🎓 ADVANCED-LEVEL DATA SCIENCE SYLLABUS

Duration: 8–16 Weeks
Prerequisite: Intermediate ML, Python, SQL, Pandas, Visualization
Outcome: Able to build production-ready ML models, deep learning systems, and deployed AI apps.

🧠 MODULE 1: Advanced Statistics & Mathematics

Topics:

• Probability distributions (Binomial, Poisson, Gaussian)
  
  

• Central Limit Theorem
  
  

• Confidence intervals
  
  

• Bayesian statistics
  
  

• Advanced Hypothesis Testing
  
  

• Maximum Likelihood Estimation
  
  

• Gradient Descent & Optimization
  
  

• Vector calculus for ML
  
  

• Regularization (L1, L2, ElasticNet)
  
  

Hands-on:

• Build your own linear model from scratch
  
  

• Implement gradient descent on a custom dataset
  
  

🤖 MODULE 2: Advanced Machine Learning

Algorithms:

• Gradient Boosting (GBM)
  
  

• XGBoost
  
  

• LightGBM
  
  

• CatBoost
  
  

• Support Vector Machines (advanced kernels)
  
  

• Ensemble learning techniques:
  
  

  • Bagging
    
    

  • Boosting
    
    

  • Stacking
    
    

• Model ensembling & blending
  
  

• AutoML concepts
  
  

ML Concepts:

• Hyperparameter tuning (GridSearch, RandomSearch, Optuna)
  
  

• Feature selection (Recursive Feature Elimination, SHAP)
  
  

• Advanced evaluation metrics (ROC-AUC, LogLoss)
  
  

Hands-on:

• Build a boosting-based ML model that outperforms linear methods
  
  

• Optimize models using advanced tuning
  
  

🧬 MODULE 3: Deep Learning Foundations

Topics:

• Neural Networks architecture
  
  

• Forward & backward propagation
  
  

• Loss functions
  
  

• Activation functions (ReLU, Softmax, LeakyReLU, GELU)
  
  

• Regularization: Dropout, BatchNorm
  
  

• GPU training fundamentals
  
  

Hands-on:

• Build Neural Network from scratch using NumPy
  
  

• Train a deep model using TensorFlow or PyTorch
  
  

🖼️ MODULE 4: Computer Vision (CV)

Topics:

• Image preprocessing
  
  

• CNNs (Convolutional Neural Networks)
  
  

• Transfer Learning:
  
  

  • ResNet
    
    

  • VGG
    
    

  • MobileNet
    
    

• Data augmentation
  
  

• Object detection intro (YOLO / Faster R-CNN)
  
  

Hands-on:

• Train an image classifier model
  
  

• Build a small project using transfer learning
  
  

🗣️ MODULE 5: Natural Language Processing (NLP)

Topics:

• Text cleaning
  
  

• Tokenization & Lemmatization
  
  

• TF-IDF & Bag-of-Words
  
  

• Word embeddings: Word2Vec, GloVe
  
  

• Transformers (BERT, GPT models)
  
  

• Sequence modeling: RNN, LSTM, GRU
  
  

Hands-on:

• Build a sentiment analysis model
  
  

• Train a text classifier
  
  

• Use HuggingFace Transformers
  
  

🧩 MODULE 6: Time Series Analysis

Topics:

• Trend, seasonality, cyclic analysis
  
  

• ARIMA / SARIMA
  
  

• Prophet (Meta)
  
  

• LSTM models for time series
  
  

• Feature engineering for time series
  
  

Hands-on:

• Forecast sales or stock price
  
  

• Compare statistical vs deep learning models
  
  

🛠️ MODULE 7: Data Engineering for Data Science

Topics:

• Pipelines (ETL, ELT)
  
  

• Data lakes & warehouses
  
  

• Apache Spark basics
  
  

• Cloud storage: AWS S3, Google Cloud Storage
  
  

• Docker (containerization)
  
  

Hands-on:

• Build a data preprocessing pipeline
  
  

• Store and retrieve data from a cloud bucket
  
  

🚀 MODULE 8: Machine Learning Deployment

Topics:

• Model serving with Flask / FastAPI
  
  

• Model packaging
  
  

• REST API creation
  
  

• Dockerizing ML apps
  
  

• CI/CD basics
  
  

• Monitoring deployed models (MLOps)
  
  

Hands-on:

• Deploy your ML model as a real API
  
  

• Build a full ML web app (Streamlit/Flask)
  
  

📊 MODULE 9: Big Data & Scalable ML

Topics:

• Hadoop ecosystem
  
  

• Spark MLlib
  
  

• Distributed computing
  
  

• Batch vs streaming processing
  
  

• Using GPUs & TPUs
  
  

Hands-on:

• Run ML pipelines on Spark
  
  

• Train models on large datasets
  
  

🧠 MODULE 10: Generative AI (Latest Skills)

Topics:

• Foundation Models
  
  

• Prompt engineering
  
  

• Fine-tuning LLMs (Llama, GPT, Mistral)
  
  

• Vector embeddings & RAG
  
  

• Building Chatbot with LLM
  
  

• LLM deployment architecture
  
  

Hands-on:

• Build your own RAG-based Q&A system
  
  

• Fine-tune a small LLM
  
  

🏆 Capstone Projects (Choose One or More)

Industry-level projects:

📌 Customer churn prediction
📌 Fraud detection system
📌 Computer vision object detector
📌 Chatbot using transformers
📌 Sales forecasting (deep learning)
📌 Automated ML pipeline with deployment

Deliverables:

• Jupyter Notebook
  
  

• Data pipeline
  
  

• EDA
  
  

• Model training + tuning
  
  

• Deployment + API
  
  

• Presentation & documentation
  
  

🎯 After completing this advanced course you will be able to:

✔ Build production-level ML & DL models
✔ Work professionally as Data Scientist / ML Engineer
✔ Handle large-scale data
✔ Deploy ML apps
✔ Work with modern AI models (LLMs, Transformers)