Data Science Python Bootcamp

 Introduction to Python and Jupyter Notebooks (2 Hours)

• Overview of Python programming language.
• Setting up Python environment and Jupyter Notebooks.
• Basic Python syntax, data types, and structures.

Data Manipulation with Pandas (3 Hours)

• Introduction to Pandas library.
• Series and DataFrame data structures.
• Basic operations: reading, writing, and exploring data.
• Handling missing data and data transformation.

 Matplotlib and Seaborn (2.5 Hours)

• Introduction to Matplotlib for basic plotting.
• Customizing plots: labels, legends, and styles.
• Introduction to Seaborn for advanced statistical plots.

Practical Visualization Techniques (2.5 Hours)

• Creating various plot types: line, bar, scatter, histogram.
• Visualizing data distributions and relationships.
• Using visualizations for data insights.

 Data Cleaning Techniques (2.5 Hours)

• Identifying and handling outliers.
• Dealing with duplicate and irrelevant data.
• Data normalization and standardization.

Exploratory Data Analysis (EDA) (2.5 Hours)

• Techniques for summarizing and describing data.
• Univariate and bivariate analysis.
• Visualizing EDA results.
• Hands-on EDA project.

Supervised Learning (2.5 Hours)

• Overview of supervised learning and its applications.
• Introduction to linear regression and classification.
• Training and evaluating simple models using Scikit-Learn.

Unsupervised Learning (2.5 Hours)

• Overview of unsupervised learning and its applications.
• Introduction to clustering and dimensionality reduction.
• Hands-on practice with K-means and PCA.

 Model Building and Evaluation (2.5 Hours)

• Using Scikit-Learn for model building.
• Model selection and hyperparameter tuning.
• Evaluating model performance using various metrics.

Practical Machine Learning Project (2.5 Hours)

• Application of machine learning techniques to a real-world dataset.
• End-to-end project: from data preprocessing to model deployment.
• Documenting findings and insights.

 Capstone Project Development (5 Hours)

• Applying all learned concepts to a comprehensive project.
• Data collection, cleaning, and exploration.
• Building, evaluating, and deploying a machine learning model.
• Presenting findings and insights.