The Professional Program in Data Science And Generative AI is an advanced course designed to help learners understand, build, and deploy next-generation AI models that can create text, images, and solutions autonomously.
This program dives deep into the technologies powering Generative AI — including Large Language Models (LLMs), Diffusion Models, Transformers, and Prompt Engineering. You’ll gain practical experience with cutting-edge frameworks such as OpenAI GPT, Hugging Face, LangChain, and Stability AI, and learn how to integrate them into real-world applications.
By the end of this course, you’ll be able to design intelligent systems, design intelligent systems, fine-tune AI models for domain-specific tasks, and develop innovative products using generative AI. This program is ideal for professionals aiming to stay ahead in the rapidly evolving field of Artificial Intelligence and Automation.
- Must hold a graduate degree in any discipline.
- Basic computer literacy and basic proficiency in English are required.
- No prior experience in data analytics or programming is necessary.
- A laptop or desktop with a stable internet connection.
- Enthusiasm to learn data analytics and AI applications through guided projects.
- Familiarity with Excel or basic statistics is an added advantage(Not Mandatory).
- Commitment to complete all live sessions, assignments, and real-time projects.
- Students and Fresh Graduates aspiring to enter the data analytics or business intelligence domain.
- Working professionals seeking to upgrade their skills for career advancement in data domain.
- Entrepreneurs and decision-makers looking to use AI-powered insights to drive business growth.
Whether you’re a beginner or looking for career transition or domain change, this course ensures you gain both technical expertise and strategic understanding to thrive in the field of data analytics and AI.
What You’ll Get?
Attend interactive live classes led by industry experts and never miss a session with access to high-quality recordings. Learn at your own pace while revisiting key concepts anytime, ensuring a complete and flexible learning experience.
Work on real-world projects under the mentorship of experienced professionals. Learn to implement Agile methodology, collaborate in sprints, and apply practical solutions — building skills that employers value and boosting your confidence to handle real industry challenges.
Our dedicated placement support ensures you never stop until you secure your dream job. From interview prep to company shortlisting, we guide you every step of the way, giving you the tools and confidence to succeed in the competitive job market.
Craft a professional, standout resume with personalized guidance from experts. Learn how to highlight your skills, projects, and achievements effectively — increasing your chances of catching recruiters’ attention without much stress and landing interviews.
Practice with mock interviews designed to simulate real industry scenarios. Receive constructive feedback and gain confidence, then get opportunities to attend live interviews with top companies — ensuring you’re fully prepared to excel.
Get unlimited access to all course materials, including notes, recordings, and resources. Revisit lessons anytime, stay updated with new content, and continue learning even after completing your course.
Join a vibrant community of learners, mentors, and industry experts. Collaborate, share knowledge, seek guidance, and grow together — because learning is better when it’s supported by a strong, like-minded network.
Absolutely! Our tech programs for beginners are designed for learners from all backgrounds — even if you have zero technical experience. We start with the fundamentals and gradually build your skills through hands-on projects and mentorship. Many of our successful learners come from non-technical fields like commerce, arts, and management. By the end of the course, you’ll gain industry-ready skills and a job-ready certification that opens doors to top tech roles.
Yes, definitely! A career gap doesn’t define your future — your skills do. Our training is 100% industry-aligned, helping you gain the latest tools, technologies, and certifications that companies are actively seeking. You’ll also receive dedicated career support — resume building, mock interviews, and placement guidance — to confidently re-enter the workforce with a strong profile and competitive salary.This ensures you can compete for high-paying tech jobs in India and globally, despite a career break.
We believe in complete transparency and learner satisfaction. If you’re not satisfied with the course within the first 7 days of enrollment, you can request a full refund — no questions asked. Beyond that, you can switch to another job-ready course or future batch that better suits your learning goals.
Our learners typically start with packages ranging from ₹4 LPA to ₹8 LPA, depending on the course and skill level. Advanced tracks like AI, Data Analytics, and Cloud Computing can lead to even higher salaries. Completing a job-ready certification from our program enhances your profile and helps you secure high-paying tech jobs in India.
Yes! Once you enroll, you get lifetime access to course materials and session replays. You can revisit the sessions, refresh your skills, and attend future batches at no extra cost. Continuous learning ensures you stay ahead in your career and fully leverage your career gap courses to re-enter the tech industry confidently.
