6 + 2 months

Live Training


Real World Projects

240 Days

Cloud Lab Access



3 Days

Campus Immersion

E&ICT, IIT Roorkee


Course Overview

Home All Courses Post Graduate certification in Applied Data Science and AI, E&ICT IIT Roorkee Jamshedpur

Post Graduate certification in Applied Data Science and AI, E&ICT IIT Roorkee

The PG Certificate Course in Data Science, AI/ML & Data Engineering in Jamshedpur is an intensive online instructor led course. You will master Machine Learning, Artificial Intelligence, ChatGPT Architecture, Data Engineering, Python, Spark, Hadoop, Classification, Regression, SVM, ANN, Tableau and more.

This Data Science course in Jamshedpur will empower you to solve complex problems and make impactful data-informed decisions. On completing the course successfully, you will receive a certificate from IIT Roorkee that can propel your career.

(4.82K) 37K+ Learners
34 Projects 240 Days Cloud Lab Access
Estimated 11.5M new Data Science jobs(US)
Avg. Salary of over $84000 in Data Science roles
High demands in Tech, Finance, E-Commerce, Healthcare
Highly transferable mainstream skills

Program Highlights

Key Highlights

6 + 2 Months of Blended Training
240 Days of Lab access
34+ Projects
24*7 Support
Placement assistance
Lifetime Access to Course Material
Doubt clearing all weekdays
Scholarships available
3 Days Campus Immersion at IIT Roorkee
Certificate from E&ICT Academy-IIT Roorkee
15+ Languages and Tools covered
Hands-on experience in cloud labs

Book Counselling Session


Application Deadline 25 June 2024


What is the certificate like?

  • About E&ICT Academy, IIT Roorkee

    E&ICT-IITR provides certification courses with emphasis on hands-on learning in basic/advanced topics and emerging technologies in Electronics and ICT. It is sponsored by the Ministry of Electronics and Information Technology, Govt. of India.

  • About Cloudxlab

    Cloudxlab is a team of developers, researchers, and educators who build innovative products and create enriching learning experiences for users. Cloudxlab upskills engineers in deep tech to make them employable & future-ready.

Campus Immersion Program

1:1 with Professors and Industry Experts
Certificate award ceremony
Network with your peers
Showcase your course project to Professors and peers
Once in a lifetime experience


Among the IITs in the ‘Citations per Faculty’ parameter

*QS World Rankings

India Today


Ranked Engineering College

*India Today 2020



Ranked for IITs

*NIRF 2020



Ranked Best Global Universities in India

*QS World Rankings

Hands-on Learning

hands-on lab
  • Gamified Learning Platform
    Making learning fun and sustainable

  • Auto-assessment Tests
    Learn by writing code and executing it on lab

  • No Installation Required
    Lab comes pre-installed softwares and accessible everywhere

  • Accessibility
    Access the lab anywhere, anytime with an internet connection

Mentors / Faculty

Instructor Tharun Reddy

Tharun Kumar Reddy Bollu

Faculty ECE Dept

IIT Roorkee

Instructor Sanjeev Manhas

Sanjeev Manhas

Faculty ECE Dept

IIT Roorkee

Instructor Sandeep Giri

Sandeep Giri

Founder at CloudxLab

Past: Amazon, InMobi, D.E.Shaw

Instructor Abhinav Singh

Abhinav Singh

Co-Founder at CloudxLab

Past: Byjus

Dr. M.L. Virdi

Dr. M.L. Virdi

Senior Research Scientist


Instructor Praveen

Praveen Pavithran

Co-Founder at Yatis

Past: YourCabs, Cypress Semiconductor

Instructor Jatin Shah

Jatin Shah

Ex-LinkedIn, Yahoo, Yale CS Ph.D.


