Course Material

• Description
• Calendar
• Guidelines
• Prof. Bahrak's Youtube Channel

Course Schedule

Week	Lectures	Videos/Additional Resources	Assignments	Assignments Related Videos
1	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 Project-Phase 1	PowerBI-Final Project P1 Related video Web Scraping-Final Project P1 Related video CA0 Related Video
4	Foundations for Inference Visualization Design Principles	Confidence Interval Central Limit Theorem Hypothesis Testing Visualising Numerical Features
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
6	Cognitive AI	Cognitive AI Workshop	CA1 Project-Phase 1	Sampling - CA1 Related video
7	SQL-1 SQL-2	SQL	CA2
8	Big Data Data Cleaning and EDA	Big Data Data Cleaning and EDA	Project-Phase 2
9	Modeling Gradient Descent Logistic Regression	Modeling Gradient Descent-1 Gradient Descent-2	CA3
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
11	Neural Networks CNNs	Neural Networks CNNs Implementing CNNs using Keras Pooling Layers CNN Architectures	Project-P3
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	CA5
15 (Optional)	Final Sample Questions Data Science Applications	A Guide to Feature Extraction	Project-Presentation