Year 1 Training

Training schedules, programmes and slides for Year 1s

Induction Session (during Welcome Week)

Induction Presentation (11.31 MB / PPTX)

E4 DTP Training presentation - Massimo Bollasina (2.52 MB / PDF)

PIP presentation - Thomas (761.3 KB / PDF)

PIP presentation Natalia (11.58 MB / PDF)

Frontiers (Semester 1) and Numeracy, Modelling and Data Management (Semester 2)

Frontiers (Semester 1)

This course consists of six 2-hour lectures delivered by leading academics and researchers from across the University and the E4 DTP partner institutions, covering a wide spectrum of Environmental Science.

Designed to give PhD students a comprehensive overview of scientific research within the remit of the Natural Environment Research Council (NERC). Students will attend seminars on NERC-relevant research and explore topics beyond their specific areas of focus. Additionally, they will have the opportunity to engage with experienced scientists from diverse fields, discussing research design and dissemination. These interactions will deepen their understanding of the broader environmental science landscape, encouraging reflection on potential interdisciplinary collaborations and inspiring new ideas and perspectives for their own research.

At the end of each lecture, speakers may also share their career journeys, offering students valuable exposure to diverse career paths and inspiration from a range of professional areas.

Numeracy, Modelling and Data Management (Semester 2):

The course takes 1st year PhD students through a number of skills that will be useful as they proceed through their PhD. They will learn to manage their data through version control, which allows user to track any changes to their data and easily discover to previous versions of data, programs and text. In addition, students will learn the basic elements of the programming language Python, which is popular in many scientific communities. Python's visualisation library, MatPlotlib, will also be introduced. Students will learn about data assimilation through both python and the statistical package R, and will refresh their knowledge of basic statistics using this software package. Students will also learn how to visualise data with R. Students will be introduced to the open source GIS QGIS and will learn the basics of handling spatial data. Finally, students will learn the basics of constructing a numerical model. All tools used in the course are non-commercial and each course session will involve a brief overview lecture followed by group and individual work while staff members are present to answer questions.

Session 1: Introduction to Qualitative Data and analysis

Session 2: Repositories and GiS

Session 3: Python

Session 4: Python and matplotlib (data visualisation)

Session 5: R

Session 6: R

Content of NMDM sessions are available are on Learn