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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English	Self Study, Virtual Classroom	Video and Text Based	Weekends

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Belhaven University, Mississippi

The fees for course Advanced Certification Program in Big Data is -

Head	Amount
Total Admission Fee	₹ 75,012 (Inclusive of All)
EMI Starts at	₹ 8,000

The Syllabus

Module 01 – Hadoop Installation and Setup
Module 02 – Introduction to Big Data Hadoop and Understanding HDFS and MapReduce
Module 03 – Deep Dive in MapReduce
Module 04 – Introduction to Hive
Module 05 – Advanced Hive and Impala
Module 06 – Introduction to Pig
Module 07 – Flume, Sqoop and HBase
Module 08 – Writing Spark Applications Using Scala
Module 09 – Use Case Bobsrockets Package
Module 10 – Introduction to Spark
Module 11 – Spark Basics
Module 12 – Working with RDDs in Spark
Module 13 – Aggregating Data with Pair RDDs
Module 14 – Writing and Deploying Spark Applications
Module 15 – Project Solution Discussion and Cloudera Certification Tips and Tricks
Module 16 – Parallel Processing
Module 17 – Spark RDD Persistence
Module 18 – Spark MLlib
Module 19 – Integrating Apache Flume and Apache Kafka
Module 20 – Spark Streaming
Module 21 – Improving Spark Performance
Module 22 – Spark SQL and Data Frames
Module 23 – Scheduling/Partitioning

Following topics will be available only in self-paced mode:

Module 24 – ETL Connectivity with Hadoop Ecosystem (Self-Paced)
Module 25 – Hadoop Application Testing
Module 26 – Roles and Responsibilities of Hadoop Testing Professional
Module 27 – Framework Called MRUnit for Testing of MapReduce Programs
Module 28 – Unit Testing
Module 29 – Test Execution
Module 30 – Test Plan Strategy and Writing Test Cases for Testing Hadoop Application

Scala Course Content

Module 01 – Introduction to Scala
Module 02 – Pattern Matching
Module 03 – Executing the Scala Code
Module 04 – Classes Concept in Scala
Module 05 – Case Classes and Pattern Matching
Module 06 – Concepts of Traits with Example
Module 07 – Scala–Java Interoperability
Module 08 – Scala Collections
Module 09 – Mutable Collections Vs. Immutable Collections
Module 10 – Use Case Bobsrockets Package

Spark Course Content

Module 11 – Introduction to Spark
Module 12 – Spark Basics
Module 13 – Working with RDDs in Spark
Module 14 – Aggregating Data with Pair RDDs
Module 15 – Writing and Deploying Spark Applications
Module 16 – Parallel Processing
Module 17 – Spark RDD Persistence
Module 18 – Spark MLlib
Module 19 – Integrating Apache Flume and Apache Kafka
Module 20 – Spark Streaming
Module 21 – Improving Spark Performance
Module 22 – Spark SQL and Data Frames
Module 23 – Scheduling/Partitioning

Module 1 – Splunk Development Concepts
Module 2 – Basic Searching
Module 3 – Using Fields in Searches
Module 4 – Saving and Scheduling Searches
Module 5 – Creating Alerts
Module 6 – Scheduled Reports
Module 7 – Tags and Event Types
Module 8 – Creating and Using Macros
Module 9 – Workflow
Module 10 – Splunk Search Commands
Module 11 – Transforming Commands
Module 12 – Reporting Commands
Module 13 – Mapping and Single Value Commands
Module 14 – Splunk Reports and Visualizations
Module 15 – Analyzing, Calculating and Formatting Results
Module 16 – Correlating Events
Module 17 – Enriching Data with Lookups
Module 18 – Creating Reports and Dashboards
Module 19 – Getting Started with Parsing
Module 20 – Using Pivot
Module 21 – Common Information Model (CIM) Add-On

Splunk Administration Topics

Module 22 – Overview of Splunk
Module 23 – Splunk Installation
Module 24 – Splunk Installation in Linux
Module 25 – Distributed Management Console
Module 26 – Introduction to Splunk App
Module 27 – Splunk Indexes and Users
Module 28 – Splunk Configuration Files
Module 29 – Splunk Deployment Management
Module 30 – Splunk Indexes
Module 31 – User Roles and Authentication
Module 32 – Splunk Administration Environment
Module 33 – Basic Production Environment
Module 34 – Splunk Search Engine
Module 35 – Various Splunk Input Methods
Module 36 – Splunk User and Index Management
Module 37 – Machine Data Parsing
Module 38 – Search Scaling and Monitoring
Module 39 – Splunk Cluster Implementation

