Probability and statistics
Importance: Essential (5 of 5)
Models uncertainty and draws justified conclusions from samples.
Course coverage
- Probability and Statistics
- Statistical Drift Detection Methods (planned)
← All career guides8 of 8 skills28 of 31 courses
Data and AI
Data Scientist
Uses statistics, experimentation, programming, and machine learning to answer questions and build predictive analyses.
Skill coverage
100%
Course coverage
90%
Skill coverage shows how much of the role's capability map already has a published course. Course coverage shows how much of the mapped course pipeline for this role has shipped.
- Role skills
- 8
- Published coverage
- 8
- Relevant courses
- 28
Capability map
Skills
Importance describes how central each capability is to the role. Course coverage shows where you can build the skill in the current OpenSkills catalog.
01
Quantitative foundations
4 capabilities
Experimental design
Importance: Essential (5 of 5)
Designs experiments and interprets causal evidence responsibly.
Course coverage
- Experimental Design
- A/B Testing and Experimentation (planned)
Data wrangling
Importance: Essential (5 of 5)
Cleans and reshapes data into analysis-ready form.
Course coverage
- Data Wrangling
- Feature EngineeringFree
- Databricks Fundamentals
- SQL Fundamentals
- Feature Engineering for Time Series (planned)
Exploratory analysis
Importance: Essential (5 of 5)
Investigates distributions, relationships, anomalies, and data limitations.
Course coverage
02
Modeling and communication
4 capabilities
Data programming
Importance: Essential (5 of 5)
Uses Python or R to build reproducible analyses.
Machine learning
Importance: Very important (4 of 5)
Selects and trains suitable predictive models.
Model evaluation
Importance: Essential (5 of 5)
Evaluates generalization, uncertainty, bias, and operational usefulness.
Course coverage
Data visualization
Importance: Very important (4 of 5)
Communicates evidence without distorting uncertainty or scale.
Course coverage
Learning path
Relevant courses
Core foundations
The concepts this role is built on.
Supporting knowledge
Adjacent knowledge that improves day-to-day judgment.
- Artificial Intelligence Fundamentals
- Databricks Fundamentals
- Machine Learning Frameworks
- Neural Networks
- Python for Analysis
- PyTorch Fundamentals
- Recommender Systems
- SQL Fundamentals
- Supervised Learning
- TensorFlow Fundamentals
- Unsupervised Learning
- A/B Testing and Experimentation, Feature Engineering for Time Series, Statistical Drift Detection Methods (planned)
Specializations
Deeper paths for particular environments or directions.
Put the map to work
Find Data Scientist roles.
Search multiple job sources at once with queries tailored to this career, then use the skill map above to evaluate what each role actually asks for.