Data Science Foundations: Fundamentals

Introduction

• Welcome

1. What Is Data Science?

• Supply and demand for data science
• The data science Venn diagram revisited
• The evolution of data science
• The CRISP-DM framework
• Roles, teams, and tools in modern data science
• The central role of questions in data science

2. The Place of Data Science in the Data Universe

• Artificial intelligence
• Machine learning
• Deep learning and neural networks
• Transformers and attention for generative AI
• Big data
• Predictive analytics
• Prescriptive analytics
• The evolution of business intelligence

3. Ethics, Privacy, and Regulation

• Bias
• Security and privacy
• Legal
• Explainable AI
• Agency of algorithms and decision-makers

4. Sources of Data and Insights

• Data preparation
• Labeling data for supervised learning
• In-house data
• Open data
• APIs
• Scraping data
• Synthetic data and simulation environments
• Passive collection of training data
• Data vendors
• New data from surveys and experiments
• Data ethics

5. Tools and Techniques for Data Science

• Applications for data analysis
• Languages for data science
• Alternatives to programming: Low-code, no-code, and AutoML
• MLOps
• Machine learning and AI as a service

6. Math Foundations for Data Science

• Sampling and probability
• Algebra
• Calculus
• Optimization and the combinatorial explosion
• Bayes' theorem

7. Learning Paradigms

• Supervised, unsupervised, and reinforcement learning
• Descriptive analytics
• Clustering techniques
• Dimensionality reduction
• Anomaly detection
• Trend analysis
• Aggregating models
• Validating models

8. Algorithms That Create

• Generative Adversarial Networks (GANs)
• Reinforcement learning

9. Acting on Data Science

• The importance of interpretability in AI
• Techniques for creating interpretable models
• Delivering actionable insights

Conclusion

• Next steps