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Key Features



About The Program

  • Pattern matching
  • Scala collections
  • RDDs in Spark
  • Data aggregation with pair RDDs
  • MLlib and RDD persistence in Spark
  • Python implementation
  • Apache Spark and Big Data
  • RDDs and frameworks of Apache Spark
  • Data frames and PySpark SQL
  • Flume and Apache Kafka
  • NumPy, SciPy, and Matplotlib
  • Python web scrapping
  • Professionals who must definitely sign up for this course include:

    • Big Data Analysts
    • Data Scientists
    • Analysts
    • Researchers
    • IT Developers
    • ETL Developers
    • Data Engineers
    • Software Engineers who are keen to upgrade their skills in Big Data
    • BI and Reporting Professionals
    • Students and graduates who wish to get into the field of Spark and become a professional in this domain can also take up this course

    To take up this course, you need to have a good knowledge of Python or any other programming language. Besides, you must have a good understanding of SQL or any other database query language. Moreover, having experience working with UNIX or Linux systems can be beneficial for you.

    Programming Languages,Tools & Packages

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    Spark Course Modules

    Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

    Scala Course Content
    Module 01 - Introduction to Scala
    • 1.1 Introducing Scala
    • 1.2 Deployment of Scala for Big Data applications and Apache Spark analytics
    • 1.3 Scala REPL, lazy values, and control structures in Scala
    • 1.4 Directed Acyclic Graph (DAG)
    • 1.5 First Spark application using SBT/Eclipse
    • 1.6 Spark Web UI
    • 1.7 Spark in the Hadoop ecosystem.
    Module 02 - Pattern Matching
    • 2.1 The importance of Scala
    • 2.2 The concept of REPL (Read Evaluate Print Loop)
    • 2.3 Deep dive into Scala pattern matching
    • 2.4 Type interface, higher-order function, currying, traits, application space and Scala for data analysis
    Module 03 - Executing the Scala Code
    • 3.1 Learning about the Scala Interpreter
    • 3.2 Static object timer in Scala and testing string equality in Scala
    • 3.3 Implicit classes in Scala
    • 3.4 The concept of currying in Scala
    • 3.5 Various classes in Scala
    Module 04 - Classes Concept in Scala
    • 4.1 Learning about the Classes concept
    • 4.2 Understanding the constructor overloading
    • 4.3 Various abstract classes
    • 4.4 The hierarchy types in Scala
    • 4.5 The concept of object equality
    • 4.6 The val and var methods in Scala
    Module 05 - Case Classes and Pattern Matching
    • 5.1 Understanding sealed traits, wild, constructor, tuple, variable pattern, and constant pattern
    Module 06 - Concepts of Traits with Example
    • 6.1 Understanding traits in Scala
    • 6.2 The advantages of traits
    • 6.3 Linearization of traits
    • 6.4 The Java equivalent
    • 6.5 Avoiding of boilerplate code
    Module 07 - Scala–Java Interoperability
    • 7.1 Implementation of traits in Scala and Java
    • 7.2 Handling of multiple traits extending.
    Module 08 - Scala Collections
    • 8.1 Introduction to Scala collections
    • 8.2 Classification of collections
    • 8.3 The difference between iterator and iterable in Scala
    • 8.4 Example of list sequence in Scala
    Module 09 - Mutable Collections Vs. Immutable Collections
    • 9.1 The two types of collections in Scala
    • 9.2 Mutable and immutable collections
    • 9.3 Understanding lists and arrays in Scala
    • 9.4 The list buffer and array buffer
    • 9.6 Queue in Scala
    • 9.7 Double-ended queue Deque, Stacks, Sets, Maps, and Tuples in Scala
    Module 10 - Use Case Bobsrockets Package
    • 10.1 Introduction to Scala packages and imports
    • 10.2 The selective imports
    • 10.3 The Scala test classes
