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 some of 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.
Zomalex’s list of Python courses are here.
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.
Zomalex’s list of SQL courses are here.
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:
Python is now available in Excel which is really useful. See Python in Excel course.
Zomalex’s list of Excel courses are here.
Generative AI tools include large language models such as ChatGPT, Gemini, Claude. These can explore and analyse datasets. Some tools such as ChatGPT have specialised versions (a custom GPT) such as Data Analyst. More details here.
Power BI is Microsoft’s flagship tool for business intelligence. It contains Power Query, a very helpful tool for cleaning and shaping data.
Zomalex’s list of Power BI courses are here.
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.