Generative AI

Beginner to Mastery

Masters in Data Science & Generative AI

Data Science & AI Mastery

Practical Tools Training

Advanced AI Specialization

Practical projects and real-world case studies for job readiness.

Group Enrollment with Friends or Colleagues

Masters in Data Science & Generative AI

Course Duration

650 Hours

Next Batch

14 September 2025

Course Material

Live. Online. Interactive.

Expert instruction from industry professionals.

Career-focused curriculum aligned with top employer demands.

Flexible learning paths tailored to individual career goals.

Networking opportunities with peers and industry experts.

Highlight Masters in Data Science & Generative AI

KEY HIGHLIGHTS OF MASTERS IN DATA SCIENCE & GENERATIVE AI PROGRAM

  • Weekly sessions with industry professionals
  • Dedicated Learning Management Team
  • 650 hours of hands-on learning experience
  • Over 240 hours live sessions spread across 11 months
  • 240 hours of self-paced Learning
  • Learn from IIT Faculty & Industry experts
  • More than 50+ industry-related projects and case studies
  • Personalised mentorship sessions with industry experts
  • 24*7 Support
  • Dedicated Learning Management Team
  • 1:1 Mock Interview
  • No-Cost EMI Option
  • Designed for both working professionals and fresh graduates
  • High Demand and Career Opportunities
  • Competitive Edge and Innovation
  • Problem-Solving and Critical Thinking

WHY JOIN MASTERS IN DATA SCIENCE & GENERATIVE AI PROGRAM?

In-Demand Skills

Gain expertise in data science, machine learning, and AI, with a focus on both foundational and cutting-edge technologies.

Hands-On Learning

Acquire practical experience with industry-standard tools and real-world applications.

Career Advancement

Boost your career prospects with a curriculum tailored to meet the demands of top employers.

Expert Instruction

Learn from industry professionals with real-world experience.

UPCOMING BATCH:

14 September 2025

SkillzRevo

SkillzRevo Solutions

30 MINUTE MEETING

Web conferencing details provided upon confirmation.

Corporate Training, Enterprise training for teams

Batch schedule

BatchBatch Type
Online Live Instructor Led SessionFull-Time
Online Live Instructor Led SessionPart-Time

Regional Timings

BatchBatch Type
IST (India Standard Time)09:00 PM–12:00 AM
Bahrain, Qatar, Kuwait, Saudi Arabia06:30 PM–09:30 PM
UAE / Oman07:30 PM–09:00 PM

Masters in Data Science & Generative AI OVERVIEW

This Program offers a blend of theory and practice for future data scientists and AI professionals. This Program spans fundamental data science skills, advanced machine learning including Gen AI using methods like GANs, VAEs, LLMs, MidJourney, and LangChain. With a combination of hands-on projects and case studies, the program introduces all its learners to computer science in collaboration with mathematics and ethics thereby transforming them into leaders positions within technology-sector business or research.

ENROLL NOW & BOOK YOUR SEAT AT FLAT 50% WAIVER ON FEE

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Masters in Data Science & Generative AI Objectives

This course is to equip students with a deep understanding of the foundational and advanced concepts in data science and artificial intelligence. This course teaches programming languages such as Python and R, statistical analysis, machine learning techniques by making you an expert in specialized domains like computer vision, NLP, Deep Learning among others. Courses include practical training with tools such as SQL, Tableau, Power BI and Advanced Excel to help learn how to handle, analyze and visualize the data. The course provides a view of the most advanced Generative AI technologies, creating students capable of innovating in technology that is here for future human progression at an increasing rate. Ultimately, the course aims to prepare graduates for leadership roles in data science and AI, enabling them to drive data-driven decision-making and innovation in various industries.

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Why Learn Masters in Data Science & Generative AI ?

Comprehensive Skill Development

Gain expertise in data science and AI, from foundational concepts to advanced techniques like deep learning, computer vision, and NLP.

Cutting-Edge Technology

Learn the latest in Generative AI, a rapidly growing field that drives innovation in various industries.

Practical Application

Develop hands-on experience with essential tools such as Python, R, SQL, Tableau, and Power BI, ensuring you're ready for real-world challenges.

Career Advancement

Prepare for leadership roles in data science and AI, with a curriculum designed to meet the demands of top employers.

