PG Master Diploma in Data Science & Generative AI Full Stack

Topics:

• Introduction to Python
• Writing clean, readable code
• Variables & data types (int, float, string, boolean)
• Data structures: lists, tuples, sets, dictionaries
• Conditional statements (if, elif, else)
• Loops (for, while)
• Functions & modules
• File handling (CSV, TXT, JSON)
• Exception handling

Topics:

• Numerical computing using NumPy
• Arrays vs lists
• Array operations & broadcasting
• Mathematical functions
• Indexing & slicing
• Performance optimization

Topics:

• Data manipulation using Pandas
• Series & DataFrames
• Data loading (CSV, Excel, SQL)
• Data cleaning (missing values, duplicates)
• Filtering & grouping
• Aggregations & joins

Topics:

• Visualization techniques
• Charts using Matplotlib
• Charts using Seaborn
• Bar, line, histogram, scatter plots
• Correlation heatmaps
• Storytelling with data

Topics:

• Understanding datasets
• Statistical summaries
• Correlation analysis
• Outlier detection
• Feature relationships

Topics:

• Introduction to ML
• Supervised learning: Regression
• Supervised learning: Classification
• Unsupervised learning: Clustering
• Model evaluation (accuracy, precision, recall)
• Tools: Scikit-learn

Topics:

• Improving model performance
• Feature selection
• Encoding categorical variables
• Scaling data
• Cross-validation
• Hyperparameter tuning

Topics:

• Data extraction
• SQL basics
• Connecting Python with databases
• Query execution

Topics:

• End-to-end lifecycle
• Problem definition
• Data collection
• Cleaning & analysis
• Model building
• Deployment basics

Topics:

• Sharing models
• APIs using Flask or FastAPI
• Model serialization (pickle)
• Simple deployment

Project Focus:

• Data cleaning + visualization

Project Focus:

• ML model + evaluation

Project Focus:

• Insights + visualization

Project Focus:

• Data → EDA → ML → Deployment

Scenarios:

• Cleaning messy business data
• Predicting customer behavior
• Analyzing sales trends
• Building dashboards for decision-making

Skills Gained:

• Python programming
• Data analysis & visualization
• Machine learning basics
• Real-world data problem solving

Topics:

• What is Data Science
• Data Science lifecycle
• Business problem framing
• CRISP-DM methodology
• Types of analytics (descriptive, predictive, prescriptive)
• Data-driven decision making
• Industry use cases (finance, retail, healthcare)

Topics:

• Programming using Python
• Data structures (list, dict, set)
• Functions & modules
• File handling
• Basic scripting for data

Topics:

• Data acquisition & cleaning
• Working with Pandas
• Working with NumPy
• Handling missing values
• Removing duplicates
• Data transformation
• Feature creation

Topics:

• Understanding data patterns
• Statistical summaries
• Correlation analysis
• Outlier detection
• Trend analysis
• Tools: Matplotlib, Seaborn

Topics:

• Communicating insights
• Charts (bar, line, scatter, heatmaps)
• Dashboard creation
• Data storytelling techniques

Topics:

• Introduction to ML
• Supervised learning: Regression
• Supervised learning: Classification
• Unsupervised learning: Clustering
• Model evaluation metrics
• Tools: Scikit-learn

Topics:

• Model improvement
• Feature engineering
• Feature selection
• Hyperparameter tuning
• Ensemble methods (Random Forest, Boosting)

Topics:

• Data extraction
• SQL queries (SELECT, JOIN, GROUP BY)
• Connecting Python to database
• Data pipelines

Topics:

• Forecasting techniques
• Trend & seasonality
• Moving averages
• ARIMA basics

Topics:

• Neural networks
• ANN basics
• CNN overview
• RNN basics
• Tools: TensorFlow / PyTorch

Topics:

• Model deployment
• APIs using Flask or FastAPI
• Model serialization
• Basic cloud deployment

Topics:

• End-to-end workflow
• Problem definition
• Data collection
• Modeling
• Deployment
• Monitoring

Project Focus:

• Data cleaning + visualization

Project Focus:

• ML model + evaluation

Project Focus:

• Product/user recommendation

Project Focus:

• Data → EDA → ML → Deployment

Scenarios:

• Customer behavior prediction
• Fraud detection
• Sales forecasting
• Marketing campaign analysis
• Inventory optimization

Skills Gained:

• Data analysis & visualization
• Machine learning
• Business problem solving
• Model deployment

Topics:

• What is Machine Learning
• Types of ML (Supervised, Unsupervised, Semi-supervised)
• Business problem → ML problem mapping
• Regression vs Classification vs Clustering
• ML pipeline overview
• Bias vs variance concept

Topics:

• Preparing data for ML
• Handling missing values
• Encoding categorical variables
• Feature scaling (Normalization, Standardization)
• Outlier detection
• Feature creation & transformation
• Tools: Pandas, NumPy

Topics:

• Regression algorithms
• Linear Regression
• Polynomial Regression
• Regularization (Ridge, Lasso)
• Tools: Scikit-learn

Topics:

• Classification models
• Logistic Regression
• Decision Trees
• Random Forest
• K-Nearest Neighbors
• Evaluation: Accuracy, Precision, Recall, F1 Score
• Confusion matrix

Topics:

• Clustering & pattern detection
• K-Means clustering
• Hierarchical clustering
• Dimensionality reduction (PCA)

