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AI/ML for Business Data Analysis
Turn business data into decisions: clean and explore data with Python, build forecasting and prediction models, and use AI to speed up analysis.
Overview
About this course
Most organisations have more data than time to understand it. This course teaches you to move from spreadsheets to real analysis — cleaning data, spotting patterns and building simple machine-learning models that answer practical business questions.
Everything is taught through business examples: sales, customers, operations and finance. You’ll finish with a capstone analysis on a dataset from your own work (or a realistic sample), ready to present to decision-makers.
Who it’s for
- Business and financial analysts
- Managers who work with reports and KPIs
- Excel power users ready for the next step
Outcomes
What you’ll be able to do
- Clean, combine and explore business data with Python
- Build clear charts and dashboards that support decisions
- Forecast sales and demand with regression models
- Predict outcomes such as customer churn with classification
- Segment customers with clustering
- Use AI assistants to speed up analysis — and check their work
Curriculum
9 modules, step by step
Every module mixes a short explanation with guided, hands-on practice.
- 01
Python for analysts
- Jupyter notebooks
- Python essentials
- Loading Excel and CSV data with pandas
- 02
Preparing business data
- Cleaning and fixing data quality issues
- Joining and reshaping data
- Feature creation
- 03
Exploring & visualising
- Descriptive statistics
- Charts that tell a story
- Dashboards in Power BI
- 04
Machine learning foundations
- How ML works, without the maths overload
- Training, testing and avoiding overfitting
- 05
Forecasting with regression
- Sales and demand forecasting
- Interpreting model results
- 06
Classification & clustering
- Predicting churn and risk
- Customer segmentation
- 07
AI-assisted analysis
- Using LLMs to write and explain code
- Asking better questions of your data
- Verifying AI output
- 08
Responsible AI
- Privacy and data protection
- Bias and fairness
- Explaining results to stakeholders
- 09
Capstone: your own dataset
- End-to-end analysis of a real business problem
- Present findings and recommendations
Tools you’ll use
- Python
- Jupyter
- pandas
- scikit-learn
- Power BI
- ChatGPT / Claude
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