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Electronics & ICT Academy IIT Guwahati

Advanced Certification in Big Data Analytics

This certification program is in collaboration with E&ICT, IIT, Guwahati, and aims to provide extensive training on Big Data Analytics concepts such as Hadoop, Spark, Python, MongoDB, Data Warehousing, and more. This program warrants providing a complete experience to learners in terms of understanding the concepts, mastering them thoroughly, and applying them in real life.

In collaboration with

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Learning Format


Live Classes

9 Months

Career Services

by Intellipaat

3 Guaranteed



Hiring Partners

About Program

This certification program in Big Data Analytics will provide you academic rigor along with Industry exposure. The course is designed and created under the mentorship of the top faculties of IIT Guwahati.

Key Highlights

231 Hrs Instructor Led Training
182 Hrs Self-paced Videos
300 Hrs Project & Exercises
Certification by E&ICT, IIT GUWAHATI
Job Assistance
24*7 Support
1:1 Mentor Support
2 Days campus immersion at IIT Guwahati

Free Career Counselling

We are happy to help you 24/7

Partnering with E&ICT, IIT Guwahati

This Certification Program in Big Data Analytics is in partnership with E&ICT Academy IIT Guwahati. E&ICT IIT Guwahati is an initiative of Meity (Ministry of Electronics and Information Technology, Govt. of India) and formed with the team of IIT Guwahati professors to provide high-quality education programs.

Upon completion of this program, you will:

  • Receive a certificate from E&ICT, IIT Guwahati

Program in Collaboration with IBM

IBM is one of the leading innovators and the biggest player in creating innovative tools for big data analytical tools. Top subject matter experts from IBM will share knowledge in the domain of analytics and big data through this training program.

Benefits for students from IBM:

  • Industry-recognized IBM certificates
  • Access to IBM Watson for hands-on training and practice
  • Industry in-line case studies and project work

Who Can Apply for the Course?

  • Anyone with a bachelor’s degree and passion for Big Data Analytics
  • Professionals looking to grow their career in Data Analytics, Data Science
  • Analysts & Software Engineers with a bachelor’s degree who wants to enter this domain
  • Project Managers / Product Managers looking to up-skill
  • Anyone with degrees in fields like Maths, Computer Science, Statistics, or similar
Who can aaply

What roles can a Big Data Analyst play?

Big Data Specialist

Builds and manages a personalized pluggable service-based framework to allow import, cleansing, transformation, and validation of data.

Data Engineer

Transforms raw data into meaningful insights and presents data into a meaningful form for business users.

Data Scientist

Identifies problems, understands data sets, collects & cleans large data sets, creates data models, and performs data mining.

Big Data Analyst

Develops data pipelines and designs necessary solutions to resolve complex issues.

Big Data Engineer

Designs, creates, and tests scalable and robust elements of data platforms and provides solutions for various problems.

Business Analyst

Extracts required data for tasks like business analysis, and builds reports, metrics, and dashboards for performance monitoring.

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Skills to Master

Big Data




Data Science

Machine Learning




Real-time Streaming


Data Mining

Business Intelligence

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Tools to Master

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Meet Your Mentors

Program Curriculum

Live Course Self Paced
  • Python & Linux Foundation
  • Python Environment Setup and Essentials
  • Python Language Basic Constructs
  • Introduction to Linux and File Management
  • Java programming for MapReduce
  • SQL fundamentals
  • Linux fundamentals

Tools covered

Tools Covered Tools Covered Tools Covered
  • Introduction to NoSQL Databases
  • Introduction to NoSQL and MongoDB
  • MongoDB installation
  • Importance of NoSQL
  • CRUD operations
  • Data modeling and schema design
  • Data management and administration
  • Data indexing and aggregation
  • MongoDB security
  • Working with unstructured data

Tools covered

Tools Covered
  • Distributions
  • Central Limit Theorem
  • hypothesis testing/ statistical significance
  • Advanced hypothesis testing
  • Data Analytics in Excel
    • Concepts of finance
    • Concepts of economics
    • Hands-on: Inferential statistics, descriptive statistics, simple and multivariate regression, and 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

Tools covered

Tools Covered Tools Covered
  • 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

Tools covered

Tools Covered
  • 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

Tools covered

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

Tools covered

Tools Covered Tools Covered
  • 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


  • 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

Tools covered

Tools Covered Tools Covered Tools Covered Tools Covered
  • Why model tuning?
  • What is model tuning?
  • What are parameters
  • What are Hyper-parameters
  • What is Hyper-parameter tuning?
  • Types of Hyper parameter tuning:
  • Grid Search
  • Random Search


  • Performing Grid Search Hyperparameter Tuning to Increase model accuracy
  • Performing Random Search Hyperparameter Tuning to Increase model accuracy
  • Why Ensemble Learning?
  • What is Ensemble Learning?
  • Model Error
  • Bias
  • Variance
  • Reducing Model Error
  • Different Types of Ensemble Learning
  • Bagging
  • Boosting
  • Stacking


