Course Objective & Scope
Most data science projects stall because the business question is poorly framed and data readiness problems surface too late. This introductory programme walks through every step, from turning a business problem into a data science question to interpreting the result, using the CRISP-DM framework.
Participants explore data with pandas in Jupyter Notebook, build a simple classification and regression model with scikit-learn and discuss what the metrics actually say. The sample dataset is chosen to match the organisation's industry, and each team leaves with a draft AI-ready data checklist it can reuse.
Who Should Attend?
- Business analysts and reporting specialists moving into data science
- Product and project managers who work alongside data teams
- Software developers who want to understand the data side of AI projects
- Prerequisite: basic Excel knowledge is enough; programming experience is not required
Key Takeaways
- Translate a business problem into a measurable data science question
- Plan a project by adapting the CRISP-DM phases to their own context
- Perform basic exploratory data analysis and visualisation with pandas
- Train a simple classification model and interpret accuracy, precision and recall
- Recognise overfitting and data leakage risks
Curriculum
What Data Science Is and Is Not
- Differences between data science, BI and AI
- Enterprise use case examples and value hypotheses
Framing Projects with CRISP-DM
- Business understanding, data understanding and success criteria
- Early checks on data readiness
Exploratory Data Analysis
- Summary statistics, missing values and outliers with pandas
- Distribution and relationship charts
Your First Machine Learning Model
- Train/test split and feature selection
- Classification and regression with scikit-learn
Model Evaluation and Presenting Results
- Confusion matrix, precision/recall and business impact
- Communicating findings to stakeholders
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