Skills
Data Scientist Skills & Tools
The technical skills, people skills and tools employers look for in data scientists, and how to build and show them.
What skills do data scientists need?
Data Scientists need technical skills such as python or r, sql, and statistics, plus people skills like curiosity and communication. They also use tools such as Python libraries such as pandas and scikit-learn and Jupyter notebooks.
Technical (hard) skills
- Python or R
- SQL
- Statistics
- Machine learning
- Data visualization
- Experiment design
People (soft) skills
- Curiosity
- Communication
- Critical thinking
- Business sense
Tools, equipment and technology
- Python libraries such as pandas and scikit-learn
- Jupyter notebooks
- SQL databases and data warehouses
- Visualization tools such as Tableau or Power BI
- Cloud platforms
- Git
Skills in action: common tasks
These step-by-step guides show how data scientists put their skills to work:
- How to clean a dataset for analysis
- How to build and evaluate a prediction model
- How to analyze an a/b test
How to show these skills on a resume
- Lead your skills section with the technical skills from the job posting, for example, “Python or R” and “SQL.”
- Prove soft skills with results: instead of listing “curiosity,” describe a situation where you used it.
- List credentials such as Google Advanced Data Analytics Certificate near the top, with the issuing organization and date.
- Use the employer’s vocabulary. Our data scientist glossary covers the terms hiring managers expect.
How to build these skills
Formal training, supervised practice and certification are the most direct routes. Our Data Scientist training introduces each of these skills with plain-language lessons, procedures and a final knowledge check: a strong foundation before hands-on training or your first day.