This course will introduce you to some basic data analytics skills in Python, and is aimed at novices with day 1 introducing the basics of Python programming including data types, packages, data cleaning, data exploration and visualisation, as well as some basic data manipulation and statistics. We take a practical approach and aim to get you starting coding early, with short explanations followed by practical exercises to work through. At the end of day 1 we will also help you get started with using Python on your own machine, so it is preferable that you work from your own laptop.
Day 2 will build on the skills learnt in day 1 and introduce spatial data analytics including visualisation, spatial manipulations and some basic spatial analysis. The afternoon of day 2 gives you the opportunity to put your newfound python data analytic skills into practice in a supported environment. Using either your own data or an example dataset from the Consumer Data Research Centre (CDRC). We also aim to help you get started with GitHub in order to effectively save and manage versions of your code.
Francesca Pontin is CDRC Research Data Scientist for Consumer Analytics. She is interested in spatial determinants of health and the use of secondary big data sources, and her work focuses on the use of smartphone health app data in measuring population physical activity and spatial influences on activity levels, as well as retailer consumer data to understand purchasing behaviours for health, nutrition and sustainability. She has previously run introductory courses for the Leeds University Data Science Society, for students keen to broaden their coding and data analytics skills sets.
By the end of this course you will be able to:
- Understand and work with the different data types in Python.
- Run different Python IDEs on your own machine and understand how to update versions and packages, as well as package dependencies.
- Conduct basic data explorations and manipulations to prepare data for analysis
- Produce a wide range of data visualisations, both spatial and non-spatial and understand how to produce an effective visualisation.
- Load, read in, manipulate and analyse spatial datasets
Is this course for me?
This course is for researchers who want to start looking at data analysis in Python. Day 1 of the course will assume that your knowledge of Python is zero, whereas day 2 will develop on knowledge gained in day 1 and apply this to spatial data visualisation and analysis. It is preferable that you use your own laptop.
09.00: registration opens
09:30-10.45: What is Python, the basics? (Exercise 1: Getting started)
10.45-11.00: Coffee break
11.00-12.30: Using and exploring data. (Exercise 2: Reading in Data, data types, summarising data)
12.30-13.15: Lunch break
13.15-14.30: Data Visualisation. (Exercise 3: different methods of data visualisation)
14.30-14.45: Coffee break
14.45-16.00: Getting started with Python on your machine. (Exercise 4)
16.00-17.00: Data manipulation and statistical analysis. (Exercise 5)
09.00: Registration opens
09.30-10.45: Spatial data visualisation (Exercise 6)
10.45-11.00: Coffee Break
11.00-12.30: Spatial data analysis (Exercise 7)
12.30-13.30: Lunch break
13.30-14.45: Putting it into practice: Own data or one of two examples
14.45-15.00: Coffee break
15.00-16.30: Putting it into Practice cont.