    • 10.4 Introduction to JUnit test class
    • 10.5 JUnit interface via JUnit 3 suite for Scala test
    • 10.6 Packaging of Scala applications in the directory structure
    • 10.7 Examples of Spark Split and Spark Scala
    Spark Course Content
    Module 11 - Introduction to Spark
    • 11.1 Introduction to Spark
    • 11.2 Spark overcomes the drawbacks of working on MapReduce
    • 11.3 Understanding in-memory MapReduce
    • 11.4 Interactive operations on MapReduce
    • 11.5 Spark stack, fine vs. coarse-grained update, Spark stack, Spark Hadoop YARN, HDFS Revision, and YARN Revision
    • 11.6 The overview of Spark and how it is better than Hadoop
    • 11.7 Deploying Spark without Hadoop
    • 11.8 Spark history server and Cloudera distribution
    Module 12 - Spark Basics
    • 12.1 Spark installation guide
    • 12.2 Spark configuration
    • 12.3 Memory management
    • 12.4 Executor memory vs. driver memory
    • 12.5 Working with Spark Shell
    • 12.6 The concept of resilient distributed datasets (RDD)
    • 12.7 Learning to do functional programming in Spark
    • 12.8 The architecture of Spark
    Module 13 - Working with RDDs in Spark
    • 13.1 Spark RDD
    • 13.2 Creating RDDs
    • 13.3 RDD partitioning
    • 13.4 Operations and transformation in RDD
    • 13.5 Deep dive into Spark RDDs
    • 13.6 The RDD general operations
    • 13.7 Read-only partitioned collection of records
    • 13.8 Using the concept of RDD for faster and efficient data processing
    • 13.9 RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions
    Module 14 - Aggregating Data with Pair RDDs
    • 14.1 Understanding the concept of key-value pair in RDDs
    • 14.2 Learning how Spark makes MapReduce operations faster
    • 14.3 Various operations of RDD
    • 14.4 MapReduce interactive operations
    • 14.5 Fine and coarse-grained update
    • 14.6 Spark stack
    Module 15 - Writing and Deploying Spark Applications
    • 15.1 Comparing the Spark applications with Spark Shell
    • 15.2 Creating a Spark application using Scala or Java
    • 15.3 Deploying a Spark application
    • 15.4 Scala built application
    • 15.5 Creation of the mutable list, set and set operations, list, tuple, and concatenating list
    • 15.6 Creating an application using SBT
    • 15.7 Deploying an application using Maven
    • 15.8 The web user interface of Spark application
    • 15.9 A real-world example of Spark
    • 15.10 Configuring of Spark
    Module 16 - Parallel Processing
    • 16.1 Learning about Spark parallel processing
    • 16.2 Deploying on a cluster
    • 16.3 Introduction to Spark partitions
    • 16.4 File-based partitioning of RDDs
    • 16.5 Understanding of HDFS and data locality
    • 16.6 Mastering the technique of parallel operations
    • 16.7 Comparing repartition and coalesce
    • 16.8 RDD actions
    Module 17 - Spark RDD Persistence
    • 17.1 The execution flow in Spark

    • 17.2 Understanding the RDD persistence overview
    • 17.3 Spark execution flow, and Spark terminology
    • 17.4 Distribution shared memory vs. RDD
    • 17.5 RDD limitations
    • 17.6 Spark shell arguments
    • 17.7 Distributed persistence
    • 17.8 RDD lineage
    • 17.9 Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey
    Module 18 - Spark MLlib
    • 18.1 Introduction to Machine Learning
    • 18.2 Types of Machine Learning
    • 18.3 Introduction to MLlib
    • 18.4 Various ML algorithms supported by MLlib
    • 18.5 Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques
    Hands-on Exercise:

    • 1. Building a Recommendation Engine
    Module 19 - Integrating Apache Flume and Apache Kafka
    • 19.1 Why Kafka and what is Kafka?
    • 19.2 Kafka architecture
    • 19.3 Kafka workflow
    • 19.4 Configuring Kafka cluster
    • 19.5 Operations
    • 19.6 Kafka monitoring tools
    • 19.7 Integrating Apache Flume and Apache Kafka
    • Hands-on Exercise:

