Probability and statistics
Importance: Essential (5 of 5)
Models uncertainty and draws justified conclusions from samples.
Course coverage
- Course planned (1)
Data and AI
Data Scientist
Uses statistics, experimentation, programming, and machine learning to answer questions and build predictive analyses.
- Skills
- 8
- Published coverage
- 2
- Awaiting publication
- 6
Skills
Quantitative foundations
Experimental design
Importance: Essential (5 of 5)
Designs experiments and interprets causal evidence responsibly.
Course coverage
- Course planned (1)
Data wrangling
Importance: Essential (5 of 5)
Cleans and reshapes data into analysis-ready form.
Course coverage
Exploratory analysis
Importance: Essential (5 of 5)
Investigates distributions, relationships, anomalies, and data limitations.
Course coverage
- Course planned (1)
Modeling and communication
Data programming
Importance: Essential (5 of 5)
Uses Python or R to build reproducible analyses.
Course coverage
- Courses planned (2)
Machine learning
Importance: Very important (4 of 5)
Selects and trains suitable predictive models.
Course coverage
- Course planned (1)
Model evaluation
Importance: Essential (5 of 5)
Evaluates generalization, uncertainty, bias, and operational usefulness.
Course coverage
- Course planned (1)
Data visualization
Importance: Very important (4 of 5)
Communicates evidence without distorting uncertainty or scale.
Course coverage
Relevant courses
Core foundations
- Data Visualization
- Data Wrangling
- Courses planned (6)
Specializations
- Course planned (1)