**This course is now fully-booked**
This two-day course provides an introduction to Python programming with a focus on data analytics. At the start of the course we will help participants to set up Python on their laptops, we therefore strongly encourage participants to bring their own devices with them. The course will introduce some basics in Python programming such as data types, basic operations, data sequences and data structures, control flows and exceptions. We will then focus on working with actual data, such as survey data, time-series data, JSON data (e.g. Twitter data). This will include data processing, data visualization, statistical analysis of numerical data and a brief introduction into machine learning. The course will also offer an introduction into geo-spatial analyses and visualization of geo-spatial data. Short lectures will be interspersed with hands-on practical exercises with plenty of opportunity to work with real data of various types. During the last session participants will have the possibility to work on their own data in class, having the opportunity to get help from the tutor and assistants.
The participants of this course will leave with a practical understanding of Python and its applications in data analytics, including being able to continue exploring Python in self-study.
Dr Viktoria Spaiser has a background in Sociology (PhD , Bielefeld University, Germany, 2012), Political Science (MA in Conflict, Security and Development, King’s College London, UK, 2008) and Computer Science (German Diploma, University of Applied Sciences Trier, Germany, 2013). She was a visiting researcher in the Computational Social Science Research Group at ETH Zurich in 2012 and a postdoctoral researcher at the Institute for Futures Studies Stockholm (2012-2014) and at the Department of Mathematics, Uppsala University in Sweden (2014-2015).
Since August 2015 she has been the University Academic Fellow in Political Science Informatics at the University of Leeds, POLIS and is also affiliated with the Leeds Institute for Data Analytics (LIDA). Her research interests include applying mathematical and computational approaches (such as Dynamical Systems Modelling, Bayesian Statistics, Agent Based Modelling and Data Science Approaches) to social and political science research questions. Most recently, she has been interested in public goods dilemmas and in combining data science and experimental methods.
Day 1, 14th Nov
09.00 – 10.00: Setting Up Python on Your Computer
10.00 – 10.15: Short break
10.15 – 12.00: Basics
12.00 – 13.00: Lunch
13.00 – 14.45: Working with Data
14.45 – 15.00: Short break
15.00 – 16.15: Data Visualization
16.15 – 17.00: Descriptive Statistics
Day 2, 15th Nov
09.00 – 11.00: Multivariate Statistics & Introduction to Machine Learning
11.00 – 11.15: Short break
11.15 – 12.00: Introduction to Spatial Analyses
12.00 – 13.00: Lunch break
13.00 – 14.30: Continuing Spatial Analyses
14.30 – 14.45: Short break
14.45 – 16.00: Putting it all together, Questions & Bring Your Data
Who is this course suitable for?
No prior programming knowledge is assumed for this course, but, it is assumed that the participants will have had some statistical training (e.g. understanding linear and logistic regression) prior to the course, as the methods demonstrated in the training will not be explained in statistical terms.
£100 – Students
£200 – Academic, public and charitable sector employees
£400 – Other
Catering will be provided. If you have any queries about this course, please contact Kylie Norman.