Course Material

Course Schedule

Week	Lectures	Videos/Additional Resources	Assignments
1	Data Science Lifecycle	Python for Data Science
2	Sampling and Scientific Studies	Sampling Correlation and Causation Sampling Strategies Observational vs. Experimental
3	Statistical Charts	Numerical Variables Visualization Shape of Numerical Distributions Data Transformation Categorical Variables Visualization	CA0
4	Review of Probability	Probability Definition Independence Conditional Probability Random Variable Normal Distribution Q-Q Plot	CA1
5	Foundations for Inference	Central Limit Theorem Confidence Interval Hypothesis Testing Types of Error
6	Linear Regression	Introduction to Linear Regression Hypothesis Testing for Linear Regression Multiple Linear Regression	CA2
7	SQL-1 SQL-2 Data Preprocessing	SQL Data Cleaning and EDA	CA3 Project-Phase 0
8	Modeling Gradient Descent	Modeling Gradient Descent-1 Gradient Descent-2	CA4
9	Sklearn and Feature Engineering		CA5
10	Logistic Regression-2 Cross-Validation & Regularization	Cross Validation & Regularization Performance Metrics	Project-Phase 1
11	SVM & KNN Decision Tree & Random Forest	Decision Tree SVM & KNN
12	Dimensionality Reduction Unsupervised Learning-1	Dimensionality Reduction PCA Random Projection LLE K-Means Choosing Correct Number of Clusters Clustering Applications	CA6
13	Neural Networks Natural Language Processing	A Guide to Data Visualization Neural Networks NLP-1 NLP-2	CA7
14	Data Science Applications	A Guide to Feature Extraction	Project-Phase 2 Project-Presentation