AI TRAINING
Python for Non-Engineers: AI and Data Essentials
Gain hands-on Python skills to query APIs, manipulate data, and integrate LLMs without an engineering background.
What it covers
This practical programme teaches analysts, ops professionals, and product managers enough Python to work confidently with data and AI tools. Participants learn to use Jupyter notebooks, manipulate datasets with pandas, call REST APIs, and build simple LLM-powered scripts using the OpenAI or Anthropic SDKs. The course is structured around real business tasks — summarising documents, extracting structured data, and automating repetitive workflows. No prior programming experience is required; the focus is on usable skills, not computer science theory.
What you'll be able to do
- Write Python scripts that load a CSV, filter rows with pandas, and export results to Excel
- Call the OpenAI or Anthropic API to summarise or classify a batch of text records from a spreadsheet
- Build a Jupyter notebook that combines data manipulation and LLM calls into a repeatable workflow
- Parse structured JSON responses from an LLM and insert them into a pandas DataFrame
- Identify when a task is better solved with Python than with a no-code tool, and scope it accordingly
Topics covered
- Python basics: variables, loops, functions, and error handling
- Jupyter notebooks for interactive, reproducible analysis
- pandas for loading, filtering, and transforming tabular data
- Calling REST APIs with the requests library
- OpenAI and Anthropic SDK usage: completions, chat, and embeddings
- Prompt construction and response parsing in Python
- Automating document summarisation and data extraction workflows
- Reading and writing CSV, JSON, and Excel files
Delivery
Delivered as four half-day sessions (online or in-person) spread over two weeks, allowing participants time to practise between sessions. Each session includes a short concept introduction (30%) followed by guided coding exercises on real datasets (70%). Participants work in pre-configured cloud Jupyter environments so no local setup is required. A Slack or Teams channel is opened for async Q&A between sessions. In-person delivery requires a laptop per participant and stable Wi-Fi.
What makes it work
- Anchor every exercise to a real dataset or workflow the participant already owns
- Provide a cloud-based coding environment that removes local setup friction entirely
- Assign a small between-session mini-project that is reviewed at the start of the next session
- Follow up four weeks later with an optional office-hours session to unblock real projects
Common mistakes
- Jumping straight to LLM integrations before participants are comfortable with basic Python syntax and file I/O
- Using abstract programming exercises instead of datasets from participants' actual jobs, leading to low retention
- Skipping environment setup guidance, causing half the cohort to spend session one debugging installations
- Treating the programme as a one-off event without follow-up projects or peer accountability, so skills atrophy quickly
When NOT to take this
This training is not the right fit if the organisation already has a data engineering team that owns all Python tooling and business users are expected only to consume dashboards — in that case, a BI tool (e.g. Tableau, Power BI) literacy programme delivers more immediate value.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.