  • Creating a Bagging classifier to reduce model error using sklearn
  • Creating a Boosting classifier to reduce model error using sklearn
  • Creating a Stacking classifier to reduce model error using sklearn
  • What is Model Deployment
  • Model Deployment Strategy
  • Steps in Model Deployment
  • Create a model
  • Save it
  • Load in in a web server/ web api
  • Make Predictions


  • Creating, Saving and Deploying a model using a python falsk web api
  • 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

Tools covered

Tools Covered Tools Covered
  • 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

Tools covered

Tools Covered Tools Covered Tools Covered
  • 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

Tools covered

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

Tools covered

Tools Covered Tools Covered Tools Covered
  • Introduction to data visualization and the power of Tableau
  • Architecture of Tableau
  • Working with metadata and data blending
  • Creation of sets
  • Working with filters
  • Organizing data and visual analytics
  • Working with mapping
  • Working with calculations and expressions
  • Working with parameters
  • Charts and graphs
  • Dashboards and stories
  • Tableau Prep
  • Integration of Tableau with Big Data tools like Hadoop and Spark

Tools covered

Tools Covered
  • Marketing, Web, and Social Media Analytics
  • Fraud and Risk Analytics
  • Supply Chain and Logistics Analytics
  • HR Analytics
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Program Highlights

231 Hours of live training
182 Hours of Self-paced video
300 Hours of Guided projects
24/7 Lifetime support

Interested in This Program? Secure your spot now.

The application is free and takes only 5 minutes to complete.

Project Work

The projects will be a part of your certification in Big Data Analytics to consolidate your learning. Industry-based projects will ensure that you gain real-world experience before starting your career in Big Data.

Practice 100+ Essential Tools

Designed by Industry Experts

Get Real-world Experience

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends – the possibilities are endless and our community has something for everyone!


Career Services By Intellipaat

Career Services

Career Oriented Sessions

Throughout the course

Over 20+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up skilling objective.


Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & Linkedin Profile from our career services team and learn how to grab the attention of the hiring manager at profile shortlisting stage


Mock Interview Preparation

After 80% of the course completion.

Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.


1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, past experience, and future career aspirations.


3 Guaranteed Interviews

After 80% of the course completion

Guaranteed 3 job interviews upon submission of projects and assignments. Get interviewed by our 400+ hiring partners.


Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Our Alumni Work At

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Admission Details

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

Submit Application

Submit Application

Tell us a bit about yourself and why you want to join this program

Application Review

Application Review

An admission panel will shortlist candidates based on their application


Application Review

Selected candidates will be notified within 1–2 weeks

Program Fee

Total Admission Fee

$ 2,983

Program Cohorts

Next Cohorts

Date Time Batch Type
Program Induction 5th Dec 2021 08:00 PM IST Weekend (Sat-Sun)
Regular Classes 5th Dec 2021 08:00 PM IST Weekend (Sat-Sun)

Frequently Asked Questions

Is this program conducted online or offline?

This program is conducted online for 9 months with the help of multiple live instructor-led training sessions.

After you share your basic details with us, our course advisor will speak to you and based on the discussion, your application will be screened. If your application is shortlisted, you will need to fill in a detailed application form and attend a telephonic interview, which will be conducted by a subject matter expert. Based on your profile and interview, if you are selected, you will receive an admission offer letter.

To complete this program, it requires 9 months of attending live classes and completing the assignments and projects, along the way.

If by any circumstance you miss a live class, you will be given the recording of the class within the next 12 hours. Also, if you need any support, you will have access to our 24/7 technical support team for any sort of query resolution.

To complete this program, you will have to spare around 6 hours a week in learning. Classes will be held over weekends (Sat/Sun), and each session will be of 3 hours.

To ensure that you make the most of this program, you will be given industry-grade projects to work on. This is done to make sure that you get a concrete understanding of what you’ve learned.

Upon the completion of this program, you will be first preparing for job interviews through mock interview sessions, and then you will get assistance in preparing a resume that fulfils industry standards. This will be followed by a minimum of 3 exclusive interviews with 400+ hiring partners across the globe.

Upon the completion of all of the requirements of the program, you will be awarded a certificate from E&ICT Academy IIT, Guwahati.

There will be a 2-day campus immersion module at E&ICT Academy, IIT-Guwahati during which learners will visit the campus. You will learn from the faculty as well as interact with your peers. However, this is subject to COVID-19 situation and guidelines provided by the Institute. The cost of travel and accommodation will be borne by the learners. However, the campus immersion module is optional.

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What is included in this course?

  • Upskill without leaving your Job
  • Lear from top IIT Guwahati Faculty & Industry Experts
  • Rigorous curriculum designed to gain Industry relevant skills
  • Group Learning with your peers through Intellipaat PeerChat
  • Dedicated Career Services to land you in dream Job!

I’m Interested in This Program

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