Data Science Course Content 1. Visualization 2. Exploratory Data Analysis 3. A/B testing and tracking Experiment via CometML 4. EDA and Feature Engineering 5. Classification 6. Encoders from Sklearn Library 7. Hyperparameters: Various methods to tune 8. Coordinate and Stochastic Gradient Descents 9. Linear Algebra in context of Linear Models