Interdisciplinary Approach

Benefit from a holistic program that integrates statistics, programming, machine learning, and advanced AI, making you a versatile and in-demand professional.

Industry-Relevant Training

Stay ahead of the curve with a course designed to address the latest trends and needs in the data science and AI landscape.

Global Opportunities

Open doors to international career paths by mastering skills that are in demand across industries and geographies.

Capstone Projects & Real Case Studies

Work on industry-based projects and case studies that help bridge the gap between theoretical knowledge and real-world problem-solving.

Program Advantages

Expert-led instruction from industry professionals with real-world experience in data science, and AI.

Learn from experienced instructors with deep industry expertise.

Hands-on learning through practical projects and case studies using industry-standard tools like Tableau, Power BI, and Advanced Excel.

Interdisciplinary approach that integrates statistics, programming, and AI for a well-rounded understanding of data science principles.

Comprehensive curriculum covering key areas of data science and AI, including machine learning, deep learning, NLP, computer vision, and generative AI.

Engage in practical exercises and real-world case studies to apply concepts effectively.

Career-focused outcomes with skills aligned to meet the demands of top employers, enhancing your job prospects.

Real-world applications, preparing you to solve complex challenges in various industries.

Description

Masters in Data Science & Generative AI program Certifications

Nasscom

Nasscom

Course Completion

Course Completion

Capstone Project Completion

Capstone Project Completion

Masters in Data Science & Generative AI Curriculum

Lecture 01: Microsoft Excel Overview, Basic Navigation and Usage, Cell referencing, Formatting Excel, Advanced Formatting, Shortcuts and Basic Formulas
Lecture 02: Sorting, Filtering, Advanced Filtering, Charts, Types of Charts, Advanced Charting Techniques and Pivot Tables, Creating, Grouping and Summarizing Data
Lecture 03: Lookup Function, Vlookup, Using VLOOKUP with Multiple, Criteria Hlookup, Combining HLOOKUP with Other Functions, Match Function, Using MATCH for Dynamic Referencing
Lecture 04: Introduction to VBA & Macros, Understanding VBA basics, Debugging and error handling, Advanced VBA Techniques, Integrating VBA with Excel functions, Designing Effective Dashboards, Building a Dashboard
Lecture 05: Understanding the basics of data analysis, Data Import and Cleaning, Using Formulas and Functions, Data Visualization, Descriptive statistics
Lecture 06: Advanced Data Analysis Techniques, DAX, Scenario and Sensitivity Analysis, Dashboards and Reports, Case Studies and Real-World Applications, Practical examples of data analysis in Excel
Lecture 08: Introduction to Python, Why Python, Variables, Data Types, Type castings, Strings, Indexing
Lecture 09: Operators and Conditional Statements, Looping Statements and its Control Statement
Lecture 10: Lambda Functions, *args, **kwargs, Functions
Lecture 11: Data Structures - List, Tuple and List Comprehensions
Lecture 12: Data Structures - Set and Dictionaries
Lecture 13: Classes, Objects and Constructors, Inheritance
Lecture 14: Polymorphism, Abstraction and Encapsulation
Lecture 15: Connecting to Databases, Establishing connections to databases, Executing SQL Queries, ORM (Object-Relational Mapping), Working with NoSQL Databases
Lecture 16: Introduction of Numpy, and Pandas
Lecture 17: Introduction of Seaborn and Matplotlib
Lecture 18: Introduction to Statistics, Descriptive Statistics, Sample, Population, Major of Central Tendency, Standard Deviation
Lecture 19: Variance, Range, IQR, Outliers, Correlation, Covariance Skewness, Kurtosis, Probability
Lecture 20: Probability, Probability distributions, Central Limit Theorem, Binomial and Poisson Distribution
Lecture 21: Normal Distribution, Type I & Type II Error
Lecture 22: T-test, Z-test, Hypothesis Testing Interview Questions
Lecture 23: Introduction to ML, Types of variables, Encoding, Normalization, Standardization, Types of ML, Linear Regression
Lecture 24: Linear Regression, Logistic Regression, SVM, KNN, Naïve Bayes, Decision Tree, Random Forest
Lecture 25: Mean Absolute Error, Mean and Root Mean Square Error, Confusion Matrix, R2 Score, Adjusted R2 Score, F1 Score
Lecture 26: Classification Report, AUC ROC, Accuracy, Ensemble Techniques, Random Forest, Xgboost
Lecture 27: Unsupervised Machine Learning, PCA, Clustering, k-Means Clustering and Hierarchical Clustering
Lecture 28: Introduction to Neural Network, Forward Propagation, Activation Function
Lecture 29: Activation Function (Linear, Sigmoid, Relu, Leaky Relu), Optimizers, Gradient Descent, Stochastic Gradient Descent