Instructor Shubh Tripathi

Shubh Tripathi

ML Engineer at CloudxLab


Foundation Courses

1. Programming Tools and Foundational Concepts
1. Linux for Data Science
2. Getting Started with Git
3. Python Foundations
4. Machine Learning Prerequisites(Including Numpy, Pandas and Linear Algebra)
5. Getting Started with SQL
6. Statistics Foundations

Course on Machine Learning

1. Machine Learning Applications & Landscape
1. Introduction to Machine Learning
2. Machine Learning Application
3. Introduction to AI
4. Different types of Machine Learning - Supervised, Unsupervised
2. Building end-to-end Machine Learning Project
1. Machine Learning Projects Checklist
2. Get the data
3. Launch, monitor, and maintain the system
4. Explore the data to gain insights
5. Prepare the data for Machine Learning algorithms
6. Explore many different models and short-list the best ones
7. Fine-tune model
3. Training Models
1. Linear Regression
2. Gradient Descent
3. Polynomial Regression
4. Learning Curves
5. Regularized Linear Models
6. Logistic Regression
4. Classification
1. Training a Binary classification
2. Multiclass,Multilabel and Multioutput Classification
3. Performance Measures
4. Confusion Matrix
5. Precision and Recall
6. Precision/Recall Tradeoff
7. The ROC Curve
5. Support Vector Machines
1. Linear SVM Classification
2. Nonlinear SVM Classification
3. SVM Regression
6. Decision Trees
1. Training and Visualizing a Decision Tree
2. Making Predictions
3. Estimating Class Probabilities
4. The CART Training Algorithm
5. Gini Impurity or Entropy
6. Regularization Hyperparameters
7. Instability
7. Ensemble Learning and Random Forests
1. Voting Classifiers
2. Bagging and Pasting
3. Random Patches and Random Subspaces
4. Random Forests
5. Boosting and Stacking
8. Dimensionality Reduction
1. The Curse of Dimensionality
2. Main Approaches for Dimensionality Reduction
3. PCA
4. Kernel PCA
5. LLE
6. Other Dimensionality Reduction Techniques

Course on Deep Learning and Reinforcement Learning

1. Introduction to Artificial Neural Networks
1. From Biological to Artificial Neurons
2. Implementing MLPs using Keras with TensorFlow Backend
3. Fine-Tuning Neural Network Hyperparameters
2. Convolutional Neural Networks and Computer Vision
1. The Architecture of the Visual Cortex
2. Convolutional Layer
3. Pooling Layer
4. CNN Architectures
5. Classification with Keras
6. Transfer Learning with Keras
7. Object Detection
3. Recurrent Neural Networks
1. Recurrent Neurons and Layers
2. Basic RNNs in TensorFlow
3. Training RNNs
4. Deep RNNs
5. Forecasting a Time Series
6. LSTM Cell
7. GRU Cell
4. Natural Language Processing
1. Introduction to Natural Language Processing
2. Creating a Quiz Using TextBlob
3. Finding Related Posts with scikit-learn
4. Generating Shakespearean Text Using Character RNN
5. Sentiment Analysis
6. Encoder-Decoder Network for Neural Machine Translation
7. Attention Mechanisms
8. Recent Innovations in Language Models
5. Training Deep Neural Networks
1. The Vanishing / Exploding Gradients Problems
2. Reusing Pretrained Layers
3. Faster Optimizers
4. Avoiding Overfitting Through Regularization
5. Practical Guidelines to Train Deep Neural Networks
6. Custom Models and Training with Tensorflow
1. A Quick Tour of TensorFlow
2. Customizing Models and Training Algorithms
3. Tensorflow Functions and Graphs
7. Loading and Preprocessing Data with TensorFlow
1. Introduction to the Data API
2. TFRecord Format
3. Preprocessing the Input Features
4. TF Transform
5. The TensorFlow Datasets (TDFS) Projects
8. Autoencoders and GANs
1. Efficient Data Representations
2. Performing PCA with an Under Complete Linear Autoencoder
3. Stacked Autoencoders
4. Unsupervised Pre Training Using Stacked Autoencoders
5. Denoising Autoencoders
6. Sparse Autoencoders
7. Variational Autoencoders
8. Generative Adversarial Networks
9. Reinforcement Learning
1. Learning to Optimize Rewards
2. Policy Search
3. Introduction to OpenAI Gym
4. Neural Network Policies
5. Evaluating Actions: The Credit Assignment Problem
6. Policy Gradients
7. Markov Decision Processes
8. Temporal Difference Learning and Q-Learning
9. Deep Q-Learning Variants
10. The TF-Agents Library

