Overview
Get a basic introduction to the careers, tools, and techniques of modern data science.
Syllabus
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
- Next steps
Conclusion