Lecture 30: Mini batch Gradient Descent, Adagrad, Padding, Pooling, Convolution
Lecture 31: Checkpoints and Neural Networks Implementation and Introduction to Time Series Analysis
Lecture 32: Various components of the TSA, Decomposition Method (Additive Method and Multiplicative)
Lecture 33: ARMA and ARIMA
Lecture 34: Introduction to R, Installing R and RStudio, Basics of RStudio IDE, Writing and executing R scripts, Variables and Data Type in R, Operators
Lecture 35: Creating vectors, Vector indexing and slicing, Vectorized operations, Creating matrices, Matrix operations, Matrix indexing, Creating lists, Creating data frames, Indexing and manipulating lists and data frames
Lecture 36: Conditional statements, Loops, Applying functions, Flow Control, Functions in R, Object-Oriented Programming in R, S3 and S4 classes, Methods and inheritance, Creating and using objects
Lecture 37: Creating and using factors, Working with dates and times, Reading and writing (CSV files and Excel files), Introduction to the readr and readxl packages
Lecture 38: Introduction to dplyr, Selecting, filtering, and arranging data, Grouping data, Summarizing data with summarize and mutate
Lecture 39: Data Manipulation in R - dplyr, Data Manipulation & Data Visualization in R - tidyr
Lecture 40: Introduction to Text Mining, Text Preprocessing, Document-Term Matrix (DTM) and TF-IDF, Exploratory Text Analysis, Sentiment Analysis
Lecture 41: Install Necessary Packages, Create a New Package, Package Structure, Writing Functions, Documenting Functions, Testing Your Package, Building and Checking and Sharing Your Package
Lecture 42: Introduction to APIs, Using the 'httr' Package, GET & POST Request, and Authentication, Introduction to Web Scraping, Using the 'rvest' Package, Handling Dynamic Content, Handling Sessions and Cookies
Lecture 43: Connecting to Databases in R, Packages Installation, Connect to Database, Execute Queries, Write Data, Disconnect and Error Handling
Lecture 44: Project Session
Lecture 45: Orientation Session (Introduction to Business Intelligence
Lecture 46: Basics of Database, Types of Database, Data Types, SQL Operators, Expression, Create, Insert
Lecture 47: Drop, Truncate, Delete, Alter, Update, Select, Range, Operator, IN, Wildcard, Like, Clause
Lecture 48: Constraint, Aggregation Function, Group by, Order by, Having
Lecture 49: Joins, Case, Complex Queries, Doubt Clearing
Lecture 50: Tableau Desktop, Tableau products
Lecture 51: Data import, Measures, Filters
Lecture 52: Data transformation, Marks, Dual Axis
Lecture 53: Manage worksheets, Data visualization, Dashboarding, Project
Lecture 54: Power BI Platform, Process Flow
Lecture 55: Features, Dataset, Bins
Lecture 56: Pivoting, Query Group, DAX Function
Lecture 57: Formula, Charts, Reports, Dashboards
Lecture 58: Bookmarks and Buttons, Conditional Formatting and Sorting, and Report Layout and Interaction
Lecture 59: Tabular Visuals, Modelling and Calculations, Advanced Data Modelling Scenarios and DAX in Power BI
Lecture 60: Project Session
Lecture 61: Orientation Session (Introduction to Artificial Intelligence)
Lecture 62: Introduction to Image Processing, Feature Detection, OpenCV
Lecture 63: Convolution, Padding, Pooling & its Mechanisms
Lecture 64: Forward Propagation & Backward Propagation for CNN
Lecture 65: CNN Architectures like AlexNet, VGGNet, InceptionNet, ResNet, Transfer Learning
Lecture 66: Introduction to Text Mining, Text Processing using Python and Introduction to NLTK
Lecture 67: Sentiment Analysis, Topic Modeling (LDA) and Named Entity Recognition
Lecture 68: BERT, Text Segmentation, Text Mining, Text Classification
Lecture 69: Automatic Speech Recognition, Introduction to Web Scraping
Lecture 70: RL Framework, Components of RL Framework, Examples of Systems
Lecture 71: Types of RL Systems, Q-Learning
Lecture 72: Project Session
Lecture 73: Orientation Session (Introduction to Gen AI)
Lecture 74: Introduction to AI, Hype vs. Reality, Business Applications, Ethical Considerations
Lecture 75: Introduction to Open Source Huggingface Transformers Platform
Lecture 76: Feature Engineering: Normalization, Stemming, Lemmatization, Stop Word Removal
Lecture 77: Sentiment Analysis, Sentence Classification, Generating Text
Lecture 78: Understanding Language Models, Introduction to Transformer Models
Lecture 79: Types of Models, Attention Mechanism, Tasks for Transformer Models
Lecture 80: Introduction to Large Language Models (LLMs)
Lecture 81: Other Types of Generative AI Algorithms
Lecture 82: Hands-On Practice of NLP Tasks using Huggingface
Lecture 83: Applications of Generative AI in Business
Lecture 84: Langchain, Applied Use Case for Gen AI
Lecture 85: AI Application Stack: Infrastructure & Foundation Layer
Lecture 86: Hallucination, Data Privacy, Ethics, and Environmental Impact of AI
Lecture 87: Project Session

