📚 Data Science – Course Content

1. Introduction to Data Science

• What is Data Science & its applications
• Data Science lifecycle
• Roles: Data Analyst, Data Scientist, ML Engineer
• Tools & technologies overview

2. Python for Data Science

• Python basics & syntax
• Data structures & functions
• File handling
• Working with Jupyter Notebook

3. Data Analysis with Python

• NumPy for numerical computing
• Pandas for data manipulation
• Data cleaning & preprocessing
• Handling missing values & outliers

4. Data Visualization

• Matplotlib & Seaborn
• Exploratory Data Analysis (EDA)
• Visual storytelling with data
• Dashboard-ready visual concepts

5. Statistics for Data Science

• Descriptive statistics
• Probability & distributions
• Hypothesis testing
• Correlation & regression analysis

6. SQL for Data Science

• Database fundamentals
• Writing SQL queries
• Joins, subqueries & aggregations
• Using SQL with analytics datasets

7. Machine Learning Fundamentals

• What is Machine Learning
• Supervised vs Unsupervised learning
• Model training & evaluation
• Overfitting & underfitting

8. Supervised Learning Algorithms

• Linear & multiple regression
• Logistic regression
• Decision trees
• Random forest
• K-Nearest Neighbors (KNN)

9. Unsupervised Learning Algorithms

• Clustering (K-Means, Hierarchical)
• Dimensionality reduction (PCA basics)
• Market segmentation use cases

10. Model Evaluation & Tuning

• Accuracy, precision, recall, F1-score
• Confusion matrix
• Cross-validation
• Hyperparameter tuning

11. Introduction to Deep Learning (Basics)

• Neural network fundamentals
• Deep learning overview
• Use cases & industry relevance

12. Data Science Tools & Platforms

• Jupyter Notebook
• Google Colab
• Git & GitHub basics
• Deployment concepts (intro)

13. Business Use Cases & Analytics

• Sales & marketing analytics
• Customer behavior analysis
• Finance & risk analytics
• Operations & forecasting

14. Capstone Projects & Practical Work

• End-to-end Data Science project
• Real-world datasets
• Model building & presentation
• Portfolio-ready project