This course is delivered through the Jupyter notebook application. It begins with a coverage of fundamental building blocks in Python – numeric data types, strings, lists, dictionaries, sets – replete with examples. Illustrations are then provided on the use of these data types to compose code with selection and iteration constructs. To promote modular and readable code the set-up and use of functions with parameters are also covered.
Specialised knowledge in data analysis is then developed through a comprehensive overview of the Pandas library. The two main data structures in this library – the series and dataframe and associated methods are explained with clarifying examples on how to select, filter, aggregate and merge data. Key tasks like handling null values, applying functions, plotting are also highlighted. Each section has exercises for participants to attempt to consolidate their learning.
Participants should attain a good understanding of basic data types in Python and associated methods and constructs that can be applied to them.
They should also become knowledgeable in how to deal with tabular data through using the specialised data structures found in the Pandas library and how to perform key analysis through methods available in this library.
- Describe basic data types in Python;
- Apply methods to basic data types;
- Enact selection and iteration over basic data types;
- Construct functions to compose modular code;
- Describe specialised data structures – the series and dataframe in the Pandas library;
- Select, filter, aggregate, merge data in the Pandas dataframe;
- Execute specific operations to handle null values and apply functions;
- Plot data in a dataframe column(s).
E learning – Available
Self learning – Available
Face to face – Not available
No previous experience in coding is required, though a basic digital literacy assumed.
This course is under review and a new version will be available soon.
To discuss booking this course for remote delivery, please contact the Data Science Campus Faculty.