Overview
Python for Data Analytics & Explainable Artificial Intelligence. Data Mining for Business Data Analytics & Intelligence.
Syllabus
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- Introduction to Data Mining and Business Analytics
-- Overview of Data Mining
-- Importance of Data Mining in Business
-- Introduction to Business Analytics
-- Tools and Technologies Used
- Setting Up Your Python Environment
-- Installing Python and Jupyter Notebook
-- Overview of Essential Python Libraries (Pandas, NumPy, Matplotlib, Sci-kit Learn)
- Data Preparation and Exploration
-- Collecting and Importing Data
-- Data Cleaning and Preprocessing
-- Exploratory Data Analysis (EDA)
-- Handling Missing Data and Outliers
- Data Mining Techniques
-- Supervised Learning Techniques
--- Classification (Logistic Regression, Decision Trees, Random Forests)
--- Regression Analysis
-- Unsupervised Learning Techniques
--- Clustering (K-Means, Hierarchical Clustering)
--- Association Rule Mining
-- Dimensionality Reduction (PCA)
- Business Analytics Applications
-- Market Basket Analysis
-- Customer Segmentation
-- Sales Forecasting
-- Predictive Maintenance
- Explainable Artificial Intelligence (XAI)
-- Understanding Model Interpretability
-- Tools and Techniques for Explainability (e.g., LIME, SHAP)
- Advanced Topics in Data Analysis
-- Time Series Analysis
-- Text Mining and Sentiment Analysis
-- Anomaly Detection
- Deploying Data Mining Models
-- Model Evaluation and Validation
-- Implementing Models in Business Environments
- Project Work
-- Real-world Business Case Study
-- Data Mining Project Design and Execution
-- Presentation of Business Insights and Recommendations
- Conclusion and Future Directions
-- Emerging Trends in Data Mining and Analytics
-- Continuous Learning Resources and Next Steps
Taught by
Tags
On-Demand
