Introduction to Data Science

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

Data Manipulation with Pandas

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

Data Visualization with Matplotlib and Seaborn

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

Exploratory Data Analysis (EDA)

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

Statistical Analysis with NumPy and SciPy

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

Machine Learning Algorithms

• 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

Model Selection and Validation

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

Feature Engineering and Selection

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

Text Mining and Natural Language Processing (NLP)

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

Time Series Analysis

• Working with time series data
• Time series visualization and exploration
• Forecasting time series data

Introduction to Deep Learning

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

Model Deployment and Production

• Exporting models for deployment
• Building APIs for model integration
• Model deployment considerations

Big Data Processing with PySpark

• Introduction to PySpark and Apache Spark
• Working with large datasets
• Distributed computing with Spark

Data Science Projects and Case Studies

• Real-world data science projects
• End-to-end project workflow
• Best practices and tips for successful data science projects