Masters in Data Science & Generative AI Skills Covered

Proficiency in Programming
Statistical Analysis
Advanced Machine Learning Techniques
Deep Learning and AI Techniques
Computer Vision Techniques
Natural Language Processing Techniques
Reinforcement Learning Techniques
Advanced Data Analysis in Excel
Data Analysis and Visualization
SQL querying
Transformer architectures Language Modeling

Masters in Data Science & Generative AI Tools Covered

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Masters in Data Science & Generative AI Program Benefits

Masters in Data Science & Generative AI Program Benefits Illustration

CAREER OPPORTUNITIES AFTER THIS COURSE

AI Product Manager Salary Range

Min

$100,000

Average

$145,000

Max

$190,000

Projects

MASTER GANS, TRANSFORMERS & DEEP LEARNING WITH REAL PROJECTS

Practice Essential Tools

Designed By Industry Experts

Get Real-world Experience

Machine Learning
NO. OF PROJECTS: 10
Deep Learning
NO. OF PROJECTS: 10
Generative AI
NO. OF PROJECTS: 8

Capstone Projects of this Program

Multimodal Text-to-Image Generator

A system that creates high-quality, realistic images from descriptive text inputs, blending multiple data types for creative visual output.

AI Music Composer with Genre Adaptation

An AI tool that generates original musical compositions while adapting to specific genre styles, tempo, and mood preferences.

Realistic Deepfake Video Synthesizer

A tool that realistically manipulates and synthesizes video content to create authentic-looking deepfake footage for various applications.

Personality-Driven Conversational AI Assistant

A chatbot designed to interact with a distinct and consistent personality, providing a more natural and engaging user experience.

Artistic Style Transfer Engine for Videos

A program that applies the artistic style of a source image to every frame of a video, creating a new, stylized visual experience.

Context-Aware Text Summarization System

An advanced system that generates concise summaries of long texts by understanding the document's broader context and key information.

Voice Cloning and Speech Style Transfer Tool

A tool that replicates a speaker's voice and transfers a specific speech style or emotion to new synthesized audio content.

Interactive AI Storyteller with Image Generation

An AI that collaboratively creates stories with users, dynamically generating scenes and characters based on a developing narrative.

2D Sketch to 3D Object Generation Pipeline

A seamless workflow that converts simple two-dimensional sketches into detailed, three-dimensional models for creative and design purposes.

Synthetic Data Generator for Model Training

A system that produces large and diverse datasets for machine learning models, overcoming data scarcity for effective training.

AI-Powered Video Subtitle Generator and Translator

An automated solution that transcribes spoken language in videos and translates it into accurate subtitles for global accessibility.

Generative Design Tool for Product Prototyping

An AI application that rapidly generates numerous product design prototypes based on a set of constraints and functional requirements.