    • 1. Configuring Single Node Single Broker Cluster
    • 2. Configuring Single Node Multi Broker Cluster
    • 3. Producing and consuming messages
    • 4. Integrating Apache Flume and Apache Kafka
    Module 20 - Spark Streaming
    • 20.1 Introduction to Spark Streaming
    • 20.2 Features of Spark Streaming
    • 20.3 Spark Streaming workflow
    • 20.4 Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
    • 20.5 Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
    • 20.6 Important windowed operators and stateful operators
    • Hands-on Exercise:

    • 1. Twitter Sentiment analysis
    • 2. Streaming using Netcat server
    • 3. Kafka–Spark streaming
    • 4. Spark–Flume streaming
    Module 21 - Improving Spark Performance
    • 21.1 Introduction to various variables in Spark like shared variables and broadcast variables
    • 21.2 Learning about accumulators
    • 21.3 The common performance issues
    • 21.4 Troubleshooting the performance problems
    Module 22 - Spark SQL and Data Frames
    • 22.1 Learning about Spark SQL
    • 22.2 The context of SQL in Spark for providing structured data processing
    • 22.3 JSON support in Spark SQL
    • 22.4 Working with XML data
    • 22.5 Parquet files
    • 22.6 Creating Hive context
    • 22.7 Writing data frame to Hive
    • 22.8 Reading JDBC files
    • 22.9 Understanding the data frames in Spark
    • 22.10 Creating Data Frames
    • 22.11 Manual inferring of schema
    • 22.12 Working with CSV files
    • 22.13 Reading JDBC tables
    • 22.14 Data frame to JDBC
    • 22.15 User-defined functions in Spark SQL
    • 22.16 Shared variables and accumulators
    • 22.17 Learning to query and transform data in data frames
    • 22.18 Data frame provides the benefit of both Spark RDD and Spark SQL
    • 22.19 Deploying Hive on Spark as the execution engine
    Module 23 - Scheduling/Partitioning
    • 23.1 Learning about the scheduling and partitioning in Spark
    • 23.2 Hash partition
    • 23.3 Range partition
    • 23.4 Scheduling within and around applications
    • 23.5 Static partitioning, dynamic sharing, and fair scheduling
    • 23.6 Map partition with index, the Zip, and GroupByKey
    • 23.7 Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions
    Spark and Scala Projects
    • Movie Recommendation
    • Deploy Apache Spark for a movie recommendation system. Through this project, you will be working with Spark MLlib, collaborative filtering, clustering, regression, and dimensionality reduction. By the completion of this project, you will be proficient in working with streaming data, sampling, testing, and statistics.
    • Twitter API Integration for Tweet Analysis
    • integrate Twitter API for analyzing tweets. You can use any of the scripting languages, like PHP, Ruby, or Python, for requesting the Twitter API and get the results in JSON format. You will have to perform aggregation, filtering, and parsing as per the requirement for the tweet analysis.
    • Data Exploration Using Spark SQL – Wikipedia Data
    • This project will allow you to work with Spark SQL and combine it with ETL applications, real-time analysis of data, performing batch analysis, deploying Machine Learning, creating visualizations, and processing of graphs.
    Introduction to the Basics of Python
    • Explaining Python and Highlighting Its Importance
    • Setting up Python Environment and Discussing Flow Control
    • Running Python Scripts and Exploring Python Editors and IDEs
    Sequence and File Operations
    • Defining Reserve Keywords and Command Line Arguments
    • Describing Flow Control and Sequencing
    • Indexing and Slicing
    • Learning the xrange() Function
    • Working Around Dictionaries and Sets
    • Working with Files
    Functions, Sorting, Errors and Exception, Regular Expressions, and Packages
    • Explaining Functions and Various Forms of Function Arguments
    • Learning Variable Scope, Function Parameters, and Lambda Functions
    • Sorting Using Python
    • Exception Handling
    • Package Installation
    • Regular Expressions
    Python: An OOP Implementation
    • Using Class, Objects, and Attributes
    • Developing Applications Based on OOP
    • Learning About Classes, Objects and How They Function Together
    • Explaining OOPs Concepts Including Inheritance, Encapsulation, and Polymorphism, Among Others
    Debugging and Databases
    • Debugging Python Scripts Using pdb and IDE
    • Classifying Errors and Developing Test Units
    • Implementing Databases Using SQLite
    • Performing CRUD Operations
    Introduction to Big Data and Apache Spark
    • What is Big Data?
    • 5 V’s of Big Data
    • Problems related to Big Data: Use Case
    • What tools available for handling Big Data?
    • What is Hadoop?
    • Why do we need Hadoop?
    • Key Characteristics of Hadoop
    • Important Hadoop ecosystem concepts
    • MapReduce and HDFS
    • Introduction to Apache Spark
    • What is Apache Spark?
    • Why do we need Apache Spark?
    • Who uses Spark in the industry?
    • Apache Spark architecture
    • Spark Vs. Hadoop
    • Various Big data applications using Apache Spark
    Python for Spark
    • Introduction to PySpark
    • Who uses PySpark?
    • Why Python for Spark?
    • Values, Types, Variables
    • Operands and Expressions
    • Conditional Statements
    • Loops
    • Numbers
    • Python files I/O Functions
    • Strings and associated operations
    • Sets and associated operations
    • Lists and associated operations
    • Tuples and associated operations
    • Dictionaries and associated operations


