Probability and statistics
Importance: Essential (5 of 5)
Models uncertainty and draws justified conclusions from samples.
← All career guides8 of 8 skills32 of 33 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
97%
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
- 32
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.
Data wrangling: 2 more courses
Data programming: 4 more courses
Data Scientist capability map
- Data Scientist
- Quantitative foundations
- Modeling and communication
01
Quantitative foundations
4 capabilities
Experimental design
Importance: Essential (5 of 5)
Designs experiments and interprets causal evidence responsibly.
Course coverage
Data wrangling
Importance: Essential (5 of 5)
Cleans and reshapes data into analysis-ready form.
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.
Course coverage
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
Downloadable persona
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Last updated September 5, 2026
Learning path
Relevant courses
Core foundations
The concepts this role is built on.
Supporting knowledge
Adjacent knowledge that improves day-to-day judgment.
- A/B Testing and Experimentation
- Artificial Intelligence FundamentalsFree
- Databricks Fundamentals
- Feature Engineering for Time Series
- Machine Learning Frameworks
- Neural Networks
- Python for Analysis
- PyTorch Fundamentals
- Recommender Systems
- Regression Analysis
- SQL FundamentalsFree
- Statistical Drift Detection Methods
- Supervised Learning
- TensorFlow Fundamentals
- Unsupervised Learning
Specializations
Deeper paths for particular environments or directions.
Put the map to work
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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.