Data Science with SparkML Training & Certification Courses
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This training will help in understanding Apache Spark framework and its implementation for Big Data Analytics and Data Science. This training will give hands-on experience along with practical use cases approach, which will make participants experience in understanding how Spark is helpful to solve practical data science problems over distributed data and big data. With the training they will be able to learn how to load data, assemble and disassemble data objects, writing Spark jobs, Spark RDD and Dataframe operations, and use of Spark Processing Framework.


  • Social media sites like Facebook, twitter and LinkedIn for analyzing likes, tweets and profiles searched by people.
  • E-commerce sites like Flipkart, Amazon, and Alibaba for analyzing sales of products and search for products.
  • Sentimental analysis like customer satisfaction and feedbacks, moods.
  • Helps in business decisions based on visualization of data and trends.
  • Analyzing large objects like satellite pictures, images and graphs.
  • Live streaming analysis for providing real time results.
  • Adoption of storage where data grows very fast.
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Schedule & Prices

Delivery Mode Location Course Duration Fees  Schedule
Instructor-Led Online Training (1-on-1) Client's Home/Office3 Days $ 1,400 As per mutual convenience (4-Hours Evenings & Weekends Possible
Classroom Training * Dubai 3 Days $ 2,060 On Request
Delhi, Bangalore, Dehradun (Rishikesh), Goa, Shimla, Chennai 3 Days $ 1,400
3-5 Dec 2018 (1 Seat Left),
31-2 Jan 2019
Fly-Me-a-Trainer Client's Location3 Days On Request As per mutual convenience
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Course Content / Exam(s)

Schedule for Data Science with SparkML

Course Name Regular Track (days) Fast Track (days) Super Fast Track (days)
Data Science with SparkML333

Course Prerequisites

Basic understanding of big data concepts.

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Data Science with SparkML Benefits

This course is best suited for fresher and experienced IT professionals, software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming.


Apache Spark Basics

  • What is Apache Spark?
  • Using the Spark Shell
  • RDDs (Resilient Distributed Datasets)
  • Functional Programming in Spark

Working with RDDs

  • Creating RDDs
  • Other General RDD Operations

Aggregating Data with Pair RDDs

  • Key-Value Pair RDDs
  • Map-Reduce
  • Other Pair RDD Operations

Dataframe Basics

  • What is Dataframe
  • Dataframe vs RDD
  • Getting started with Dataframes
  • Dataframe operations
  • Creating Dataframe from various Data Sources – Avro, Parquet, RDBMS, Hive etc.
  • Save Dataframes
  • Dataframe execution (eager and Lazy)

Writing Queries on Dataframes

  • Query Dataframe with column names and expressions
  • Join Dataframes
  • Grouping and Aggregating dataframes

Machine Learning Overview

  • Introduction
  • Collaborative Filtering
  • Clustering
  • Classification
  • Relationship of Algorithms and Data Volume

Machine Learning with Spark MLlib

  • Introduction
  • Data Types
  • Basic Statistics
  • Feature Extraction
  • Dimensionality Reduction
  • Models
  • Regression Module Contents

Machine Learning with Spark ML

  • Overview of Spark ML
  • Use of DataFrames
  • Transformers and Estimators
  • Pipelines

Implement Machine Learning Algorithms

  • Decision Tree Classifiers
  • k-Means Clustering
  • Linear Regression
  • Logistic Regression
  • Naïve-Bayes
  • Recommender Engine using Spark

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