Course Material Description Calendar Guidelines Prof. Bahrak's Youtube Channel Course Schedule Week Lectures Videos/Additional Resources Assignments Assignments Related Videos 1 Introduction – Data science lifecycle Python for Data Science Probability & Statistics Review Python for Data Science NoteBooks 2 Sampling and Statistical Charts Sampling Correlation and Causation Sampling Strategies Observational vs. Experimental Numerical Variables Visualization Shape of Numerical Distributions Data Transformation Categorical Variables Visualization 3 Review of Probability Probability Definition Independence Conditional Probability Random Variable Normal Distribution Types of Error Q-Q Plot Monte Carlo Simulation CA0 CA0 Related Materials 4 Foundations for Inference Visualization Design Principles Confidence Interval Central Limit Theorem Hypothesis Testing Visualising Numerical Features Project-Phase 0 PowerBI-Final Project P0 Related video Web Scraping-Final Project P0 Related video 5 Preattentive Attributes Linear Regression(optional) Dashboards and Storytelling Tableau Import datasets into Tableau Effective visualization methods in Tableau Abnormality diagnosis with the help of visualization statistical charts in Tableau Creating KPI Creating dashboard Introduction to Linear Regression Hypothesis Testing for Linear Regression Multiple Linear Regression CA1 Sampling - CA1 Related video 6 Cognitive AI (Optional) Cognitive AI Workshop Project-Phase 1 7 SQL-1 SQL-2 SQL CA2 8 Big Data Data Cleaning and EDA Big Data Data Cleaning and EDA 9 Modeling Gradient Descent Logistic Regression Modeling Gradient Descent-1 Gradient Descent-2 10 Sklearn Feature Engineering Logistic Regression Cross Validation and Regularization SVM & KNN Decision Tree and Random Forest Logistic Regression SoftMax Classifier SVM (linear) Decision Trees Decision Trees Regularization Ensemble Learning Bagging & Boosting Decision Tree2(Optional) Cross Validation & Regularization Performance Metrics CA3 11 Neural Networks CNNs Neural Networks CNNs Implementing CNNs using Keras Pooling Layers CNN Architectures Project-Phase 2 12 RNNs NLP RNNs-1 RNNs-2 NLP-1 NLP-2 CA4 13 LMs 14 LLM Agents(just Part1&2) Unsupervised Learning LLM Agents Dimensionality Reduction PCA Random Projection LLE (Optional) K-Means Choosing Correct Number of Clusters Clustering Applications DBScan 15 Project-Presentation CA5 16 Project-Presentation 17 Final Sample Questions Data Science Applications (Optional) A Guide to Feature Extraction