Introduction to Apache Spark for R Experienced Data Scientists and Analysts

Getting Started with Spark for Experienced Data Scientists Already Working with R

Course Code: TTSK7516

Skill Level: Introductory

Duration: 2 Days

Course Overview

Spark is a highly optimized Data Science environment running on Hadoop YARN, with support for Machine Learning through MLib and Mahout, SQL, DataFrames, and Streaming. In this course, Data Scientists dive into the details of practical data science on the Spark platform, including real-world interaction with other systems in modern Data Science environments.

Quick Start to Spark for R Experienced Data Scientists & Analysts is intended for existing Data Scientists already fluent in data science techniques in other languages such as SAS and already comfortable with R. This course will be presented in a "rolling lab" approach - a continuous workshop of real-world data exploration involving real-world problems. As such, problems and opportunities will be explored as data suggests and as questions arise. "Lecture" material will be provided only as is necessary to explain the background of the approach being used at the moment. Times and ordering of the material are highly flexible and should be used only as estimates. Student questions and requests will also significantly alter the direction of the workshop.

The objective of the course is to practically transition these data scientists to the R/Spark/Hadoop environment, becoming comfortable with the tools and machine learning libraries and conduct statistical and machine learning analyses they've already been performing in SAS or similar environments.

Course Objectives

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. The objective of the course is to practically transition these data scientists to the R/Spark/Hadoop environment, becoming comfortable with the tools and machine learning libraries and conduct statistical and machine learning analyses they've already been performing in SAS or similar environments.

Course Prerequisites

This course is intended for existing Data Scientists already fluent in data science techniques in other languages such as SAS and already comfortable with R.

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses or Follow On training options. Our team will be happy to help you with recommendations for next steps in your Learning Journey.

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

Getting Started - Overview

Our Data and our problem set
Accessing the cluster, the data, and the tools
The Continuous Workshop approach
"Let's build a model together"
Focus on analysis, exploration, data munging, algorithms
Tooling and fundamentals as necessary to get the job done

Spark Overview

Data Science: The State of the Art
Hadoop, Yarn, and Spark
Architectural Overview
MLib Overview
HDFS data - Accessing
Lab Focus
Working with HDFS data
Distributed vs. Local Run Modes
Spark vs. Other tools (when is Spark the right tool for the job?)
Spark vs. SAS
Spark Languages (Java, R, Python, and Scala)
Hello, Spark

Spark Overview

Spark Core
Spark SQL
Spark and Hive
Lab
MLib
Spark Streaming
Spark API

DataFrames

DataFrames and Resilient Distributed Datasets (RDDs)
Partitions
Adding variables to a DataFrame
DataFrame Types
DataFrame Operations
Dependent vs. Independent variables
Map/Reduce with DataFrames

Spark SQL

Spark SQL Overview
Data stores: HDFS, Cassandra, HBase, Hive, and S3
Table Definitions
Queries

Spark MLib

MLib overview
MLib Algorithms Overview
Classification Algorithms
Regression Algorithms
Lab Focus
Brief Comparison to SAS
Here's your split, how to tune regression
Decision Trees and forests
Lab Focus
Brief Comparison to SAS
Stepwise approach to Decision Trees
Working with Exit Criteria
Recommendation with ALS
Clustering Algorithms
Lab Focus
Key Clustering Algorithms
Choosing Clustering Algorithms
Working with key algorithms
Machine Learning Pipelines
Linear Algebra (SVD, PCA)
Statistics in MLib

Spark Streaming

Streaming overview

Streaming with Kafka

Kafka overview
Kafka and Spark Streaming

Data Flow with NiFi

Apache NiFi overview
NiFi data flows with Spark/R

Cluster Mode

Standalone Cluster
Masters and Workers

Spark - the Big Picture

Spark in Real-Time and near-Real-Time Decision Support Systems
Spark in the Enterprise
Best Practices

Course Materials

Student Materials: Each participant will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work

Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. Please inquire for details and options.

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