This project collects practical Jupyter notebooks for analysing and visualising data using Python. Each notebook explores a different facet of data analysis—from manipulating and cleaning datasets to creating publication‑quality visualisations.
- Confidence intervals – calculating and interpreting confidence intervals for population parameters.
- Data manipulation with pandas – joining, reshaping and filtering tabular data; handling dates and timestamps; working with missing values.
- Data visualisation – creating plots with Matplotlib and Seaborn, including histograms, scatter plots, time series charts and heatmaps.
- pandas – a fast and flexible tool for data analysis and manipulation. Homepage: https://pandas.pydata.org/
- Seaborn – statistical data visualisation built on top of Matplotlib. Homepage: https://seaborn.pydata.org/
- Matplotlib – the foundational plotting library for Python. Homepage: https://matplotlib.org/
- NumPy / SciPy – used for numerical operations and some statistical functions. See https://numpy.org/ and https://scipy.org/.