Step into the future of Artificial Intelligence with our comprehensive Generative AI program designed for innovators and professionals alike. This course takes you from the fundamentals of AI creativity to advanced generative model techniques, empowering you to build intelligent systems that can create text, images, and solutions autonomously.
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Module 1: Python
Dive into the world of Python, the most versatile and beginner-friendly programming language. Perfect for beginners and non-tech professionals aiming to build a strong foundation.
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Lecture 1.1: Introduction to Python and Its Advantages
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Lecture 1.2: Introduction To IDEs
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Lecture 1.3: Why Python ?
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Lecture 1.4: Variable And Its Rules
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Lecture 1.5: Operators
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Lecture 1.6: Data Types
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Lecture 1.7: Conditional Statements and Loops
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Lecture 1.8: Functions And Parameters
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Lecture 1.9: Exception Handling and File Handling
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Lecture 1.10: Regular Expressions
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Lecture 1.11: Numpy
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Lecture 1.12: Pandas
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Lecture 1.13: Matplotlib
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Lecture 1.14: Seaborn
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Module 2: Statistics
Master the fundamentals of statistics to analyze and interpret data effectively. Learn essential concepts and techniques that form the backbone of data-driven decision-making.
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Lecture 2.1: Introduction To Statistics
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Lecture 2.2: Data Collection, Data Preparation and Inference
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Lecture 2.3: Sampling, Types Of Sampling and Variance in sample
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Lecture 2.4: Data Types
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Lecture 2.5: Descriptive Statistics
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Lecture 2.6: Data Distributions
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Lecture 2.7: Probability Theory
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Lecture 2.8: Central Limit Theorem
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Lecture 2.9: Confidence Interval
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Lecture 2.10: Hypothesis Testing
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Lecture 2.11: Correlation and Covariance
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Lecture 2.12: Data Preparation
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Module 3: Stuctured Query Language(SQL)
Master the essentials of SQL to efficiently manage and query databases. Learn to extract, analyze, and manipulate data to make informed, data-driven decisions.
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Lecture 3.1: Introduction to Database
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Lecture 3.2: Understanding The Architecture of the Database
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Lecture 3.3: Data Types In SQL
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Lecture 3.4: Command Types in SQL
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Lecture 3.5: Operators in SQL
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Lecture 3.6: Constraints in SQL
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Lecture 3.7: Joins
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Lecture 3.8: Sub Queries
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Lecture 3.9: Index, Views and Union
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Lecture 3.10: Common Table Expression
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Lecture 3.11: Window Functions
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Lecture 3.12: Triggers
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Module 4: Power BI
Master Power BI to transform raw data into interactive dashboards and insightful visualizations. Gain hands-on experience building real-world reports that drive smart business decisions.
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Lecture 4.1: Prepare The Data
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Lecture 4.2: Get Or Connect To Data
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Lecture 4.3: Profile and Clean The Data
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Lecture 4.4: Transform and Load The Data
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Lecture 4.5: Model The Data
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Lecture 4.6: Design And Implement The Data Model
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Lecture 4.7: Create Model Calculation Using Data Model
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Lecture 4.8: Optimize Model Performance
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Lecture 4.9: Visualize And Analyze The Data
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Lecture 4.10: Enhance Reports For Usability And Storytelling
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Lecture 4.11: Identify Patterns And Trends
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Lecture 4.12: Manage And Secure Power BI
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Lecture 4.13: Create And Manage Workspaces and Assets
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Lecture 4.14: Secure and Govern Power BI Items
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Module 5: Machine Learning And Deep Learning
Learn the mathematical foundations, supervised and unsupervised algorithms, natural language processing, time series forecasting, and deep learning architectures — from basics to advanced neural networks — through hands-on projects and real-world case studies.