Course on Large Language Models and Generative AI

1. Introduction to Large Language Models
1. What are LLMs?
2. Evolution of Natural Language Processing
3. How does an LLM outputs a word?
4. Next Word Prediction
5. Training and using LLMs
6. Ways to use LLMs:- Text Response and Embeddings
7. LLM Embeddings
8. Sentiment Analysis with LLMs
9. Serendipity in LLMs
10. How are LLMs revolutionizing industries today?
2. Generative Pre-Trained Transformer(GPT)
1. Attention Mechanism
2. Transformer Architecture and components
3. GPT Architecture
4. GPT Training Process:- Pre-Training and Fine-Tuning
5. Building your own GPT from scratch using Tensorflow
3. OpenAI ChatGPT
1. Introduction to ChatGPT
2. ChatGPT Architecture and Training
4. Vector Databases
1. Introduction to Vector Databases
2. Architecture of Vector Databases
3. Indexing Techniques
4. Distance Metrics and Similarity Measures
5. Nearest Neighbor Search
6. Open Source Vector Databases:- Chroma and Milvus
5. Langchain
1. Introduction to Langchain
2. The building blocks of LangChain:- Prompt, Chains, Retrievers, Parsers, Memory and Agents
3. Building a RAG based chat agent
4. Building a Text to SQL query generator
5. Building a RAG based chat agent web app using Flask
6. Stable Diffusion
1. Introduction to Stable Diffusion
2. Stable Diffusion Components
3. Diffusion Model
4. Stable Diffusion Architecture and Training
7. Prompt Engineering
1. Art of Prompt Engineering
8. Gen AI with APIs
1. Hands-on with commonly used Gen AI APIs such as GPT, DALL-E, Whisper, Midjourney, etc.
2. Developing a Voice-Controlled RAG Chat Agent App
3. Group mobile app reviews to generate clean actionable insights using GPT
4. Building an OpenAI agent to automate tasks
5. Building a QR Code AI Art Generator
6. Building an image editor to edit images using text
9. Use of Common AI tools for automating daily tasks

Course on Data Engineering (Self-Paced)

