These guides are provided for learners on Generation UK & I Data Analysis and Data Engineering bootcamps; It also acts as an extension resource for anyone looking to extend their training from other programs.
These resources cover the theory of data visualisation, and guide you through the deployment of an Anaconda (Miniconda) Jupyter Notebook environment.
Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, it provides an accessible way to see and understand trends, outliers, and patterns in data.
Three core tools used by almost all data scientists are numpy, pandas, and matplotlib.
These tools are closely related and integrate seamlessly, however each has specific functions and can be used individually.
NumPy is the Foundation, and provides fast numerical computing capabilities. It provides:
- Fast, memory‑efficient arrays
- Vectorised mathematical operations
- Tools for generating sequences, random numbers, matrices, etc.
Almost everything in scientific Python depends on NumPy because it replaces slow Python loops with fast C‑based operations.
Pandas uses NumPy internally; Every Pandas Series is essentially a NumPy array with labels; Every DataFrame column is built on top of NumPy arrays.
So NumPy is the engine that powers Pandas’ performance.
When using Pandas we work with:
Series(labelled 1D data)DataFrames(labelled 2D tables)
Pandas includes tools for:
- loading data (CSV, Excel, SQL…)
- cleaning and transforming data
- filtering and grouping
- summarising and aggregating
Matplotlib is the visualisation layer that draws plots, which can be:
- line charts
- bar charts
- scatter plots
- histograms
- box plots
- and much more
Imagine you’re cooking a meal:
- NumPy is your raw ingredients (fast, basic building blocks)
- Pandas is your kitchen (tools for organising, preparing, and combining ingredients)
- Matplotlib is the plating and presentation (turning the final dish into something you can show others)
Each layer depends on the one below it, and together they form a complete workflow.
Find tutorials for each of these libraries below:
Introduction to Numpy
Introduction to Pandas
Introduction to Matplotlib