    • Demonstrating Loops and Conditional Statements
    • Tuple – related operations, properties, list, etc.
    • List – operations, related properties
    • Set – properties, associated operations
    • Dictionary – operations, related properties
    Python for Spark: Functional and Object-Oriented Model
    • Functions
    • Lambda Functions
    • Global Variables, its Scope, and Returning Values
    • Standard Libraries
    • Object-Oriented Concepts
    • Modules Used in Python
    • The Import Statements
    • Module Search Path
    • Package Installation Ways


    • Lambda – Features, Options, Syntax, Compared with the Functions
    • Functions – Syntax, Return Values, Arguments, and Keyword Arguments
    • Errors and Exceptions – Issue Types, Remediation
    • Packages and Modules – Import Options, Modules, sys Path
    Apache Spark Framework and RDDs
    • Spark Components & its Architecture
    • Spark Deployment Modes
    • Spark Web UI
    • Introduction to PySpark Shell
    • Submitting PySpark Job
    • Writing your first PySpark Job Using Jupyter Notebook
    • What is Spark RDDs?
    • Stopgaps in existing computing methodologies
    • How RDD solve the problem?
    • What are the ways to create RDD in PySpark?
    • RDD persistence and caching
    • General operations: Transformation, Actions, and Functions
    • Concept of Key-Value pair in RDDs
    • Other pair, two pair RDDs
    • RDD Lineage
    • RDD Persistence
    • WordCount Program Using RDD Concepts
    • RDD Partitioning & How it Helps Achieve Parallelization
    • Passing Functions to Spark


    • Building and Running Spark Application
    • Spark Application Web UI
    • Loading data in RDDs
    • Saving data through RDDs
    • RDD Transformations
    • RDD Actions and Functions
    • RDD Partitions
    • WordCount program using RDD’s in Python
    PySpark SQL and Data Frames
    • Need for Spark SQL
    • What is Spark SQL
    • Spark SQL Architecture
    • SQL Context in Spark SQL
    • User-Defined Functions
    • Data Frames
    • Interoperating with RDDs
    • Loading Data through Different Sources
    • Performance Tuning
    • Spark-Hive Integration