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Lecture 5.1:Mathematical Foundations
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Lecture 5.2: Hierarchical Clustering
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Lecture 5.3:k-Means Clustering
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Lecture 5.4:Dimension Reduction Technique - PCA, SVD
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Lecture 5.5:Association Rules
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Lecture 5.6:Recommendation Engine
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Lecture 5.7:Network Analytics
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Lecture 5.8:Linear Regression
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Lecture 5.9:Logistic Regression
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Lecture 5.10:Lasso And Ridge Regression
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Lecture 5.11:K-NN Classification
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Lecture 5.12:Naïve Bayes
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Lecture 5.13:Ensemble Techniques
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Lecture 5.14:Support Vector Machine
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Lecture 5.15:Basics Of NLP
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Lecture 5.16:Text Classification
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Lecture 5.17:Sentiment Analysis
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Lecture 5.18:Named Entity Recognition
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Lecture 5.19:Text Summarization - Basics
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Lecture 5.20:Topic Modeling
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Lecture 5.21:Survival analytics
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Lecture 5.22:Time Series Analysis - Forecasting
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Lecture 5.23:Basics Of Nueral Network
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Lecture 5.24:Artificial Nueral Network
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Lecture 5.25:Convolutional Nueral Network
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Lecture 5.26:Recurrent Nueral Network
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Lecture 5.27:Hyper Parameter Tuning in Neural Network
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Module 6:Artificial Intelligence
Master the foundations of Generative AI by exploring large language models, diffusion models, prompt engineering, and fine-tuning techniques. Learn how to build AI systems capable of generating text, images, and insights — all through hands-on projects and real-world applications.
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Lecture 6.1:Anatomy Of Neural Network
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Lecture 6.2:Convolutional Neural Network
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Lecture 6.3:Region Based Convolution Nueral Network - RCNN
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Lecture 6.4:Fast RCNN, Faster-RCNN, Mask-RCNN, Cascade RCNN
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Lecture 6.5:Recurrent Nueral Network - Regression, Classification, Sentiment Analysis, Next Word Prediction
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Lecture 6.6:Gated Recurrent Unit
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Lecture 6.7:Long and Short Term Memory - LSTM
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Lecture 6.8:Overview Of Bi-Directional LSTM
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Lecture 6.9:Computer Vision and Image Processing
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Lecture 6.10:Image Segmentation - Semantic, Instance, Panopic
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Lecture 6.11:Sequence To Sequence Models
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Lecture 6.12:Transformers
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Lecture 6.13:Auto Encoders and Variational Auto Encoders
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Lecture 6.14:Generative Adversarial Networks & Its Varieties
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Lecture 6.15:Speech Recognition
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Lecture 6.16:Reinforcement Learning
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Module 7: Tableau
Master the art of visual storytelling with Tableau and turn complex data into insightful, interactive dashboards.
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Lecture 7.1: Introduction to Tableau
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Lecture 7.2: Tableau Workflow
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Lecture 7.3: Connection and Data Source Setup
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Lecture 7.4: Understand Changes to Data
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Lecture 7.5: Data Simplification and Sorting
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Lecture 7.6: Data Organization
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Lecture 7.7: Specific Values
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Lecture 7.8: Generate Grand Totals, Subtotals, and Changing Aggregation
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Lecture 7.9: Build Highlight Tables
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Lecture 7.10: Data by Date
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Lecture 7.11: Multiple Measures in a View
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Lecture 7.12: Measure Values and Names
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Lecture 7.13: Relationships Between Numerical Values
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Lecture 7.14: Geographic Data Mapping
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Lecture 7.15: Data Customization
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Lecture 7.16: Quick Table Calculations
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Lecture 7.17: View Sharing
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Module 8: Advanced Excel
Unlock the full potential of Excel with advanced formulas, pivot tables, and automation techniques.
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Lecture 8.1: Introduction to Excel & Interface Overview
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Lecture 8.2: Data Entry, Formatting, and Validation
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Lecture 8.3: Formulas & Basic Functions (SUM, AVERAGE, COUNT, etc.)
