Go from raw data to actionable insights with Python. Master Pandas for data manipulation, NumPy for numerical computing, Matplotlib and Seaborn for storytelling visualisations, and Scikit-Learn for machine learning — with AI tools that help you write analysis code faster and explain results more clearly.
Hands-on labs with real datasets — sales data, IoT sensor readings, financial records, and social media data — using Jupyter notebooks throughout.
ChatGPT and Claude write Pandas operations from plain English, generate EDA summaries, explain statistical results, and create analysis narratives — teaching the workflow of modern data teams.
Work with real-world datasets from finance, healthcare, e-commerce, and cloud infrastructure — the same types of data you'll encounter in data analyst and data engineer roles.
Analyse 2 years of sales data — clean, aggregate, visualise trends, build a 30-day revenue forecast with Scikit-Learn, and present insights in an interactive Plotly dashboard.
Perform RFM (Recency, Frequency, Monetary) analysis on e-commerce data, apply K-Means clustering, and generate AI-assisted business recommendations for each customer segment.
Build an automated ETL pipeline that ingests healthcare records from PostgreSQL, applies data quality rules, generates daily statistical reports, and emails them to stakeholders.
Use ChatGPT and Claude to guide a full market analysis — generating Pandas operations, writing statistical summaries, creating visualisations, and producing an executive report with AI-generated narratives.
Build a customer churn prediction model with feature engineering, RandomForest classification, Optuna hyperparameter tuning, and SHAP feature importance explanations.
Upon completing all labs and the capstone project, you receive a verified certificate in Data Science with Python & AI — covering the full data workflow from ingestion to machine learning and stakeholder reporting. Shareable on LinkedIn.
Extract insights from business data using Pandas, SQL, and visualisation tools to support decision-making.
Build robust ETL pipelines and data infrastructure using Python, SQL, and cloud data warehouses.
Train, evaluate, and deploy machine learning models using Scikit-Learn and Python automation tools.
Combine traditional data analysis with AI tools (ChatGPT, Claude) for faster insights and automated reporting.
Manage data pipelines on cloud platforms — BigQuery, Redshift, Azure Synapse — using Python automation.
Build dashboards and reporting systems that translate data into clear, actionable business intelligence.
"The AI-assisted EDA module is a game-changer. I now use Claude to write my initial Pandas exploration and ChatGPT to generate analysis summaries. 3x faster than doing it manually."
"The SQL to Python pipeline section is exactly what my analytics role needed. I automated a weekly report that took 4 hours manually — it now runs overnight and emails results."
"The customer churn project got me my first data analyst interview. The end-to-end project with ML, feature importance, and business recommendations showed I could do the whole job."
