Module 1: Introduction to Data Science

• What is Data Science?

• Applications and trends

• The data science workflow

Module 2: Python for Data Science

• Python basics and data structures

• NumPy, Pandas, and data manipulation

• Working with Jupyter notebooks

Module 3: Data Visualization

• Matplotlib and Seaborn

• Plotly and interactive dashboards

• Visual storytelling techniques

Module 4: Statistics & Probability

• Descriptive and inferential statistics

• Hypothesis testing

• Probability distributions

Module 5: Data Preprocessing

• Handling missing values

• Data normalization and transformation

• Feature engineering and selection

Module 6: Exploratory Data Analysis (EDA)

• Understanding data trends and patterns

• Correlation and outlier detection

• Using Pandas Profiling

Module 7: Machine Learning Fundamentals

• Supervised vs unsupervised learning

• Regression and classification

• Clustering and dimensionality reduction

Module 8: Advanced Machine Learning

• Ensemble models (Random Forest, XGBoost)

• Model evaluation and hyperparameter tuning

• Cross-validation and pipelines

Module 9: SQL for Data Science

• Basics of SQL and data querying

• Joins, subqueries, and aggregations

• Working with relational databases

Module 10: Capstone Project & Deployment

• Real-world project on end-to-end data analysis

• Model deployment using Flask or Streamlit

• Resume building and interview preparation