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Lecture 8.4: Advanced Functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
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Lecture 8.5: Logical Functions (IF, AND, OR, Nested IF)
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Lecture 8.6: Text Functions (LEFT, RIGHT, MID, CONCATENATE, TEXT)
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Lecture 8.7: Date & Time Functions (TODAY, NOW, DATE, YEAR, MONTH)
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Lecture 8.8: Data Sorting, Filtering & Custom Views
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Lecture 8.9: Conditional Formatting & Highlighting Data
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Lecture 8.10: Data Validation & Drop-Down Lists
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Lecture 8.11: Pivot Tables & Pivot Charts
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Lecture 8.12: Charts & Visualizations (Column, Line, Pie, Combo)
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Lecture 8.13: Advanced Data Analysis with Power Query
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Lecture 8.14: Advanced Data Analysis with Power Pivot & DAX
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Lecture 8.15: Scenario Analysis, Goal Seek, and What-If Analysis
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Lecture 8.16: Macros & Introduction to VBA for Automation
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Lecture 8.17: Best Practices in Excel for Data Cleaning and Reporting
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Module 9: PySpark
Learn to process massive datasets efficiently using PySpark and unlock the power of distributed computing
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Lecture 9.1: Introduction to PySpark & Spark Ecosystem Overview
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Lecture 9.2: RDDs (Resilient Distributed Datasets) Basics and Operations
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Lecture 9.3: DataFrames & Spark SQL Fundamentals
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Lecture 9.4: Transformations & Actions in PySpark
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Lecture 9.5: Handling Structured & Semi-Structured Data (CSV, JSON, Parquet)
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Lecture 9.6: PySpark Functions (UDFs, Built-in Functions, Aggregations)
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Lecture 9.7: Data Cleaning & Preprocessing with PySpark
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Lecture 9.8: Joins, Unions, and Merging DataFrames
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Lecture 9.9: GroupBy, Aggregations, and Window Functions
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Lecture 9.10: Working with Spark Streaming & Real-Time Data
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Lecture 9.11: Machine Learning with PySpark MLlib
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Lecture 9.12: Performance Tuning & Optimization (Caching, Partitioning)
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Lecture 9.13: PySpark GraphFrames for Graph Analytics
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Lecture 9.14: Best Practices for Big Data Processing in PySpark
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Module 10:Machine Learning in Cloud
Learn how to build, train, and deploy machine learning models on leading cloud platforms such as AWS, Azure, and Google Cloud. This module covers cloud-based ML workflows, scalable model training, MLOps practices, and automation techniques that help you deliver intelligent solutions efficiently in real-world environments.
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Lecture 10.1:Introduction to Cloud Computing and ML-as-a-Service
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Lecture 10.2:Overview of AWS, Azure, and GCP for Machine Learning
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Lecture 10.3:Cloud Storage and Data Management for ML (S3, GCS, Blob Storage)
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Lecture 10.4:Cloud Databases and Warehouses for ML (RDS, BigQuery, Redshift, Synapse)
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Lecture 10.5:Cloud-Based Machine Learning Platforms (SageMaker, Vertex AI, Azure ML Studio)
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Lecture 10.6:Building, Training, and Deploying ML Models in the Cloud
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Lecture 10.7:AutoML Services in Cloud (AWS AutoPilot, Vertex AutoML, Azure AutoML)
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Lecture 10.8:Serverless Machine Learning (AWS Lambda, Cloud Functions)
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Lecture 10.9:MLOps in Cloud (CI/CD, Model Monitoring, Retraining Pipelines)
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Lecture 10.10:Distributed Training and GPU/TPU Integration
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Lecture 10.11:Model Versioning and Experiment Tracking in Cloud ML Workflows
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Lecture 10.12:Real-time ML Inference and API Deployment
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Lecture 10.13:Best Practices and Case Studies in Cloud-based Machine Learning
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Module 11: Prompt Engieering
Master the art and science of prompt engineering to effectively communicate with large language models like GPT, Claude, and Gemini. Learn how to design precise, structured, and optimized prompts that enhance model accuracy, creativity, and reliability for diverse AI applications such as chatbots, automation, and content generation.
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Lecture 11.1: Introduction to Prompt Engineering and its Role in Data Analytics
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Lecture 11.2: Understanding Large Language Models (LLMs) like GPT and Gemini
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Lecture 11.3: Types of Prompts – Zero-Shot, One-Shot, and Few-Shot Learning
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Lecture 11.4: Crafting Effective Prompts for Data Exploration and Insights
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Lecture 11.5: Techniques to Improve Model Responses: Role, Context, and Constraints
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Lecture 11.6: Chain-of-Thought and Step-by-Step Reasoning Prompts
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Lecture 11.7: Prompt Templates for Data Cleaning, Summarization, and Insights Extraction
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Lecture 11.8: Prompt Engineering for SQL Query Generation and Automation
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Lecture 11.9: Evaluating and Refining Prompts for Accuracy and Relevance
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Lecture 11.10: Integrating Prompt Engineering with Big Data Tools and Workflows
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Lecture 11.11: Ethical Considerations and Bias Awareness in Prompt Design
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Lecture 11.12: Hands-on Practice: Building Custom Prompts for Analytical Tasks
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Lecture :Value Added Sessions
At our institute, we believe true learning goes beyond the classroom. Our Value Added Sessions are designed to help students understand how real-world systems and technologies work together. These sessions run parallel to the main course, bridging the gap between theory and industry application.
