1. Understanding Data Science

• What is Data Science?
• Impact on Business
• Key Technologies in Data Science

2. Data Preparation and Preprocessing

• Data Collection
• Handling Missing Data and Outliers
• Normalization and Standardization

3. Exploratory Data Analysis (EDA)

• Goals of Data Analysis
• Basic Statistical Analysis
• Importance of Data Visualization

4. Supervised Learning

• Introduction to Supervised Learning
• Linear Regression
• Decision Trees
• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)
• Naive Bayes Classifier
• Regression vs. Classification

5. Unsupervised Learning

• Introduction to Unsupervised Learning
• Clustering: K-Means, Hierarchical
• Dimensionality Reduction: PCA
• Association Rule Learning

6. Causality in Data Science

• Understanding Causality vs. Correlation
• Methods for Identifying Causality (e.g., A/B Testing, Randomized Controlled Trials)
• Applications in Business Decision Making

7. Recommender Systems

• Types of Recommender Systems (Collaborative Filtering, Content-Based)
• Building a Simple Recommender System
• Challenges and Applications in Business

8. Model Evaluation and Selection

• Importance of Model Evaluation
• Cross-Validation
• Evaluation Metrics (Accuracy, Precision, Recall)
• Overfitting vs. Underfitting

9. Model Tuning

• Hyperparameter Tuning
• Grid Search vs. Random Search
• Ensemble Methods (Bagging, Boosting)

10. Data Leakage

• What is Data Leakage?
• Identifying and Preventing Leakage
• Impact on Model Performance

11. Data Science in Business

• Data-Driven Decision Making
• Predictive Analytics: Churn, Demand Forecasting
• Managing Data Science Projects

12. Conclusion

• Integrating Data Science in Business
• Future Trends in Data Science
• Sustainable Growth Through Data Science