Learning Outcomes
Session 1:
By the end of session 1 participants will…
- be comfortable working in the Jupyter notebook integrated environment.
- have developed a familiarity with Python’s most common data types:
- integers
- floats
- strings
- booleans
- lists
- understand the differences between a function, method, and attribute.
- have learned about Python’s package system and how to import packages.
- be able to perform basic data manipulation tasks with numpy.
Session 2:
By the end of session 2 participants will…
- be familiar with Python’s pandas DataFrame and Series objects.
- be able to manipulate, extract and summarise data with the pandas package.
- know how to use the matplotlib package to make data visualisations.
- understand how to read data into Python from multiple sources, and write analysed data back out.
This course does not include:
- Advanced data analysis, wrangling and manipulation techniques.
- Enough knowledge to compose complex visualisations and interactive plots, see our Python for Data Visualisation course.
- For loops, function writing or if statements, for more programming skills in Python see our Programming with Python course for this.
- Machine and deep learning modelling techniques, see our website for courses on this topic.
Prior Knowledge
There are no pre-requisites for this course.