Through interactive workshops, live demos, and hands-on exercises, learners gain practical exposure to emerging tools, frameworks, and workflows used by professionals. Whether it’s cloud integration, AI model deployment, or end-to-end project execution, these sessions ensure our students develop a complete and job-ready skill set.
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Module 1: Flask API, FAST API
Learn how to build and deploy lightweight, scalable web APIs using Flask. Understand API integration, request handling, and connecting machine learning models to real-world applications. Explore asynchronous programming, automatic documentation, and rapid model deployment techniques.
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Module 2:Docker Basics
Get hands-on with Docker to containerize applications for seamless deployment. Learn to build, manage, and run containers that make your projects portable and production-ready.
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Module 3:Kubernets Basics
Understand the fundamentals of Kubernetes for container orchestration. Learn to automate deployment, scaling, and management of applications in a cloud-native environment.
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Module 4:HuggingFace
Discover how to use Hugging Face for state-of-the-art NLP and GenAI models. Learn to fine-tune transformers and integrate pre-trained models into your projects.
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Module 5:Web Scrapping Tools
Explore Scrapy for large-scale, automated data extraction from websites. Learn advanced crawling, data pipelines, and handling dynamic content efficiently.
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Module 6:OpenAI API
Unlock the potential of Generative AI with the OpenAI API. Learn how to build intelligent applications using GPT models for chatbots, content creation, and automation.
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Module 7:MLOps Fundamentals
Gain foundational knowledge in DevOps to streamline development and operations. Understand CI/CD pipelines, version control, and automated deployment practices.
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Module 8:Advancements in Gen AI
Stay ahead with insights into the latest breakthroughs in Generative AI. Learn about multimodal models, AI agents, and real-world GenAI applications shaping the future.
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Module 9:Teaching Machines To Paint, Sing, Generate Text
Explore the fascinating world of Generative AI where machines create art, compose music, and write human-like text. Understand how models like GANs, VAEs, and diffusion networks enable creativity through algorithms, and gain hands-on experience building AI systems that can paint, sing, and generate content across multiple media formats.
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Module 10:Introduction To Quantum Computing
Explore how quantum computing intersects with machine learning. Understand quantum algorithms, qubits, and how they revolutionize computational speed and AI problem-solving.
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Module 11:Freelancing in AI Feild
Learn how to build a successful freelancing career in the rapidly growing field of Artificial Intelligence. This module covers everything from identifying high-demand AI skills and creating a strong professional portfolio to finding projects on top freelancing platforms. Gain insights into client communication, project delivery, pricing strategies, and how to position yourself as a trusted AI expert in the global market.
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Module 12: Capstone Project
Apply your learning in a real-world project to solve business challenges using analytics and AI tools. Showcase your skills to potential employers with a fully functional project.
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Module 13: Live Project
Work on live datasets under expert guidance to gain industry-ready experience. Build confidence by implementing end-to-end solutions that mirror real business scenarios.
Hi! I’m Indira Kumar, a Data Scientist at EuclideanLab with a strong focus on building intelligent systems that create, innovate, and transform industries. I help learners understand how Large Language Models, Diffusion Models, and Prompt Engineering work behind the scenes. My sessions combine creativity with technical depth, guiding students to design AI solutions that generate text, images, and insights — shaping the future of intelligent automation.
The course content is extremely well-structured and practical. I went from having zero knowledge to confidently working with real-world datasets. The mentors are super supportive and explain every concept clearly!
I loved how interactive and hands-on the learning experience was! The live projects and case studies really helped me understand how data analytics works in the industry. Highly recommend it to anyone starting out in data analytics!
The trainers have deep industry knowledge and make even complex topics feel simple. The course gave me confidence to apply for data analyst roles and even build my own mini projects. Truly worth every minute!