Master’s in Data Science & Gen AI
August 17, 2024 2025-03-23 9:11Master’s in Data Science & Gen AI
Master's in Data Science & Gen AI
Comprehensive coverage of data science, AI, and advanced topics like generative AI.
Hands-on experience with tools such as Python, R, SQL, Tableau, and Power BI.
Practical projects and real-world case studies for job readiness.
Advanced training in deep learning, computer vision, and natural language processing.
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.
Emphasis on the latest trends and technologies in data science and AI.
Key Highlights of Master's in Data Science & Gen AI
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
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
Learn from Industry Experts.
Why Join Master's in Data Science & Gen AI?
Upcoming Batch:-
19th January 2025 (10pm to 1 am )
1st of February 2025 (10 pm to 1 am)
Master's in Data Science & Gen 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.
Master's in Data Science & Gen 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.
Why Learn Master's in Data Science & Gen AI ?
Program Advantages
Expert-led instruction from industry professionals with real-world experience in data science, and AI.
Comprehensive curriculum covering key areas of data science and AI, including machine learning, deep learning, NLP, computer vision, and generative AI.
Learn from experienced instructors with deep industry expertise.
Engage in practical exercises and real-world case studies to apply concepts effectively.
Hands-on learning through practical projects and case studies using industry-standard tools like Tableau, Power BI, and Advanced Excel.
Career-focused outcomes with skills aligned to meet the demands of top employers, enhancing your job prospects.
Interdisciplinary approach that integrates statistics, programming, and AI for a well-rounded understanding of data science principles.
Real-world applications, preparing you to solve complex challenges in various industries.
Expert-led instruction from industry professionals with real-world experience in data science, and AI.
Comprehensive curriculum covering key areas of data science and AI, including machine learning, deep learning, NLP, computer vision, and generative AI.
Learn from experienced instructors with deep industry expertise.
Engage in practical exercises and real-world case studies to apply concepts effectively.
Hands-on learning through practical projects and case studies using industry-standard tools like Tableau, Power BI, and Advanced Excel.
Career-focused outcomes with skills aligned to meet the demands of top employers, enhancing your job prospects.
Interdisciplinary approach that integrates statistics, programming, and AI for a well-rounded understanding of data science principles.
Real-world applications, preparing you to solve complex challenges in various industries.
Master's in Data Science & Gen AI Certification
Master's in Data Science & Gen AI Learning Path/Curriculum
Module 01 - Advanced Excel
Lecture 01: Orientation (Introduction to Data Science, Scope of Data Science)
Lecture 02: Microsoft Excel Overview, Basic Navigation and Usage, Cell referencing, Formatting Excel, Advanced Formatting, Shortcuts and Basic Formulas
Lecture 03: Sorting, Filtering, Advanced Filtering, Charts, Types of Charts, Advanced Charting Techniques and Pivot Tables, Creating, Grouping and Summarizing Data
Lecture 04: Lookup Function, Vlookup, Using VLOOKUP with Multiple, Criteria Hlookup, Combining HLOOKUP with Other Functions, Match Function, Using MATCH for Dynamic Referencing
Lecture 05: 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 06: Understanding the basics of data analysis, Data Import and Cleaning, Using Formulas and Functions, Data Visualization, Descriptive statistics
Lecture 07: 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
Module 02 - Python
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
Module 03 - Statistics
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
Module 04 - Machine Learning
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
Module 05 - Deep Learning
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
Module 06 - R-Programming
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)
Module 07 - SQL
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
Module 08 - Tableau
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
Module 09 - Power BI
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)
Module 10 - Computer Vision
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
Module 11 - NLP
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
Module 12 - RL
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)
Module 13 - Introduction to Gen AI & Huggingface Transformers Platform
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
Module 14 - Language Models and Transformer Models
Lecture 78: Understanding Language Models, Introduction to Transformer Models
Lecture 79: Types of Models, Attention Mechanism, Tasks for Transformer Models
Module 15 - Large Language Models (LLMs)
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
Module 16 - Langchain, AI Application Stack and Ethical Considerations
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
Master's in Data Science & Gen 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
Master's in Data Science & Gen AI Tools Covered
Master's in Data Science & Gen AI Program Benefits
Projects that you will Work On
Machine Learning
No. of Projects:10
Deep Learning
No. of Projects:10
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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
Program Advisors
IITs
IIMs
NITs
Experts from the IT Industries.
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.