1. Introduction to Hadoop
1. Introduction
2. Distributed systems
3. Big Data Use Cases
4. Various Solutions
5. Overview of Hadoop Ecosystem
6. Spark Ecosystem Walkthrough
2. Foundation & Environment
1. Understanding the CloudxLab
2. Getting Started - Hands on
3. Hadoop & Spark Hands-on
4. Understanding Regular Expressions
5. Setting up VM
3. Zookeeper
1. ZooKeeper - Race Condition
2. ZooKeeper - Deadlock
3. How does election happen - Paxos Algorithm?
4. Use cases
5. When not to use
1. Why HDFS?
2. NameNode & DataNodes
3. Advance HDFS Concepts (HA, Federation)
4. Hands-on with HDFS (Upload, Download, SetRep)
5. Data Locality (Rack Awareness)
1. Why YARN?
2. Evolution from MapReduce 1.0
3. Resource Management: YARN Architecture
4. Advance Concepts - Speculative Execution
6. MapReduce Basics
1. Understanding Sorting
2. MapReduce - Overview
3. Word Frequency Problem - Without MR
4. Only Mapper - Image Resizing
5. Temperature Problem
6. Multiple Reducer
7. Java MapReduce
7. MapReduce Advanced
1. Writing MapReduce Code Using Java
2. Apache Ant
3. Concept - Associative & Commutative
4. Combiner
5. Hadoop Streaming
6. Adv. Problem Solving - Anagrams
7. Adv. Problem Solving - Same DNA
8. Adv. Problem Solving - Similar DNA
9. Joins - Voting
10. Limitations of MapReduce
8. Analyzing Data with Pig
1. Pig - Introduction
2. Pig - Modes
3. Example - NYSE Stock Exchange
4. Concept - Lazy Evaluation
9. Processing Data with Hive
1. Hive - Introduction
2. Hive - Data Types
3. Loading Data in Hive (Tables)
4. Movielens Data Processing
5. Connecting Tableau and HiveServer 2
6. Connecting Microsoft Excel and HiveServer 2
7. Project: Sentiment Analyses of Twitter Data
8. Advanced - Partition Tables
9. Understanding HCatalog & Impal
10. NoSQL and HBase
1. NoSQL - Scaling Out / Up
2. ACID Properties and RDBMS Story
3. CAP Theorem
4. HBase Architecture - Region Servers etc
5. Hbase Data Model - Column Family Orientedness
6. Getting Started - Create table, Adding Data
7. Adv Example - Google Links Storage
8. Concept - Bloom Filter
9. Comparison of NOSQL Databases
11. Importing Data with Sqoop and Flume, Oozie
1. Sqoop - Introduction
2. Sqoop Import - MySQL to HDFS
3. Exporting to MySQL from HDFS
4. Concept - Unbounding Dataset Processing or Stream Processing
5. Flume Overview: Agents - Source, Sink, Channel
6. Data from Local network service into HDFS
7. Example - Extracting Twitter Data
8. Example - Creating workflow with Oozier
12. Introduction to Spark
1. Apache Spark ecosystem walkthrough
2. Spark Introduction - Why Spark?
13. Scala Basics
1. Introduction, Access Scala on CloudxLab
2. Variables and Methods
3. Interactive, Compilation, SBT
4. Types, Variables & Values
5. Functions
6. Collections
7. Classes
8. Parameters
14. Spark Basics
1. Apache Spark ecosystem
2. Why Spark?
3. Using the Spark Shell on CloudxLab
4. Example 1 - Performing Word Count
5. Understanding Spark Cluster Modes on YARN
6. RDDs (Resilient Distributed Datasets)
7. General RDD Operations: Transformations & Actions
8. RDD lineage
9. RDD Persistence Overview
10. Distributed Persistence
15. Writing and Deploying Spark Applications
1. Creating the SparkContext
2. Building a Spark Application (Scala, Java, Python)
3. The Spark Application Web UI
4. Configuring Spark Properties
5. Running Spark on Cluster
6. RDD Partitions
7. Executing Parallel Operations
8. Stages and Tasks
16. Common Patterns in Spark Data Processing
1. Common Spark Use Cases
1. Example 1 - Data Cleaning (Movielens)
1. Example 2 - Understanding Spark Streaming
2. Understanding Kafka
3. Example 3 - Spark Streaming from Kafka
4. Iterative Algorithms in Spark
5. Project: Real-time analytics of orders in an e-commerce company
17. Data Formats & Management
1. XML
3. How to store many small files - SequenceFile?
4. Parquet
5. Protocol Buffers
6. Comparing Compressions
7. Understanding Row Oriented and Column Oriented Formats - RCFile?
18. DataFrames and Spark SQL
1. Spark SQL - Introduction
2. Spark SQL - Dataframe Introduction
3. Transforming and Querying DataFrames
4. Saving DataFrames
5. DataFrames and RDDs
6. Comparing Spark SQL, Impala, and Hive-on-Spark
19. Machine Learning with Spark
1. Machine Learning Introduction
2. Applications Of Machine Learning
3. MlLib Example: k-means
4. SparkR Example
Months of Instructor led Training
Days of Lab Access


EMI Starting at  137/month

Course Fee: 2,999 4,199
(Additional Scholarship Available 250)

Batch 10

(16 June 2024)

Admission closed

Batch 11

(06 July 2024)

Take the Test Now

Low cost EMI


EMI Starting at 137/month

VIEW ALL EMI PLANS | Scholarship available

Earn Scholarship of 250 in Just 30 Minutes!

