Week 1: Introduction to data science – overview, lifecycle, and roles.

Week 2: Python for data science – advanced Python and core libraries.

Week 3: Statistics and probability – essential concepts and distributions.

Week 4: Data wrangling – cleaning and transforming data.

Week 5: Exploratory Data Analysis (EDA) – visualizing and interpreting data.

Week 6: Introduction to machine learning – supervised vs. unsupervised learning.

Week 7: Regression techniques – linear and multiple regression.

Week 8: Classification methods – logistic regression and decision trees.

Week 9: Clustering – K‑means and hierarchical clustering.

Week 10: Python libraries – Scikit‑Learn, TensorFlow introduction.

Week 11: Data ethics and bias in AI.

Week 12: Mini project: Apply foundational data science techniques on a curated dataset.

Week 13: Advanced machine learning – ensemble methods and gradient boosting.

Week 14: Deep learning fundamentals – neural networks and backpropagation.

Week 15: Neural networks with TensorFlow/Keras – model building.

Week 16: Convolutional Neural Networks (CNNs) for image classification.

Week 17: Recurrent Neural Networks (RNNs) for sequence modeling.

Week 18: Natural Language Processing (NLP) – text analysis and sentiment analysis.

Week 19: Model evaluation and hyperparameter tuning.

Week 20: Big data tools – introduction to Spark for data science.

Week 21: Emerging topics – reinforcement learning and generative models.

Week 22: Data storytelling – effective visualization and communication.

Week 23: Start a mid‑project applying advanced models.

Week 24: Mid‑project review and refinement.

• Define a complex data science problem, plan the project, and form teams.

• Model refinement, integration with data pipelines, and deployment strategies.

• Final integration, performance monitoring, portfolio documentation, and final presentations.