Practical Hands-on Data Analysis and Visualization using Python and Jupyter Notebook
Data is everywhere. That means more companies are tracking,
analysing, and using the insights they find to make better decisions.
Python is a popular programming language.
It is used for:
- web development (server-side),
- software development,
- Data Analysis
- Data Visualization
- System scripting.
What can Python do?
- Python can be used on a server to create web applications.
- Python can be used alongside software to create workflows.
- Python can connect to database systems. It can also read and modify files.
- Python can be used to handle big data and perform complex mathematics.
- Python can be used for rapid prototyping, or for production-ready software development.
- Python can be used for data analysis and visualization.
Data analysis is the process of analysing, interpreting, data to discover valuable insights that drive smarter and more effective business decisions.
Data analysis tools are used to extract useful information from business and other types of data, and help make the data analysis process easier.
Data visualisation is the graphical representation of information and data.
By using visual elements like charts, graphs and maps, data visualisation tools
provide an accessible way to see and understand trends, outliers and patterns in data.
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modelling, data visualization, machine learning, and much more.