Course Fees & Financing
Course Fees
(50% OFF upto 31ˢᵗ March)
(Inclusive Of All Taxes)
Payment Partners
We partnered with financing companies to provide competitive finance option at 0% interest rate with no hidden costs
Upcoming Batches/Program Cohorts
| Batch | Date | Time | Batch Type |
|---|---|---|---|
| Online Live Instructor Led Session | 5th April 2025 | 10:00 AM | Full-Time |
| Online Live Instructor Led Session | 29th March 2025 | 02:00 PM | Part-Time |
Comparison with Others
| Feature | Generative AI Program | Competitor A | Competitor B | Competitor C |
|---|---|---|---|---|
| Tools Covered | Advanced Robotics, Quantum Computing, Python, TensorFlow, and cutting-edge AI tools like GPT-4, StyleGAN, Neural Architecture Search | Primarily includes Robotic Process Automation, Python, and TensorFlow, with limited advanced AI tools | Deep Learning, Basic StyleGAN, Limited Computer Vision | Deep Learning, Computer Vision, Basic Reinforcement Learning |
| AI Focus | Intensive focus on next-gen AI technologies and applications | Basic AI concepts, with limited next-gen focus | Advanced AI techniques, Basic Computer Vision | Applied AI techniques, Advanced Computer Vision |
| Real-World Application | Innovative projects that integrate AI technologies to address cutting-edge industry challenges | Projects often focus on automation with limited AI application | Some real-world projects with a focus on computer vision | Industry-focused projects with advanced AI solutions |
| Expert-Led Instruction | Courses led by top experts in robotics, quantum computing, and AI | Instructors with a focus on automation and basic AI | Industry specialists in deep learning | Experienced AI practitioners and researchers |
| Career Opportunities | Prepares students for cutting-edge roles, including AI Researcher, Robotics Engineer, Quantum Computing Specialist, and Advanced Data Scientist | Typically prepares students for automation and basic data science roles | Provides some career advancement | Focused career paths in advanced AI fields |
| Future-Proof Skills | Cutting-edge AI and robotics skills ensuring adaptability to future technological advancements | Focuses on current technologies, with limited future-proofing | Some focus on emerging AI technologies | Advanced future technologies and AI applications |
| Cross-Industry Relevance | Skills relevant across multiple sectors, including technology, finance, healthcare, and advanced research | Skills are more niche with limited cross-industry application | Some cross-industry applications | Sector-specific expertise |
| Networking Opportunities | Extensive networking with top experts in robotics, AI, and quantum computing | Networking is typically limited to automation professionals | Some networking with AI experts | Limited networking opportunities in advanced AI fields |
| Investment Value | High return on investment due to integration of cutting-edge technologies and industry relevance | Moderate value with a focus on automation and basic data science | Provides good value for advanced AI careers | Basic investment value with a focus on specific sectors |
Self Assessments
Master's in Data Science & Gen AI Training Faqs
The duration of the the Program of Master’s in Data science is is 10-11 months.
Introduction to Data Science, Python, R Programming, Statistics, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Reinforcement Learning, Advanced Excel & Data Analysis, SQL, Tableau, Power BI, Generative AI
While not always required, having relevant work experience can strengthen your application and provide practical insights during your studies.
Interpretation and analysis of data
AI model building and machine learning
SQL, R, and Python programming
Using frameworks and tools like as PyTorch and TensorFlow
Information display
Critical thinking and problem-solving skills.
Senior Data Scientist, AI Research Scientist, Data Science Manager, Research Director.
yes Available in online mode.
Algorithms that can produce new data that is comparable to the data they were trained on are referred to as generative AI. This comprises models that can produce realistic text, music, graphics, and other types of data, such as GANs and transformers.
50+ Projects are done during the course on the topics machine learning, Natural language processing data analysis and generative ai.
duration of each lecture of 3 hours.
INR Fees(Exclusive of GST) : INR 289,999.00
INR Fee(Inclusive of GST) INR 342,199.00
USD Fee: 3475(subject to be changed).
INR Fee(Inclusive of GST) INR 342,199.00
USD Fee: 3475(subject to be changed).
To Lead complex data science projects, advance AI research.
Experienced data professionals, researchers, tech enthusiasts can apply for this course.
A Master’s in Data Science and Generative AI is a program designed to combine sophisticated generative AI techniques with fundamental data science concepts. Along with deep learning, generative adversarial networks (GANs), and natural language processing (NLP), this curriculum usually covers fundamental subjects including statistical analysis, machine learning, data mining, and big data technologies.
Core Curriculum: Statistics, machine learning, deep learning, natural language processing, GANs, and reinforcement learning.
Practical Experience: Capstone projects, internships, and practicums with real-world applications. Seminars, seminars, and lab access are examples of research opportunities.
Technical skill development includes programming (Python, R, SQL) and data engineering abilities.
Ethical and societal impact courses cover AI ethics, prejudice, data privacy, and regulation.
Career support includes job placement aid, resume seminars, and an alumni network.
Interdisciplinary Approach: Integration of areas such as business, healthcare, and social sciences.
Practical Experience: Capstone projects, internships, and practicums with real-world applications. Seminars, seminars, and lab access are examples of research opportunities.
Technical skill development includes programming (Python, R, SQL) and data engineering abilities.
Ethical and societal impact courses cover AI ethics, prejudice, data privacy, and regulation.
Career support includes job placement aid, resume seminars, and an alumni network.
Interdisciplinary Approach: Integration of areas such as business, healthcare, and social sciences.
1, we will optimise linked in profile and the algorithm of linkedin profile
2, We will conduct GITHUB and Kaggle sessions
3, We will do multiple Hackathons and guide you in problem solving skills for the interview process
4, We will ensure peer learning session are being conducted
5, We will issue mini certification for every tools
6, We will asign you a personal mentor on pre booking i’t a one one session.
2, We will conduct GITHUB and Kaggle sessions
3, We will do multiple Hackathons and guide you in problem solving skills for the interview process
4, We will ensure peer learning session are being conducted
5, We will issue mini certification for every tools
6, We will asign you a personal mentor on pre booking i’t a one one session.
