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Graduate School of Life Sciences course timetable

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Mon 10 Feb – Mon 23 Mar

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February 2020

Mon 10
Problem Solving and Innovation in a Research intensive Environment new Finished 10:00 - 16:00 Postdoc Centre@ Mill Lane, Eastwood Room

This course has been designed to help graduates students and ECRs to develop their understanding of available tools and techniques which can aid with problem solving and innovation in a research-intensive environment.

The Engaged Researcher: Working with Schools new Finished 10:00 - 12:00 17 Mill Lane, Seminar Room G

This short course will provide you with information about the UK school system, the reality of working with a school. It will cover ways in which the University already works with schools and how you can get involved. The course will help you decide whether working with schools is the right PE activity to achieve your intended outcomes. Finally, it will also provide you with a range of ideas of how to engage with schools and how to plan an activity. This course will be delivered with the Widening Participation team

Core Statistics (1 of 6) Finished 10:00 - 13:00 Clinical School, E-learning 1, 2, 3 (Level 2)

This course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R or Python confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

Core Statistics (2 of 6) Finished 14:00 - 17:00 Clinical School, E-learning 1, 2, 3 (Level 2)

This course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R or Python confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

Wed 12
Innovation and Enterprise - a commercial perspective new Finished 09:30 - 16:30 Postdoc Centre@ Mill Lane, Eastwood Room

Provides an understanding of the UK and European landscape for researchers in the context of future careers and collaborations with industry. Also valuable for academics looking for a career move into industry. Provides an insight into what innovation really means and introduces the practical project management tools to implement innovative projects.

Fri 14
The Engaged Researcher: Animate your research new Finished 10:00 - 13:00 Postdoc Centre@ Mill Lane, Eastwood Room

This course will give you an introduction to visual tools to make your research more accessible and engaging. It is all about breaking down barriers and to empower researchers and professional staff to engage well. This is often about finding a visual link for complex content. This session is going to be delivered by Dr ALina Loth, a Public Engagement professional and Illustrator (http://www.engagedart.uk/)

Mon 17
Core Statistics (3 of 6) Finished 10:00 - 13:00 Clinical School, E-learning 1, 2, 3 (Level 2)

This course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R or Python confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

CSTP: Achieving Clarity in Academic Writing new Finished 13:30 - 17:00 Student Services Centre, Exams Hall, Room AG03d

Achieving clarity in writing is not just about what’s written on the page – that is merely the final stage in a long and complex process. It actually starts with the interpretation of the question… From a linguistic perspective writing is actually rather straightforward, but the clarity of the ‘end product’, particularly in academic writing, is very much dependent on the clarity of all the stages that precede it. This session will examine this process and explores strategies to help you improve the clarity of your writing.

Core Statistics (4 of 6) Finished 14:00 - 17:00 Clinical School, E-learning 1, 2, 3 (Level 2)

This course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R or Python confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

Tue 18
How to write an academic paper and get it published (Life Sciences) Finished 09:30 - 16:30 Postdoc Centre@ Mill Lane, Seminar Room

The course takes an evidence-based approach to writing. Participants will learn that publishing is a game and the more they understand the rules of the game the higher their chances of becoming publishing authors. They will learn that writing an academic article and getting it published may help with their careers but it does not make them better researchers, or cleverer than they were before their paper was accepted; it simply means they have played the game well.

Suitable for GSLS postgraduates in any discipline who are keen to learn how to write academic papers and articles efficiently as well as more established researchers who have had papers rejected and are not really sure why.

If you want a better chance of your name on a paper, this is for you!

Trainer

Olivia Timbs is an award-winning editor and journalist with over 30 years' experience gained from working on national newspapers and for a range of specialist health and medical journals.

Thu 20
Critical Thinking and Bioethics new (1 of 2) Finished 09:30 - 13:30 Student Services Centre, Exams Hall, Room AG03d

As scientists, skills of critical thinking are well developed in hypothesis testing, observation and scientific projects. This workshop will incorporate other modes of logic and reason into scientific thinking.

This workshop will consist of a set of debates on current bioethical issues. We will then analyse and evaluate the presence and impact of critical thinking within those debates

PLEASE NOTE: This course consists of two half day sessions, with a week between sessions.