RAG-based Research Assistant (Retrieval-Augmented Generation)

An intelligent assistant that answers complex queries by retrieving relevant information from large documents and generating coherent responses.

Multilingual Chatbot with Memory and Personality

A conversational agent that can communicate in multiple languages while maintaining a consistent personality and recalling past interactions.

AI-Powered Comic Strip Generator from Text Prompts

A tool that creates unique comic strips by converting user text prompts into sequential images with a consistent artistic style.

Explainable AI Engine for Text Classification

An AI system that classifies text and provides clear, understandable reasons for its decisions, ensuring transparency and trust.

Autonomous Slide Generator from Research Papers

An AI that automatically extracts key findings and data from research papers to create well-structured, presentable slide decks.

AI Assistant for Code Generation and Explanation

A helpful tool that writes code snippets and explains complex programming logic in simple terms, assisting developers with their tasks.

AI-Driven Marketing Content Generator Across Formats (text, image, video)

A unified platform that produces marketing materials in various formats, including text, images, and videos, from a single campaign brief.

Real-Time Emotion Detection and Response AI System

A system that identifies human emotions from facial expressions or speech and generates appropriate, empathetic responses in real time.

Job Obligation After This Course

WE CAN APPLY FOR JOBS IN

Build in-depth reports and dashboards with Tableau, Excel to deliver data findings efficiently across stakeholders.

Targeted at your domain experts to develop, train and deploy AI models using Generative AI for solving complex business problems.

Collect, clean, and analyze data to extract meaningful insights that inform business decisions.

Develop and tune SQL queries to maintain database performance profiling for relational databases.

Use statistical and analytical methods to find trends, patterns or opportunities in your dataset.

Provide data insights to support and inform business decisions, ensuring organizations can realize their goals.

Automate routine tasks using Python to enhance efficiency and reduce manual workload.

Collaboratively partner with IT, management and other key stakeholders from cross-functional teams to implement data-driven solutions.

We gradually got trends on all our fingertips and advancements in data analytics, AI, and related technologies to maintain expertise and relevance.

Companies Hiring for this Course

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Admission Process

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.

Course Fees & Financing

Course Fees

Enroll Now & Save Up To

60%

In USD

$2999

In INR

249,999

Inclusive of All Taxes

Enroll Now →
Payment Partners

We partnered with financing companies to provide competitive finance options at 0% interest rate with no hidden costs.

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UPCOMING BATCHES/PROGRAM COHORTS

BatchDateTime (IST)Batch Type
Online Live Instructor Led Session30 August 20259 PM to 12 AMBatch 1
Online Live Instructor Led Session14th Sept 20259 PM to 12 AMBatch 2

COMPARISON WITH OTHERS

FeatureOur CourseCOMPETITOR ACOMPETITOR B
Tools CoveredAdvanced Robotics, Quantum Computing, Python, TensorFlow, and cutting-edge AI tools like GPT-4, StyleGAN, Neural Architecture SearchPrimarily includes Robotic Process Automation, Python, and TensorFlow, with limited advanced AI toolsDeep Learning, Basic StyleGAN, Limited Computer Vision
AI FocusIntensive focus on next-gen AI technologies and applicationsBasic AI concepts, with limited next-gen focusAdvanced AI techniques, Basic Computer Vision
Real-World ApplicationInnovative projects that integrate AI technologies to address cutting-edge industry challengesProjects often focus on automation with limited AI applicationSome real-world projects with a focus on computer vision
Expert-Led InstructionJCourses led by top experts in robotics, quantum computing, and AIInstructors with a focus on automation and basic AIIndustry specialists in deep learning
Career OpportunitiesPrepares students for cutting-edge roles, including AI Researcher, Robotics Engineer, Quantum Computing Specialist, and Advanced Data ScientistTypically prepares students for automation and basic data science rolesProvides some career advancement
Cross-Industry RelevanceSkills relevant across multiple sectors, including technology, finance, healthcare, and advanced researchSkills are more niche with limited cross-industry applicationSome cross-industry applications
Networking OpportunitiesExtensive networking with top experts in robotics, AI, and quantum computingNetworking is typically limited to automation professionalsSome networking with AI experts
Investment ValueRHigh return on investment due to integration of cutting-edge technologies and industry relevanceModerate value with a focus on automation and basic data scienceProvides good value for advanced AI careers

Frequently Asked Questions