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Quick Facts

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

Course Overview

The Big Data Analytics certification programme is offered by Intellipaat in collaboration with E&ICT, IIT, Guwahati, and strives to provide comprehensive training in Big Data Analytics concepts such as Spark, Python, Hadoop, Data Warehousing, MongoDB, and more.

The Big Data Analytics programme provides a complete experience to learners when it comes to understanding the concepts, mastering them, and applying them in real life. Moreover, the course also features top subject matter experts from IBM who will share their analytics expertise with you.

With the Big Data Analytics online course, you get access to over 231 hours of instructor-led training and 182 hours of self-paced video lectures. Dedicated career assistance is also offered to help you land your dream job in the analytics domain.

The Big Data Analytics online training also features over a whopping 300 hours of exercises and projects, all of which are designed to help you gain practical expertise and help you apply the analytical concepts learned in real-life scenarios.

The Highlights

Certification by E&ICT, IIT GUWAHATI
300 Hours of Project & Exercises
231 hours of Instructor-Led Training
One-on-one mentor support
Executive alumni status
Job assistance
182 Hours of Self-paced Videos

Programme Offerings

Access to IBM Watson
job assistance
24x7 support
1:1 mentor support
Executive alumni status
300 Hours of Project & Exercises
182 Hours of Self-paced Videos
231 Hours of Instructor-Led Training

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIT Guwahati (IITG)

Paying the Big Data Analytics course fee is compulsory for enrolment.

Certification in Big Data Analytics fee structure

Course	Fees in USD
Certification in Big Data Analytics	$2,983

Eligibility Criteria

You'll get a certificate from E&ICT Academy IIT, Guwahati, after completing all the Big Data Analytics programme requirements.

What you will learn

JavaKnowledge of PythonSQL knowledgeKnowledge of MongoDBKnowledge of KafkaKnowledge of ExcelKnowledge of Apache Spark

By the end of the Big Data Analytics syllabus, you will be proficient in analytical tools like:

MS Excel
Python
PySpark
SQL
Java
Linux
Apache Spark
Statistics for Data Analytics
Data Visualisation
Scala
Business intelligence
Real-time streaming

Who it is for

Product Development ManagerSoftware EngineerProject Manager

The Big Data Analytics certification course is ideal for:

Anyone having a bachelor’s degree in Big Data Analytics
Anyone having degrees in computer science, statistics, maths, or any similar field
Product managers/ Project managers looking to up-skill
Software engineers and analysts with a bachelor’s degree
Professionals looking to acquire skills in data analytics and data science

Admission Details

Step 1: Visit Intellipaat’s Big Data Analytics online course page.

Step 2: Click ‘Apply Now’ on the page’s top right.

Step 3: A small ‘Contact Us’ will pop up. Enter all the details prompted on the screen.

Step 4: After you have spoken to Intellipaat’s programme advisors, you’ll be required to fill and submit an application form.

Step 5: An admission panel will shortlist your application based on the application and interview.

Step 6: If you’re selected, you’ll be notified within 1-2 weeks.

Application Details

To begin the enrolment process for the Big Data Analytics online programme, you have to fill a ‘Contact Us’ application form. The form requires you to enter your full name, email ID, mobile number, and training needs.

The Syllabus

Python

Introduction to Python and IDEs
Python Basics
Object Oriented Programming
Hands-on Sessions And Assignments for Practice

Java programming for MapReduce
SQL fundamentals
Linux fundamentals

Data Analytics in Excel

Concepts of finance
Concepts of economics
Hands-on:
- Inferential statistics
- Descriptive statistics
- Simple and multivariate regression
- Confidence intervals

Data Analytics Using SQL

Introduction to MySQL
Working with MySQL and MySQL IDE: Installation and setup
Introduction to SQL queries: DDL queries (create and select) and DML queries (alter, insert, etc.)
Working with joins, group, and filter
Writing complex SQL queries for data retrieval and the import and export of data and database tables

Introduction to Python
Python basic constructs
OOPs in Python
NumPy for mathematical computing
SciPy for scientific computing
Data manipulation
Data visualization with Matplotlib
Implementing statistical algorithms using Python

Hadoop installation and setup
Introduction to Big Data and Hadoop
Understanding HDFS and MapReduce

Deep dive into MapReduce

Introduction to Hive
Advanced Hive and Impala
Introduction to Pig
Flume and Sqoop

Scala programming
Spark framework
RDD in Spark
DataFrames and Spark SQL
Machine Learning using Spark (MLlib)

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
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
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

Hands-On

Twitter Sentiment Analysis
Streaming using Netcat server
WordCount program using Kafka-Spark Streaming
Spark-flume Integration
Demonstrating Loops and Conditional Statements
Tuple – related operations, properties, list, etc.
List – operations, related properties
Set – properties, associated operations
Dictionary – operations, related properties
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

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

Hands-On

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

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

Hands-On

K- Means Clustering
Linear Regression
Logistic Regression
Decision Tree
Random Forest

Apache Flume and Apache Kafka
Spark Streaming
Case Study: Spark vs Kafka and when to use them

Distributions
Central Limit Theorem
hypothesis testing/ statistical significance
Advanced hypothesis testing

Power BI Basics

Introduction to PowerBI, Use cases and BI Tools, Data Warehousing, Power BI components, Power BI Desktop, workflows and reports, and Data Extraction with Power BI.
SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data, M Query, and Hierarchies in Power BI.