Introduction to Python for Data Analytics
- Thursday 1 - Friday 2 November, 2018, 9am - 4pm
- Leeds Institute for Data Analytics, Level 11, Worsley Building, University of Leeds, Clarendon Way, Leeds, LS2 9NL
- External URL
- CDRC Training
This two-day course provides an introduction to Python programming with a focus on data analytics. The course will introduce some basics in Python programming such as data types, basic operations, data sequences and data structures, control flows, exceptions and object-oriented programming. We will then focus on working with actual data, such as survey data, time-series data, JSON data (e.g. Twitter data), and geo-spatial data. This will include visualization and statistical analysis of numerical data. The course will also give an introduction in spatial analyses and visualization of geo-spatial data and an introduction into natural language processing and text mining of large textual data. Short lectures will be interspersed with hands-on practical exercises with plenty of opportunity to work with real data of various types.
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.
Who is this course suitable for?
No prior knowledge is assumed for this course other than having basic IT skills and basic statistical understanding.
£200 (academic, public and charitable sector employees)
£400 (private sector)