    1. Get 20 OFF for every correct answer.
    1. Test includes 25 questions (Max Duration: 30 Mins).
    1. Get straight 250 OFF on answering all questions correctly.
    1. You can take the test anytime, anywhere!
    1. Best of first three attempts will be considered.

Scholarship Test

Take the Test Now

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Admission Process

  • Step 1. Take the Scholarship Test

  • Step 2. Test results will be announced in 24 hours

  • Step 3. Join the Prestigious Program
    The admission office will send the letter of acceptance. Submit the admission fees in due time to confirm the seat

  • Note: Admission test should be taken immediately after submitting the application using the link displayed post application submission.

Eligibility Criteria

    1. Anybody in their final year of undergraduate degree or has completed their undergraduation is eligible to apply for the course
    1. Must have studied Mathematics in 12th standard

Additional Scholarships

    1. 5% Scholarships are available for students, women from STEM background and unemployed
    1. 5% Scholarship available for IIT Alumni and CloudxLab Alumni.
    1. Bring your friend along and avail discount upto 5%.

PS: Details to avail the scholarship will be sent post application-submission and only one scholarship applicable per learner

Certification Guideline

Learners are expected to complete at least 80% of the course content and any 3 of the mandatory projects within 365 days of batch commencement to be eligible for the certificate.

Placement Assistance

By CloudxLab

Placement Eligibility Test

Placement Eligibility Test

We have around 300+ recruitment partners who will be interviewing you based on your performances in PET

Dedicated Job Portal

Dedicated Job Portal

Opportunities from companies who approach us asking for our learner profiles will be posted on our job portal to providevisibility to your profile

Career Guidance Webinars

Career Guidance Webinars

Career Guidance Webinars from seasoned industry experts


Frequently Asked Questions

Will I get support?

Yes! Please feel free to ask your questions on CloudxLab forum and our community and team of experts will answer your questions. We believe forum will add better perspectives, ideas, and solutions to your questions.

Can I get a certificate for the projects completed?

We have created a set of Guided Projects on our platform. You may complete these guided projects and earn the certificate for free. Check it out here

Can I expect any placement support?

Yes, we do offer placement assistance that includes career guidance, resume building tips and mock interviews. Each participant will receive staunch support from the industry mentors, who also direct you through various placement opportunities within the industry. Above all, we are partnered with leading MNC’s that offer placement opportunities to our participants.

Do I need to install any software before starting this course?

No, we will provide you with the access to our online lab and BootML so that you do not have to install anything on your local machine

What is the validity of course material?

We understand that you might need course material for a longer duration to make most out of your subscription. You will get lifetime access to the course material so that you can refer to the course material anytime.

What if I miss a class?

You will never lose any lecture. You can view the recorded session of the class in your LMS.

What is the refund policy?

If you are unhappy with the product for any reason, you can ask for a full refund up to 14 days after the start of the first live sessions. Please contact us at reachus@cloudxlab.com to request a refund within the stipulated time. We will be sorry to see you go though!

I have some more questions. Can I talk to someone?

Absolutely! Please contact us here. You can also reach us anytime on our 24/7 support helpline by calling us on +918049202224

Will there be Options to Pay using EMI/Installments

Yes, you can choose to pay by installments on the payment page.

How will the payment for the course be made?

The course fee will be paid in two parts:

Admission fee - This is a small percentage of the total course fee which helps to reserve your seat for the program

Installments - The remaining course fee will be paid in equal installments monthly as per the EMI option selected while enrolling in the course.

If I have more questions during the week days apart from the live sessions, how can I get it cleared?

Teaching assistants will be helping you during the weekdays to ensure a seamless learning experience. You will be able to have a session at your and the teaching assistants convenience and get your queries cleared over WhatsApp, email or call. The learners will be part of a WhatsApp group with the course faculty so that all the queries the learners may have are answered 24/7.

Is there an admission fee for the PG Certification Programs?

No, there are no additional charges for admission. But, if you are choosing to pay by EMI, there will be a fixed admission charge that is to be paid. This amount will be subtracted from the total course fees.