Home | Power BI | Excel | Python | SQL | Generative AI | Visualising Data | Analysing Data
There are lots of great tools for data analysis. This section highlights a few of these. During the course the instructor will demo these tools
Python is a very popular open-source language for data analysis (and also machine learning and AI). It has a vast number of libraries available, such as pandas and numpy for data manipulation, matplotlib and seaborn for data visualisation
Many people explore data with Python using Jupyter notebooks. They provide an interactive and flexible environment for coding, documentation, and visualisation all in one place. Users can write and execute code in cells: this helps iterative exploration and immediate feedback on results, Data visualisations are inline making it easier to interpret data through graphs and charts. We can write documentation in Markdown which allows good presentation of any documentation, analysis and results.
R is a language specifically designed for statistical analysis and visualization. It has a rich ecosystem of packages like ggplot2 and dplyr that make data manipulation and visualization straightforward.
SQL is the languages used to analyse and manage the data in most databases. It is an ANSI standard and so is common across databases from many different manufacturers.
Excel is a powerful tool for data analysis, especially for smaller datasets. Excel can help us with data manipulation, statistical analysis, and visualisation, for example:
Power BI is Microsoft’s flagship tool for business intelligence. It contains Power Query, a very helpful tool for cleaning and shaping data.
Tableau is a data visualization tool that helps in creating interactive and shareable dashboards. It is widely used for its ability to handle large datasets and its user-friendly interface.