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8-month program

100% live sessions

1 Capstone project

34+ industry projects

Earn 7 Credits

Through SOM

350+ Hiring partners

50% Average salary hike

3 Days campus visit

At IIT Roorkee

E&ICT, IIT Roorkee

Certificate

Course Overview

Home All Courses Post Graduate certificate Program in Applied Data Science and AI, E&ICT IIT Roorkee - January Batch 12

Post Graduate certificate Program in Applied Data Science and AI, E&ICT IIT Roorkee - January Batch 12

The PG Certificate Course in Data Science, AI/ML & Data Engineering 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 will empower you to solve complex problems and make impactful data-informed decisions. On completing the course successfully, you will receive a certificate, from E&ICT 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

8-month live training every week
3 Days campus immersion at IIT Roorkee
Earn 7 credits through Statement of Marks
34+ Industry relevant projects
1 Capstone project
100% Placement support
15+ Languages and tools covered
24*7 doubt-clearing support
Earn exclusive scholarship from IIT Roorkee
No programming background required
Lifetime access to course material
240 Days of cloud lab access

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Application Deadline 08  December 2024

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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.
  • Get This Certificate

    • 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
    QS

    #1st

    Among the IITs in the ‘Citations per Faculty’ parameter

    *QS World Rankings

    India Today

    #5

    Ranked Engineering College

    *India Today 2020

    NIRF

    #6

    Ranked for IITs

    *NIRF 2020

    QS

    #12

    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

    NASA

    Instructor Praveen

    Praveen Pavithran

    Co-Founder at Yatis

    Past: YourCabs, Cypress Semiconductor

    Instructor Jatin Shah

    Jatin Shah

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

    IIT-B

    Instructor Shubh Tripathi

    Shubh Tripathi

    ML Engineer at CloudxLab

    Curriculum

    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
    8. YOLO
    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
    4. HDFS
    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)
    5. YARN
    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
    2. AVRO
    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
    8
    Months of Instructor led Training
    240
    Days of Lab Access
    34+
    Projects
    14.2K+
    Learners
    Download Curriculum

    Skills Covered

    31+ skills covered under this course

    Python Programming
    SQL
    Data Science
    Data Analysis
    Data Visualization
    Data Mining
    Big Data
    Mathematical Modelling
    Descriptive Statistics
    Inferential Statistics
    Hypothesis Testing
    Statistical Analysis
    Machine Learning
    Supervised Learning
    Unsupervised Learning
    Deep Learning
    Reinforcement Learning
    Generative AI
    Artificial Intelligence
    Model Training and Optimization
    Machine Learning Algorithms
    Model Evaluation and Validation
    Large Language Models
    Conversational AI
    Natural Language Processing
    Speech Recognition
    Computer Vision
    Prompt Engineering
    ChatGPT
    Story Telling
    Research Methods

    Projects

    EMI Starting at  137/month

    Course Fee: 2,999 4,199

    Batch 11

    (Enrollment Closed)

    Batch 12

    (08 December 2024)

    Enroll Now, 5 seats left

    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

    Testimonials

    ​

    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?

    You will be able to get your queries cleared over WhatsApp, email, call and discussion forums on cloudxlab.

    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.

    Where can I verify if the course is from E&ICT Academy, IIT Roorkee?

    You can see the program listed on the E&ICT Website using the following link: https://eict.iitr.ac.in/post-graduate-certificate-program-in-applied-data-science-ai/

    Will the Certificate contain the IIT Roorkee Logo?

    Yes, the PG Certificate course contains IIT Roorkee Logo, you can see the sample certificate here: https://cxl-web-prod-uploads.s3.amazonaws.com/public/filestore-uploads/2ec3a1a0f7f350b2be5eeac9e56455d87a54ffbf.webp

    Is the certification recognized by WES?

    The Post Graduate Certificate in Applied Data Science and AI, offered in collaboration with IIT Roorkee, is globally recognized. It is also accredited by the World Education Services (WES), ensuring international acknowledgment, which makes it valuable for career opportunities worldwide.