    • Spark SQL – Creating data frames
    • Loading and transforming data through different sources
    • Spark-Hive Integration
    Apache Kafka and Flume
    • Why Kafka
    • What is Kafka?
    • Kafka Workflow
    • Kafka Architecture
    • Kafka Cluster Configuring
    • Kafka Monitoring tools
    • Basic operations
    • What is Apache Flume?
    • Integrating Apache Flume and Apache Kafka


    • Single Broker Kafka Cluster
    • Multi-Broker Kafka Cluster
    • Topic Operations
    • Integrating Apache Flume and Apache Kafka
    PySpark Streaming
    • Introduction to Spark Streaming
    • Features of Spark Streaming
    • Spark Streaming Workflow
    • StreamingContext Initializing
    • Discretized Streams (DStreams)
    • Input DStreams, Receivers
    • Transformations on DStreams
    • DStreams Output Operations
    • Describe Windowed Operators and Why it is Useful
    • Stateful Operators
    • Vital Windowed Operators
    • Twitter Sentiment Analysis
    • Streaming using Netcat server
    • WordCount program using Kafka-Spark Streaming

    • Twitter Sentiment Analysis
    • Streaming using Netcat server
    • WordCount program using Kafka-Spark Streaming
    • Spark-flume Integration
    Introduction to PySpark Machine Learning
    • Introduction to Machine Learning- What, Why and Where?
    • Use Case
    • Types of Machine Learning Techniques
    • Why use Machine Learning for Spark?
    • Applications of Machine Learning (general)
    • Applications of Machine Learning with Spark
    • Introduction to MLlib
    • Features of MLlib and MLlib Tools
    • Various ML algorithms supported by MLlib
    • Supervised Learning Algorithms
    • Unsupervised Learning Algorithms
    • ML workflow utilities

    • K- Means Clustering
    • Linear Regression
    • Logistic Regression
    • Decision Tree
    • Random Forest
    Databricks Spark
    • Why Databricks ?
    • Databricks with Microsoft Azure
    • Spark Databricks Analytics in Azure
    • Provisioning Databricks workspace in Azure portal
    • Developing Spark ML application
    • Developing Spark Streaming applications (real time Twitter Data)
    • Optimizing Spark Performance
    Spark Using Java
    • run Apache Spark on Java.
    • executing a Spark example program in a Java environment

    Introduction to NoSQL and MongoDB

    RDBMS, types of relational databases, challenges of RDBMS, NoSQL database, its significance, how NoSQL suits Big Data needs, introduction to MongoDB and its advantages, MongoDB installation, JSON features, data types and examples

    MongoDB Installation

    Installing MongoDB, basic MongoDB commands and operations, MongoChef (MongoGUI) installation and MongoDB data types

    Hands-on Exercise: Install MongoDB and install MongoChef (MongoGUI)

    Importance of NoSQL

    The need for NoSQL, types of NoSQL databases, OLTP, OLAP, limitations of RDBMS, ACID properties, CAP Theorem, Base property, learning about JSON/BSON, database collection and documentation, MongoDB uses, MongoDB write concern—acknowledged, replica acknowledged, unacknowledged, journaled—and Fsync

    Hands-on Exercise:Write a JSON document

    CRUD Operations

    Understanding CRUD and its functionality, CRUD concepts, MongoDB query and syntax and read and write queries and query optimization

    Hands-on Exercise:Use insert query to create a data entry, use find query to read data, use update and replace queries to update and use delete query operations on a DB file

    Data Modeling and Schema Design

    Concepts of data modelling, difference between MongoDB and RDBMS modelling, model tree structure, operational strategies, monitoring and backup

    Hands-on Exercise: Write a data model tree structure for a family hierarchy

    Data Management and Administration

    In this module, you will learn MongoDB® Administration activities such as health check, backup, recovery, database sharding and profiling, data import/export, performance tuning, etc.

