Basics of Data Science

1. An Introduction to Python Programming
   • Variables, Data Types, Operators
   • Control Structures (if-else, loops)
   • Functions and Modules
   • Exception Handling
   • Libraries
     • Pandas: DataFrames, Series, Data Manipulation (filtering, grouping, merging)
     • NumPy: Array, Array Operations, Math Functions, Broadcasting
     • Matplotlib and Seaborn: Visualizations
2. Machine Learning
   • Exploratory Data Analysis
     • Data Cleaning: Missing Values, Duplicates, ...
     • Data Transformations: normalization, onehot-encoding, standardization
     • Visualizations: Line, Bar, Scatter, Histogram, Box-Plot, Violin Plots, ...
   • Supervised Learning
     • Linear Regression: Simple, Multiple, ...
     • Classification: Logistic, k-Nearest Neighbors, SVM
     • Ensemble Methods: RF, GB, XGBoost, LightGBM
     • Multi-Class and Multi-Label Classifications
     • Survival Models
   • Unsupervised Learning
     • Clustering: k-Means, Heirarchical, ...
     • Dimensionality Reduction: PCA, t-SNE
   • Model Evaluation and Improvement
     • Hyperparameter Tuning: Grid, Random Search
     • Cross-Validation: k-Fold, Stratified
3. Introduction to Deep Learning
   • Frameworks: PyTorch, TensorFlow, Keras
     • Tensor Basics, Gradient Computation, Forward and Backward propagations, Loss Functions, ...
     • Training and Testing Loops, Data Loading and Preprocessing, Dataset Transforms, Activation Functions, ...
   • Neural Networks: FFNN, CNN, RNN
     • Training and Testing Models,
     • Making Predictions,
     • Saving and Loading Models
   • Device-Agnostic Codes, Going Modular
4. End-to-End Project: Problem Definition, Model Development and Deployment