Get an accessible, nontechnical overview of data science, covering the vocabulary, skills, jobs, tools, and techniques of the field.
Overview
Syllabus
Introduction
- Getting started
- 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
- Artificial intelligence
- Machine learning
- Deep learning neural networks
- Big data
- Predictive analytics
- Prescriptive analytics
- Business intelligence
- Bias
- Security
- Legal
- Explainable AI
- Agency of algorithms and decision-makers
- 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
- The enumeration of explicit rules
- The derivation of rules from data analysis
- The generation of implicit rules
- Applications for data analysis
- Languages for data science
- AutoML
- Machine learning as a service
- Sampling and probability
- Algebra
- Calculus
- Optimization and the combinatorial explosion
- Bayes' theorem
- Supervised vs. unsupervised learning
- Descriptive analyses
- Clustering
- Dimensionality reduction
- Anomaly detection
- Supervised learning with predictive models
- Time-series data
- Classifying
- Feature selection and creation
- Aggregating models
- Validating models
- Generative adversarial networks (GANs)
- Reinforcement learning
- The importance of interpretability
- Interpretable methods
- Actionable insights
- Next steps and additional resources
Taught by
Barton Poulson