    Hands-on Exercise: Use shard key and hashed shard keys, perform backup and recovery of a dummy dataset, import data from a CSV file and export data to a CSV file

    Data Indexing and Aggregation

    Concepts of data aggregation and types and data indexing concepts, properties and variations

    Hands-on Exercise: Do aggregation using pipeline, sort, skip and limit and create index on data using single key and using multi-key

    MongoDB Security

    Understanding database security risks, MongoDB security concept and security approach and MongoDB integration with Java and Robomongo

    Hands-on Exercise: 

    MongoDB integration with Java and Robomongo

    Working with Unstructured Data

    Implementing techniques to work with variety of unstructured data like images, videos, log data and others and understanding GridFS MongoDB file system for storing data

    Hands-on Exercise:

    Work with variety of unstructured data like images, videos, log data and others

    What projects I will be working on this MongoDB training?


    Working with the MongoDB Java Driver

    Industry: General

    Problem Statement: How to create table for video insertion using Java

    Topics: In this project, you will work with MongoDB Java Driver and become proficient in creating a table for inserting video using Java programming. You will work with collections and documents and understand the read and write basics of MongoDB database and the Java virtual machine libraries.


    • Setting up MongoDB JDBC Driver
    • Connecting to the database
    • Java virtual machine libraries


    Earn your Spark certificate

    Our Spark program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.

    Differentiate yourself with a Spark Certificate

    The knowledge and Azure skills you've gained working on projects, simulations, case studies will set you ahead of the competition.

    Share your achievement

    Talk about it on Linkedin, Twitter, Facebook, boost your resume, or frame it - tell your friends and colleagues about it.

    Data Scientist

    DeepNeuron Testimonials


    • What is Apache Spark?

      Apache Spark is an open-source data processing framework that can easily perform processing tasks on very large data sets, and also distribute data processing tasks across multiple computers, either on its own or with other distributed computing tools.

    • What is Scala?

      Spark is a unified analytics engine that is used for processing Big Data. It is a cluster computing framework that developers use for processing tasks rapidly with extensive datasets. Scala is an object-oriented programming language in which Spark is written. With Scala, developers can dive into Spark’s source code to get access to the framework’s newest features.

    • Scala vs Spark

      Spark is a unified analytics engine that is used for processing Big Data. It is a cluster computing framework that developers use for processing tasks rapidly with extensive datasets. Scala is an object-oriented programming language in which Spark is written. With Scala, developers can dive into Spark’s source code to get access to the framework’s newest features.

    • Does Deepneuron offer job assistance?

      Deepneuron actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.

    • Is it possible to switch from self-paced training to instructor-led training?

      You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

    • Does the job assistance program guarantee me a Job?

      Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

    • *For which all courses will I get certificates from IBM?

      Following are the list of courses for which you will get IBM certificates:

      • R Programming for Data Science
      • Python for Data Science

    • How do I earn the Master’s certificate?

      Upon completion of the following minimum requirements, you will be eligible to receive the Data Scientist Master’s certificate that will testify to your skills as an expert in Data Science.


      Course completion certificate

      Data Science and Statistics Fundamentals Required 85% of Online Self-paced completion
      Data Science with R Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project
      Data Science with SAS Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project
      Data Science with Python Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in course-end assessment and successful evaluation in at least 1 project
      Machine Learning and Tableau Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and successful evaluation in at least 1 project
      Big Data Hadoop and Spark Developer Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation of at least 1 project
      Capstone Project Required Attendance of 1 Live Virtual Classroom and successful completion of the capstone project

    • How do I enroll for the Data Scientist course?

      You can enroll in this Data Science training on our website and make an online payment using any of the following options:

      • Visa Credit or Debit Card
      • MasterCard
      • American Express
      • Diner’s Club
      • PayPal

      Once payment is received you will automatically receive a payment receipt and access information via email.

    • If I need to cancel my enrollment, can I get a refund?

      Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, please read our Refund Policy.

    • I am not able to access the online Data Science courses. Who can help me?

      Yes, we do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support. portal.

    • Who are the instructors and how are they selected?

      All of our highly qualified Data Science trainers are industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

    • What is Global Teaching Assistance?

      Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

    • What is covered under the 24/7 Support promise?

      We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

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