Apache Spark 2.0 + Python DO Big Data Analytics & ML

MP4 | Video: AVC 1280×720 | Audio: AAC 48KHz 2ch | Duration: 7.5 Hours | Lec: 58 | 883 MB | Language: English | Sub: English [Auto-generated]

Project Based, Hands-on Practices, Spark SQL, Spark Streaming, Real life Full cycle Project
What you’ll learn
Acquire Knowledge of Apache Spark 2.0 fundamentals and architecture
Write Spark 2.0 scripts for Transformations, actions, Spark SQL and Spark Streaming
Execute Machine Learning / Data Science algorithms
Solve real world data problems with Apache Spark 2.0
Handle interviews for Apache Spark 2.0 confidently and get jobs

Requirements
Python programming
Have a laptop/desktop to setup Spark

Description
Welcome to our course. Looking to learn Apache Spark 2.0, practice end-to-end projects and take it to a job interview? You have come to the RIGHT course! This course teaches you Apache Spark 2.0 with Python, trains you in building Spark Analytics and machine learning programs and helps you practice hands-on with an end-to-end real life application project. Our goal is to help you and everyone learn, so we keep our prices low and affordable.

Apache Spark is the hottest Big Data skill today. More and more organizations are adapting Apache Spark for building their big data processing and analytics applications and the demand for Apache Spark professionals is sky rocketing. Learning Apache Spark is a great vehicle to good jobs, better quality of work and the best remuneration packages.

The goal of this project is provide hands-on training that applies directly to real world Big Data projects. It uses the learn-train-practice-apply methodology where you

Learn solid fundamentals of the domain
See demos, train and execute solid examples
Practice hands-on and validate it with solutions provided
Apply knowledge you acquired in an end-to-end real life project

Who this course is for?
Software Professionals
Big Data Architects
Data Engineers


http://nitroflare.com/view/BAE8A2EEE4BC1EC/Apache_Spark_2.0___Python__DO_Big_Data_Analytics_%26_ML.rar

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