and specializes in large-scale, distributed computing infrastructures and machine-learning algorithms to support recommendations systems.

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