Module 01 – Introduction to Data Science using Python
Module 02 – Python basic constructs
Module 03 – Maths for DS-Statistics & Probability
Module 04 – OOPs in Python (Self paced)
Module 05 – NumPy for mathematical computing
Module 06 – SciPy for scientific computing
Module 07 – Data manipulation
Module 08 – Data visualization with Matplotlib
Module 09 – Machine Learning using Python
Module 10 – Supervised learning
Module 11 – Unsupervised Learning
Module 12 – Python integration with Spark (Self paced)
Module 13 – Dimensionality Reduction
Module 14 – Time Series Forecasting

Module 01 – Introduction to the Basics of Python
Module 02 – Sequence and File Operations
Module 03 – Functions, Sorting, Errors and Exception, Regular Expressions, and Packages
Module 04 – Python: An OOP Implementation
Module 05 – Debugging and Databases
Module 06 – Introduction to Big Data and Apache Spark
Module 07 – Python for Spark
Module 08 – Python for Spark: Functional and Object-Oriented Model
Module 09 – Apache Spark Framework and RDDs
Module 10 – PySpark SQL and Data Frames
Module 11 – Apache Kafka and Flume
Module 12 – PySpark Streaming
Module 13 – Introduction to PySpark Machine Learning

Module 01 – Introduction to NoSQL and MongoDB
Module 02 – MongoDB Installation
Module 03 – Importance of NoSQL
Module 04 – CRUD Operations
Module 05 – Data Modeling and Schema Design
Module 06 – Data Management and Administration
Module 07 – Data Indexing and Aggregation
Module 08 – MongoDB Security
Module 09 – Working with Unstructured Data

Module 01 – Introduction to Big Data and Data Collection
Module 02 – Introduction to Cloud Computing & AWS
Module 03 – Elastic Compute and Storage Volumes
Module 04 – Virtual Private Cloud
Module 05 – Storage – Simple Storage Service (S3)
Module 06 – Databases and In-Memory DataStores
Module 07 – Data Storage
Module 08 – Data Processing
Module 09 – Data Analysis
Module 09 – Data Visualization and Data Security

Module 01 – Introduction to Hadoop and Its Ecosystem, MapReduce and HDFS
Module 02 – MapReduce
Module 03 – Introduction to Pig and Its Features
Module 04 – Introduction to Hive
Module 05 – Hadoop Stack Integration Testing
Module 06 – Roles and Responsibilities of Hadoop Testing
Module 07 – Framework Called MRUnit for Testing of MapReduce Programs
Module 08 – Unit Testing
Module 09 – Test Execution of Hadoop: Customized
Module 10 – Test Plan Strategy Test Cases of Hadoop Testing

Module 01 – Understanding the Architecture of Storm
Module 02 – Installation of Apache Storm
Module 03 – Introduction to Apache Storm
Module 04 – Apache Kafka Installation
Module 05 – Apache Storm Advanced
Module 06 – Storm Topology
Module 07 – Overview of Trident
Module 08 – Storm Components and Classes
Module 09 – Cassandra Introduction
Module 10 – Boot Stripping

Module 01 – What is Kafka – An Introduction
Module 02 – Multi Broker Kafka Implementation
Module 03 – Multi Node Cluster Setup
Module 04 – Integrate Flume with Kafka
Module 05 – Kafka API
Module 06 – Producers & Consumers

Module 01 – Advantages and Usage of Cassandra
Module 02 – CAP Theorem and No SQL DataBase
Module 03 – Cassandra fundamentals, Data model, Installation and setup
Module 04 – Cassandra Configuration
Module 05 – Summarization, node tool commands, cluster, Indexes, Cassandra & MapReduce, Installing Ops-center
Module 06 – Multi Cluster setup
Module 07 – Thrift/Avro/Json/Hector Client
Module 08 – Datastax installation part,· Secondary index
Module 09 – Advance Modelling
Module 10 – Deploying the IDE for Cassandra applications
Module 11 – Cassandra Administration
Module 12 – Cassandra API and Summarization and Thrift

Module 01 – Core Java Concepts
Module 02 – Writing Java Programs using Java Principles
Module 03 – Language Conceptuals
Module 04 – Operating with Java Statements
Module 05 – Concept of Objects and Classes
Module 06 – Introduction to Core Classes
Module 07 – Inheritance in Java
Module 08 – Exception Handling in Detail
Module 09 – Getting started with Interfaces and Abstract Classes
Module 10 – Overview of Nested Classes
Module 11 – Getting started with Java Threads
Module 12 – Overview of Java Collections
Module 13 – Understanding JDBC
Module 14 – Java Generics
Module 15 – Input/Output in Java
Module 16 – Getting started with Java Annotations
Module 17 – Reflection and its Usage

Introduction to Linux – Establishing the fundamental knowledge of how Linux works and how you can begin with Linux OS.
Linux Basics – File Handling, data extraction, etc.
Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux.

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