- Overview of data science and its applications
- Understanding the data science workflow
- Introduction to Python for data science

- Introduction to the Pandas library
- Working with data structures (Series, DataFrame)
- Data cleaning, filtering, and transformation
- Handling missing data and outliers

- Introduction to data visualization
- Using Matplotlib for creating basic plots
- Creating advanced visualizations with Seaborn
- Customizing plots for effective data communication

- Techniques for exploring and summarizing data
- Descriptive statistics and data distributions
- Data visualization for EDA
- Identifying patterns and relationships in data

- Introduction to NumPy and its array manipulation capabilities
- Performing statistical computations with NumPy
- Hypothesis testing and confidence intervals with SciPy

- Supervised learning algorithms (linear regression, logistic regression, decision trees, random forests, support vector machines, etc.)
- Unsupervised learning algorithms (clustering, dimensionality reduction, etc.)
- Evaluation metrics for machine learning models

- Training and testing machine learning models
- Cross-validation techniques
- Hyperparameter tuning and model selection

- Techniques for feature engineering
- Feature scaling and normalization
- Feature selection methods (filter, wrapper, embedded)

- Processing and cleaning text data
- Text feature extraction techniques
- Building text classification models

- Basics of neural networks and deep learning
- Building deep learning models with TensorFlow or Keras
- Transfer learning and pre-trained models

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