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• Fundamentals of AI:
o Machine learning, deep learning, and neural networks
o Supervised, unsupervised, and reinforcement learning
• Business Intelligence Basics:
o Data warehousing and data marts
o ETL processes and data pipelines
o Business intelligence tools (Power BI, Tableau, Looker)
• Data Cleaning and Preprocessing:
o Handling missing data, outliers, and inconsistencies
o Data normalization and standardization
• Feature Engineering:
o Feature selection and extraction
o Feature transformation and scaling
• Data Visualization:
o Creating effective data visualizations
o Storytelling with data
• Supervised Learning:
o Regression analysis
o Classification algorithms (decision trees, random forests, logistic regression)
• Unsupervised Learning:
o Clustering algorithms (k-means, hierarchical clustering)
o Dimensionality reduction techniques (PCA, t-SNE) 1
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www.nexusberry.com
• Model Evaluation and Deployment:
o Model evaluation metrics
o Model deployment and monitoring
• Predictive Analytics:
o Time series forecasting
o Anomaly detection
o Customer churn prediction
• Prescriptive Analytics:
o Recommendation systems
o Optimization techniques
• AI and Business Decision Making:
o Using AI to inform strategic decisions
o Quantifying the impact of AI initiatives
• Ethical Considerations in AI:
o Bias and fairness in AI algorithms
o Privacy and security concerns
o Responsible AI development
• AI and the Future of Work:
o Automation and job displacement
o The rise of new jobs and skills
• Emerging Trends in AI and BI:
o AI-driven automation
o Real-time analytics
o Explainable AI