Administrative Information
| Title | Exploratory Data Analysis |
| Duration | 60 min |
| Module | A |
| Lesson Type | Tutorial |
| Focus | Technical - Foundations of AI |
| Topic | Exploratory Data Analysis |
Keywords
Data exploration,Python,Pandas,visualisations.,
Learning Goals
- Learner can perform exploratory data analysis using Python.
- Learner can use visualisations to investigate a variable's distribution using Python.
- Learner can check for dependencies between variables by using visualisation in Python.
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- Read the following tutorials:
- Calculate statistics with Pandas
- Time series
- Text data
- Read chapter 3 and 4 of Python Data Science Handbook
Optional for Students
None.
References and background for students
None.
Recommended for Teachers
Lesson materials
Instructions for Teachers
This is a hands-on session following the lecture Lecture: Exploratory Data Analysis. Students will work on a jupyter notebook. First they can work on pre-defined exercises and then they can choose a dataset to explore on their own. You can also use choose to instruct the student to do the exercises before this session, so they can focus on the exploration of the dataset of their choice in this session.
Acknowledgements
Rianne van Os
The Human-Centered AI Masters programme was Co-Financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.