Foundations of Data Science

Before the program A short pre-read on data science fundamentals is shared before the first session. Students arrive knowing what data science is and why it matters, so live time goes straight into doing. Week 1 - Data and Excel Session 1 Introduction to data science and problem statement. What data science is and how it solves real problems. Students frame their problem statement and identify their raw dataset. Session 2 Excel for data analysis and visualisation basics. Cleaning and organising data, applying formulas, and building initial charts. Session 3 Tableau and Power BI. Building dashboards that tell a clear visual story from your data. Week 2 - Dashboards and Python Session 4 Tableau and Power BI deep dive. Advanced dashboard techniques applied to each student's own dataset. Session 5 Python intro. Syntax, pandas, and loading data. Students set up their environment and run their first analysis. Session 6 Python data cleaning and analysis. Working with real data, handling missing values, and generating initial findings. Week 3 - Analysis and Insights Session 7 Python visualisation with matplotlib and seaborn. Generating charts directly from project data. Session 8 Python project deep dive and insight generation. Students push their analysis further and identify the key story in their data. Session 9 Final project visualisation and dashboard polish. Bringing together Python outputs and dashboard work into a cohesive, publish-ready visual. Week 4 - Refinement and Publishing Session 10 Advanced Tableau and Power BI. Refining and elevating the dashboard component of the project. Session 11 Problem statement refinement and data check-in. Students revisit their original question, tighten the framing, and confirm the data supports their conclusions. Session 12 Report structure, data storytelling, and publishing. Writing methodology, structuring the report, citing sources, and submitting a polished project on Futurowise. After the program Students receive a curated reading list covering data science, Python, and analytics, drawn from resources that practitioners and students actually use. Support is available to help finalize and polish the project report before submission.