Data Science Foundations: Fundamentals (2019)

Introduction

• The fundamentals of data science

1. What Is Data Science?

• Supply and demand for data science
• The data science Venn diagram
• The data science pathway
• Roles and teams 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

• Legal, ethical, and social issues of data science
• Agency of algorithms and decision-makers

4. Sources of Data

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

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
• Machine learning as a service

7. Mathematics for Data Science

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

8. Analyses for Data Science

• Descriptive analyses
• Predictive models
• Trend analysis
• Clustering
• Classifying
• Anomaly detection
• Dimensionality reduction
• Feature selection and creation
• Validating models
• Aggregating models

9. Acting on Data Science

• Interpretability
• Actionable insights

Conclusion

• Next steps