How to Build an AI Team: Roles and Operating Models
Which roles does your first AI team need, in what order should you hire, and which operating model fits: centralised, federated or hybrid?
Read more →
Technical Teams
Data Science with Python
Data preparation, exploratory analysis and visualisation: a solid foundation in data science.
- 3–5 days
- Software, data and infrastructure teams
- On-site or online
- Certificate of attendance
What you will learn
- Load, clean and transform data with Python
- Analyse data with pandas and NumPy
- Perform exploratory data analysis and visualisation
- Complete an end-to-end analysis project on a real dataset
Course content
- Python and Jupyter for data science
- Data processing with NumPy and pandas
- Data cleaning, missing values and outliers
- Visualisation with Matplotlib and Seaborn
- Exploratory data analysis and capstone exercise
Who should attend
- Developers and analysts moving into data science
- Reporting and BI teams
- Newly formed data teams
Prerequisites
Basic programming knowledge is recommended.
Course content is tailored to your organisation's needs. All programmes can be delivered on-site at your premises or online. Participants who complete a programme receive a certificate of attendance.
Frequently Asked Questions
Is programming knowledge required?
Basic programming knowledge is recommended. The content can be adapted for Python beginners.
Which libraries are used?
Widely used Python data science libraries, mainly NumPy, pandas, Matplotlib and Seaborn.
Can it run in an internal environment?
Yes. A working environment that meets your security rules is agreed in advance.
Which programme should come next?
For teams moving into model building, Applied Machine Learning is the natural next step.
Free assessment · 2 min
Start now
Training Finder: Which AI Training Is Right for You?
Answer a few short questions and see the AI training programmes that suit your team as an ordered path, with the reason for each.
Related articles
Related courses
Applied Machine Learning
Classification, regression, model evaluation and feature engineering on real-world datasets.
Learn more →Building LLM Applications
RAG architectures, agents and MCP, output evaluation, security and on-premise LLM deployment.
Learn more →MLOps: Taking Models to Production
CI/CD, model versioning, monitoring and data drift, plus LLMOps for LLM applications: keeping models reliable in production.
Learn more →DevOps & AWS Fundamentals
Containers, automation and the fundamentals of AWS cloud architecture.
Learn more →Get a tailored training proposal
Tell us which programmes you are interested in, the number of participants and the preferred format, and we will prepare a tailored proposal.