Data Science Foundations: Fundamentals (2019)

Introduction

• Getting started

1. What Is Data Science?

• Supply and demand for data science
• The data science Venn diagram
• The data science pathway
• The CRISP-DM model in data science
• Roles and teams in data science
• The role of questions in data science

2. The Place of Data Science in the Data Universe

• Artificial intelligence
• Machine learning
• Deep learning neural networks
• Big data
• Predictive analytics
• Prescriptive analytics
• Business intelligence

3. Ethics and Agency

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

4. Sources of Data

• Data preparation
• Labeling data
• In-house data
• Open data
• APIs
• Scraping data
• Creating data
• Passive collection of training data
• Self-generated data
• Data vendors
• Data ethics

5. Sources of Rules

• The enumeration of explicit rules
• The derivation of rules from data analysis
• The generation of implicit rules

6. Tools for Data Science

• Applications for data analysis
• Languages for data science
• AutoML
• Machine learning as a service

7. Mathematics for Data Science

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

8. Unsupervised Learning

• Supervised vs. unsupervised learning
• Descriptive analyses
• Clustering
• Dimensionality reduction
• Anomaly detection

9. Supervised Learning

• Supervised learning with predictive models
• Time-series data
• Classifying
• Feature selection and creation
• Aggregating models
• Validating models

10: Generative Methods in Data Science

• Generative adversarial networks (GANs)
• Reinforcement learning

11. Acting on Data Science

• The importance of interpretability
• Interpretable methods
• Actionable insights

Conclusion

• Next steps and additional resources