Don't Watch the AI Revolution-
Be a Part of It.
Data Science & GenAI Experiential Learning Program
Get IT Job-Ready
in Just 14 Weeks!
Essential Takeaways from the Program
Program Snapshot
140+
Hours of Learning
6+
Industry Projects
1:1
Expert Mentorship
Future-Ready Skills for a Tech-Driven World
Program Description
The AI revolution is here, and Data Science & GenAI are at the forefront of innovation.
Our 3-month Experiential Learning Cohort is designed to equip you with the skills needed to build AI-powered automation, machine learning models, and intelligent systems—all through real-world projects and hands-on learning.
Gain hands-on experience by working on cutting-edge AI projects
Exciting AI Projects To Work On
01.
AI Chatbots
Build ChatGPT-like models for private knowledge bases.
02.
AI-Based LMS
Develop adaptive learning management systems.
03.
Virtual Telecaller
Design AI-powered communication agents.
04.
AI-Based
HR Assist
Create intelligent tools for
HR automation.
HR automation.
05.
AI-Based Question Bank Generator
Automate customized question bank creation for
exams.
06.
Fintech
Products
Explore AI-driven financial technology solutions.
Cutting-Edge Curriculum
Stay ahead with a future-ready curriculum designed by AI & Data Science pioneers, now integrated with advanced Generative AI applications.
Industry simulated learning by AI experts from the industry
Curriculum Overview
Software Engineering Basics (2 hours per day)
Topics Covered
- Introduction to Software Engineering in Data Science
- Software Design and Testing
- Data Structures and Algorithms for Data Science
- Introduction to Databases and SQL
- Software Development Practices for Data Science Projects
Python Programming for Data Science (3 days a week) & Math and Statistics (2 days a week)
Python Programming for Data Science (Weeks 2-6)
Topics Covered
- Basic Python Programming
○ Variables, Data Types, Lists, Tuples, Dictionaries, Sets, and Functions
○ Flow Control, Loops, and Conditionals
○ Introduction to libraries: NumPy and Pandas
- Intermediate Python Programming
○ Object-Oriented Programming (OOP) in Python
○ Handling Errors (Try-Except Blocks), File Handling, and Data I/O
○ Handling Errors (Try-Except Blocks), File Handling, and Data I/O
- Advanced Python for Data Science
○ Advanced Data Structures and Algorithms
○ Data Manipulation with Pandas and visualization with Matplotlib/Seaborn
○ Data Manipulation with Pandas and visualization with Matplotlib/Seaborn
- Data Science Libraries and APIs
○ Advanced Pandas, NumPy for data manipulation
○ Web Scraping with BeautifulSoup and APIs (requests)
○ Web Scraping with BeautifulSoup and APIs (requests)
- Python for Machine Learning
○ Introduction to scikit-learn for ML models
○ Basic ML workflows (train-test split, cross-validation, pipelines)
○ Basic ML workflows (train-test split, cross-validation, pipelines)
Math and Statistics (Weeks 2-6)
Topics Covered
- Basic Math and Stats
○ Basic Calculus, Linear Algebra, and Vectors
○ Probability Theory, Distributions (Normal, Binomial, etc.), Descriptive Statistics
○ Probability Theory, Distributions (Normal, Binomial, etc.), Descriptive Statistics
- Probability and Inferential Statistics
○ Probability Distributions (Gaussian, Poisson, etc.), Central Limit Theorem
○ Hypothesis Testing, p-values, Confidence Intervals
○ Hypothesis Testing, p-values, Confidence Intervals
- Data Analysis and Regression
○ Simple and Multiple Linear Regression
○ Correlation, Covariance, and Statistical Significance
○ Correlation, Covariance, and Statistical Significance
- Advanced Statistical Methods
○ ANOVA, Chi-square Tests, and Bayesian Statistics
○ Time Series Analysis
○ Time Series Analysis
- Applied Statistics
○ A/B Testing, Experimental Design
○ Monte Carlo Simulations and Markov Chains
○ Monte Carlo Simulations and Markov Chains
Week 7: Introduction to EDA and Data Cleaning
Topics Covered
○ Data Cleaning Techniques (Missing values, outliers, duplicates)
○ Data Transformation (Scaling, Encoding, Imputation)
○ Feature Engineering and Selection
○ Data Wrangling with Pandas and Visualization with Matplotlib, Seaborn
○ Hands-on Project: Cleaning and Preprocessing a Sample Dataset
○ Data Transformation (Scaling, Encoding, Imputation)
○ Feature Engineering and Selection
○ Data Wrangling with Pandas and Visualization with Matplotlib, Seaborn
○ Hands-on Project: Cleaning and Preprocessing a Sample Dataset
Week 8: Advanced EDA Techniques
Topics Covered
○ Advanced Data Visualization (Boxplots, Heatmaps, Pairplots, etc.)