Topics:

• Improving model performance
• Cross-validation
• Hyperparameter tuning (Grid Search, Random Search)
• Overfitting vs underfitting

Topics:

• Ensemble methods
• Bagging
• Boosting (AdaBoost, Gradient Boosting)
• XGBoost basics

Topics:

• Forecasting models
• Trend & seasonality
• Moving averages
• ARIMA basics

Topics:

• Neural networks
• ANN basics
• Activation functions
• Backpropagation
• Tools: TensorFlow / PyTorch

Topics:

• Deploying ML models
• APIs using Flask or FastAPI
• Model serialization (pickle)
• Basic cloud deployment

Topics:

• ML lifecycle
• Data versioning
• Model monitoring
• CI/CD basics

Project Focus:

• Classification model

Project Focus:

• Regression model

Project Focus:

• Clustering

Project Focus:

• Advanced ML

Project Focus:

• Data → Model → Deployment

Scenarios:

• Predict customer behavior
• Detect fraud transactions
• Forecast sales demand
• Segment customers for marketing
• Optimize pricing strategy

Skills Gained:

• ML algorithms
• Data preprocessing
• Model optimization
• Deployment

Topics:

• Introduction to AI & Deep Learning
• Neural Network fundamentals
• What is AI vs ML vs Deep Learning
• Artificial Neural Networks (ANN)
• Perceptron & multi-layer networks
• Activation functions (ReLU, Sigmoid, Softmax)
• Loss functions & optimization
• Backpropagation
• Tools: TensorFlow, PyTorch

Topics:

• Deep learning for images
• CNN architecture
• Convolution, pooling layers
• Feature maps
• Image preprocessing
• Transfer learning (ResNet, VGG basics)

Topics:

• Image processing & real-world CV
• OpenCV basics
• Image transformations
• Object detection basics (YOLO overview)
• Face detection
• Image segmentation basics

Topics:

• Sequential data modeling
• RNN architecture
• Vanishing gradient problem
• LSTM & GRU
• Time-series modeling

Topics:

• Text data processing
• Text preprocessing (tokenization, stemming, lemmatization)
• Bag of Words, TF-IDF
• Word embeddings
• Sentiment analysis

Topics:

• Modern NLP techniques
• Attention mechanism
• Transformer architecture
• Pre-trained models (BERT basics)
• Tools: Hugging Face Transformers

Topics:

• Improving deep learning models
• Overfitting & regularization
• Dropout & batch normalization
• Hyperparameter tuning
• Model evaluation metrics

Topics:

• Deploying AI models
• APIs using FastAPI
• Model serving
• Integration with applications

Project Focus:

• CNN-based model

Project Focus:

• Computer Vision + OpenCV

Project Focus:

• NLP-based model

Project Focus:

• RNN/LSTM model

Project Focus:

• Combine CV or NLP + Deployment

Scenarios:

• Detect fraud using behavioral patterns
• Analyze customer reviews
• Build chatbot for support
• Identify objects in images
• Forecast sales or stock prices

Skills Gained:

• Deep learning
• Computer vision
• NLP
• Model deployment

Topics:

• Introduction to Generative AI
• Evolution of AI → ML → Deep Learning → GenAI
• What are LLMs (Large Language Models)
• Transformer architecture basics
• Tokens, embeddings, context windows
• Training vs inference

Topics:

• Working with LLMs
• Prompt engineering: Zero-shot, few-shot prompting
• Role-based prompting
• Temperature, tokens, parameters
• Context management
• Hallucination & limitations
• Tools: OpenAI APIs

Topics:

• Integrating LLMs into applications
• REST API integration
• Handling API responses
• Streaming responses
• Rate limits & optimization

Topics:

• Application frameworks for LLMs
• Chains & pipelines
• Prompt templates
• Memory management
• Tool integration
• Tools: LangChain

Topics:

• Knowledge-based AI systems
• Document ingestion
• Chunking strategies
• Embeddings
• Vector databases
• Retrieval strategies
• Tools: FAISS / Pinecone

Topics:

• Semantic search
• Text embeddings
• Similarity search
• Indexing strategies
• Query optimization

Topics:

• Context-aware AI systems
• What is MCP
• Context sharing across tools
• Standardized tool communication
• Multi-source data integration

Topics:

• AI agents & automation
• Agent architecture
• Tool usage by agents
• Multi-agent systems
• Task planning & execution
• Tools: LangChain Agents / AutoGen concepts

Topics:

• Collaboration between agents
• Task delegation
• Coordination strategies
• Workflow orchestration

Topics:

• Responsible AI
• Hallucination control
• Prompt injection attacks
• Data privacy
• Cost optimization

Topics:

• Building real applications
• Backend: FastAPI
• Frontend: Streamlit / React basics
• API integration

Topics:

• Production deployment
• Docker basics
• Cloud deployment
• Scaling AI systems

Project Focus:

• LLM + prompt engineering

Project Focus:

• Document search + Q&A

Project Focus:

• Multi-agent automation

Project Focus:

• Full stack + deployment

Scenarios:

• Build enterprise chatbot
• Automate customer support
• Create AI-powered knowledge systems
• Develop intelligent assistants
• Integrate AI into business workflows

Skills Gained:

• LLM application development
• RAG systems
• Agentic AI
• Full-stack AI development