Data Scientist Pro Combo
Course Duration: 25–27 Weeks (Flexible) | Mode: Offline/Online
Goal: Upon completing this programme, students will be able to analyze complex datasets, build and deploy machine learning and deep learning models, and create AI-powered solutions for real-world problems. They will gain end-to-end data science skills, making them job-ready for roles like Data Scientist, ML Engineer, or AI Specialist.
MODULE 1: Python for Data Science (Week 1–3)
- Python fundamentals: Variables, Data Types, Loops, Functions
- Advanced Python: OOP, Modules, Error Handling
- Libraries for Data Science: NumPy, Pandas, Matplotlib, Seaborn
- Data manipulation, cleaning, and preprocessing
MODULE 2: Statistics & Probability (Week 4–5)
- Descriptive statistics: mean, median, mode, variance
- Probability theory, conditional probability, Bayes theorem
- Inferential statistics: hypothesis testing, confidence intervals
- Correlation & covariance
- Sampling methods
MODULE 3: Data Visualization & EDA (Week 6–7)
- Exploratory Data Analysis (EDA) workflow
- Visualization libraries: Matplotlib, Seaborn, Plotly
- Dashboards with Plotly Dash or Streamlit
- Feature selection & feature engineering
MODULE 4: Machine Learning – Supervised Learning (Week 8–10)
- Regression: Linear, Polynomial, Lasso, Ridge
- Classification: Logistic Regression, KNN, Decision Trees, Random Forest, XGBoost
- Model evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC
- Cross-validation & hyperparameter tuning
MODULE 5: Machine Learning – Unsupervised Learning & Clustering (Week 11–12)
- Clustering: K-Means, Hierarchical, DBSCAN
- Dimensionality reduction: PCA, t-SNE
- Anomaly detection
- Recommender systems basics
MODULE 6: Advanced Machine Learning & Ensemble Methods (Week 13–14)
- Gradient Boosting, AdaBoost, XGBoost, LightGBM
- Bagging & stacking techniques
- Model interpretability (SHAP, LIME)
- Feature importance & selection
MODULE 7: Deep Learning & Neural Networks (Week 15–17)
- Neural network fundamentals: Perceptron, Activation functions
- Deep learning with TensorFlow/Keras or PyTorch
- CNNs for image data
- RNNs, LSTMs for sequential data
- Transfer learning
MODULE 8: Natural Language Processing (NLP) (Week 18–19)
- Text preprocessing: Tokenization, Lemmatization, Stopwords removal
- Bag of Words, TF-IDF
- Sentiment analysis, text classification
- Word embeddings: Word2Vec, GloVe
- Transformer models: BERT, GPT (concept overview)
MODULE 9: Time Series Analysis & Forecasting (Week 20–21)
- Time series concepts: trend, seasonality, noise
- ARIMA, SARIMA models
- Prophet model (Facebook)
- Forecasting metrics: MAE, MSE, RMSE
MODULE 10: Model Deployment & MLOps (Week 22–24)
- Introduction to MLOps
- Model serialization (Pickle, Joblib)
- REST APIs with Flask/FastAPI
- Dockerizing models
- Deployment on cloud platforms (AWS, Azure, GCP, or Heroku)
MODULE 11: Practical & Interview Preparation (Week 24-27)
- Practical
- Interview Preparation