Data Mining for Business Analytics & Data Analysis in Python

Übersicht

Are you looking to learn how to do Data Mining like a pro? Do you want to find actionable business insights using data science and analytics and explainable artificial intelligence?You have come to the right place.

Lehrplan

• 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

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Unterrichtet von

Diogo Alves de Resende

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Fachgebiete

Data Science