○ Feature Importance and Dimensionality Reduction (PCA)
○ Data Analysis in Action: Exploring Real-World Data (Hands-on Project)
○ Using Seaborn and Plotly for interactive visualizations
○ Presentation of EDA Findings
○ Feature Importance and Dimensionality Reduction (PCA)
○ Data Analysis in Action: Exploring Real-World Data (Hands-on Project)
○ Using Seaborn and Plotly for interactive visualizations
○ Presentation of EDA Findings
Week 9: Introduction to Machine Learning
Topics Covered
○ Supervised Learning Basics: Linear Regression, Logistic Regression
○ Model Evaluation Techniques (Accuracy, Precision, Recall, F1 Score, ROC-AUC)
Week 10: Classification Algorithms
Topics Covered
○ Decision Trees, Random Forests, KNN, SVM
○ Tuning Hyperparameters, Grid Search, and Random Search
○ Tuning Hyperparameters, Grid Search, and Random Search
Week 11: Unsupervised Learning
Topics Covered
○ Clustering Algorithms (K-Means, DBSCAN, Hierarchical)
○ Dimensionality Reduction (PCA, t-SNE)
○ Dimensionality Reduction (PCA, t-SNE)
Week 12: Advanced Machine Learning Techniques
Topics Covered
○ Ensemble Learning: Boosting, Bagging, and XGBoost
○ Model Deployment and Monitoring using Flask/Django and Cloud Platforms (AWS, GCP)
○ Model Deployment and Monitoring using Flask/Django and Cloud Platforms (AWS, GCP)
Week 13: Introduction to Neural Networks
Topics Covered
○ Basics of Neural Networks: Perceptrons, Activation Functions, and
Backpropagation
○ Building Neural Networks with TensorFlow or PyTorch
○ Building Neural Networks with TensorFlow or PyTorch
Week 14: Convolutional Neural Networks (CNNs)
Topics Covered
○ Introduction to CNNs for Image Data (Conv Layers, Pooling, and Fully Connected
Layers)
○ Hands-on: Building a CNN for Image Classification
○ Hands-on: Building a CNN for Image Classification
Week 15: Recurrent Neural Networks (RNNs) and Transformers
Topics Covered
○ Introduction to RNNs, LSTMs, GRUs for Sequence Data
○ Using Transformers (BERT, GPT models) for Natural Language Processing (NLP)
○ Using Transformers (BERT, GPT models) for Natural Language Processing (NLP)
2 Months Capstone Project
Apply your AI & Data Science skills to build and deploy
a real-world project
>
Phase 1
Data Collection, EDA, Feature Engineering, and
Model Selection
>
Phase 2
Model Optimization, Deep Learning (if applicable), and Deployment
Sam Altman, OpenAI CEO
OpenAI has started rolling out AI agents, which he says will eventually become virtual co-workers of humans.
Gain hands-on experience by working on cutting-edge AI projects
Why Join This Program?
01
Learn While You Work
Gain practical experience by working on real-time projects that challenge your analytical and problem-solving skills.
02
Learn Under Expert Guidance
Be mentored by a Senior Data Scientist and Serial Entrepreneur with deep expertise in AI
solutions for global enterprises and governments.
03
Practical-Driven Training
Our program focuses entirely on real-world applications, ensuring you develop the expertise
required to excel in AI and GenAI fields.
04
Learn GenAI Tools for Coding & Automation
Gain hands-on expertise in cutting-edge AI-driven tools that streamline coding and automation
processes.
05
Exclusive Placement Connect
Exclusive corporate alliance teams work to place you in top firms, ensuring you land the right
opportunity in the AI and GenAI industry.
06
Commitment to Growth
A 3 month commitment is required to fully benefit from this program. Your dedication will set you
up for long-term success!
Your Journey Starts Here
Who Should Join?
>
Engineering and
Science Graduates
>
Developers looking to upskill with Data Science & GEN AI skills
The Future is for AI Developers
Program Details
01.
Duration
3 Months
Intensive & Hands-On Learning
02.
Mode of Learning
In-person,
Immersive Training
At Our Campus
03.
Batch Size