Spark and Scala Training in Nagpur 
Spark and Scala Training in Nagpur our Certification Training are designed to provide you with the knowledge and skills that are required to become a successful Spark Developer and prepare you for the Spark Developer Certification

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Spark and Scala Training in Nagpur Course overview

Spark and Scala Training in Nagpur, the spark & Scala is a lightning-fast cluster computing designed for fast computation framework. Which is developed as an open source? It was built on top of Hadoop, MapReduce and it extends the MapReduce model to efficiently use more types of computations which includes Interactive Queries and Stream Processing. in certain aspects. Spark is considered and interested in learning by the aspirants, who want to learn and work on machine learning algorithms.
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Prerequisites for Spark and Scala Training in Nagpur

Knowledge of Hadoop, Functional Programming and Object Oriented Programming is surely an advantage for undertaking this course
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Training from professional certified trainers

Appzmine specialists have been executing professional Spark and Scala Training in Nagpur and provide solutions over a scope of organizations for a long time. Those advisors compose and instruct our training courses, so their experience straightforwardly educates course content.
Spark and Scala Training in Nagpur Course Content
Introduction to Scala Programming: Level One
  • Arithmetic and Numbers
  • Values and Variables
  • Booleans and Comparison Operators
  • Strings and Basic Regex
  • Tuples
  • Scala Basics – Assessment Test Exercises
Collections
  • Intro to Collections
  • Lists
  • Arrays
  • Sets
  • Maps
  • Collections – Assessment Test Exercise

 

Scala Programming: Level Two
  • Flow Control
  • For Loops
  • While Loops
  • Functions
  • Scala Programming Exercises
Spark programming basics
  • The Resilient Distributed Dataset
  • Ratings Histogram Walkthrough
  • Spark Internals
  • Key / Value RDD’s, and the Average Friends by Age example
  • [POC] Running the Average Friends by Age Example
  • Filtering RDD’s, and the Minimum Temperature by Location Example
  • [POC] Running the Minimum Temperature Example, and Modifying it for Maximum
  • [POC] Counting Word Occurrences using Flatmap()
  • [POC] Improving the Word Count Script with Regular Expressions
  • [POC] Sorting the Word Count Results
  • [Exercise] Find the Total Amount Spent by Customer and Sort Them by Total Amount Spent
Advanced Examples of Spark Programs
  • [POC] Find the Most Popular Movie
  • [POC] Use Broadcast Variables to Display Movie Names
  • [POC] Find the Most Popular Superhero in a Social Graph
  • Superhero Degrees of Separation: Introducing Breadth-First Search
  • Superhero Degrees of Separation: Accumulators, and Implementing BFS in Spark
  • Superhero Degrees of Separation: Review the code, and run it!
  • Item-Based Collaborative Filtering in Spark, cache(), and persist()
  • [POC] Running the Similar Movies Script using Spark’s Cluster Manager
  • [Exercise] Improve the Quality of Similar Movies
Running Spark on a Cluster
  • [POC] Using spark-submit to run Spark driver scripts
  • [POC] Packaging driver scripts with SBT
  • Introducing Amazon Elastic MapReduce
  • Creating Similar Movies from One Million Ratings on EMR
  • Partitioning
  • Best Practices for Running on a Cluster
  • Troubleshooting, and Managing Dependencies
Spark Dataframes and Datasets
  • Introduction to Spark Data Frames
  • Data Frames Overview
  • Spark Data Frame Operations
  • Group By and Aggregate Functions
  • Missing data
  • Date and Timestamps
  • Data Frame Project Exercises
  • [POC] Using Datasets instead of RDD’s
Regression with Spark
  • Introduction to Linear Regression
  • Introduction to Regression Section
  • Linear Regression Documentation Example
  • Alternate Linear Regression Data CSV File
  • Linear Regression Walkthrough Part 1
  • Linear Regression Walkthrough Part 2
  • Linear Regression Exercise Project
Classification with Spark
  • Introduction to Classification
  • Classification Documentation Example
  • Spark Classification – Logistic Regression Example – Part 1
  • Spark Classification – Logistic Regression Example – Part 2
  • Logistic Regression Project Exercise

 

Model Evaluation
  • Model Evaluation Overview
  • Spark Model Evaluation – Documentation Example
  • Spark – Model Evaluation – Regression Example

 

Clustering with Spark
  • Introduction to Clustering with Spark
  • KMeans Theory Lecture
  • Example of KMeans with Spark
  • Clustering Project Exercise Overview
PCA with Spark
  • PCA Theory Overview
  • PCA with Spark – Project Exercise
  • PCA with Spark – Documentation Example
Data Bricks and Spark
  • Data bricks Overview
  • Introduction to Spark Recommendation Systems
  • Spark Recommender System Implementation
  • Zeppelin Notebooks on AWS Elastic MapReduce.
Intro to Spark Streaming
  • Spark Streaming Overview
  • [POC] Set up a Twitter Developer Account, and Stream Tweets
  • Structured Streaming

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  • Training from professional Spark and Scala developers
  • 10 years of experience
  • Training and Internship combined
  • Real-Time Development experience
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