Fri 21
Understanding Open Data new CANCELLED 10:00 - 12:00 Student Services Centre, Exams Hall, Room AG03c

Conclusions without supporting data are just claims. More and more researchers are sharing their data to improve reproducibility, get more citations and spark collaborations, yet the process can be daunting. We will explore the benefits of sharing data, as well as any concerns you might have, and give you practical tips and tools to ensure that you make the most of the opportunity to open up your data for the world.

Mon 24
Core Statistics (5 of 6) Finished 10:00 - 13:00 Clinical School, E-learning 1, 2, 3 (Level 2)

This course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R or Python confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

Core Statistics (6 of 6) Finished 14:00 - 17:00 Clinical School, E-learning 1, 2, 3 (Level 2)

This course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R or Python confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

Tue 25
The Engaged Researcher: Evaluation of Public Engagement new Finished 14:00 - 17:00 Clinical School, Seminar Room 11

Successful engagement with the public can benefit research, researchers and the public – but how do you go about demonstrating this change? Evaluation of engagement doesn’t just help us demonstrate the value of our PE initiatives but can help bring us closer to our audiences by giving the public a strong clear voice. This workshop will guide you through the best evaluation processes showing you When, Why and crucially How to use evaluation to give you reliable and clear data. Demonstrate success to funders; record Impact for REF; learn how to improve your processes and have a better understanding of the people you are connecting with. This course is going to be run by Jamie Galagher: Jamie is an award-winning freelance science communicator and engagement professional. He has delivered training around the world, from skyscrapers of Hong Kong to tents in the African bush. Having had four years’ experience as the central PE lead for the University of Glasgow he has worked on improving the reach, profile and impact of research engagement in almost every academic discipline. Specialising in evaluation Jamie provides consultancy services to charities and universities helping them to demonstrate their impact and understand their audiences and stakeholders. Jamie is also an associate editor of the Research for All journal. He was named as one of the “100 leading practising scientists in the UK” by the Science Council and as one of the “175 Faces of Chemistry” by the Royal Society of Chemistry. He won the International 3 Minute Thesis Competition and Famelab Scotland. www.jamiebgall.co.uk @jamiebgall

Thu 27
Critical Thinking and Bioethics new (2 of 2) Finished 09:30 - 13:30 Student Services Centre, Exams Hall, Room AG03c

As scientists, skills of critical thinking are well developed in hypothesis testing, observation and scientific projects. This workshop will incorporate other modes of logic and reason into scientific thinking.

This workshop will consist of a set of debates on current bioethical issues. We will then analyse and evaluate the presence and impact of critical thinking within those debates

PLEASE NOTE: This course consists of two half day sessions, with a week between sessions.

The Engaged Researcher: Introduction to Social Media Engagement new CANCELLED 10:00 - 13:00 Clinical School, Seminar Room 10

This course will cover how to use Social Media tools for Public Engagement. The course will be delivered by the Social Media and AV team.

March 2020

Fri 6
The Engaged Researcher: Engaging with Policy new Finished 10:00 - 12:00 Postdoc Centre@ Mill Lane, Seminar Room

This session will be an introduction to Public Engagement and Policy. The session will start with a short introduction to Public Engagement and Stakeholder involvement. It will then focus on how researchers can get started in engaging with policymakers and explore synergies between governmental structures and higher education institutions in the UK. The session will be co-led by Dr. Maja Spanu, a Junior Research Fellow in International Relations.

Mon 9
Profile-Raising and Networking new Finished 10:00 - 16:00 Postdoc Centre@ Mill Lane, Seminar Room

This whole day session is designed to help researchers develop strategies for making networking part of a successful career, whether inside or outside of research. It focuses on thinking about all of the researchers' working life as a route to networking, rather than being a course about "personal impact" in conference coffee breaks.

Core Statistics (1 of 6) Finished 14:00 - 17:00 8 Mill Lane, Lecture Room 5

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R or Python confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

Wed 11
Core Statistics (2 of 6) Finished 14:00 - 17:00 8 Mill Lane, Lecture Room 5

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R or Python confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

Mon 16
Core Statistics (3 of 6) Finished 10:00 - 13:00 8 Mill Lane, Lecture Room 7

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R or Python confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

Wed 18
Core Statistics (4 of 6) Finished 14:00 - 17:00 8 Mill Lane, Lecture Room 5

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R or Python confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

Fri 20
Finishing Your PhD and Looking Forward (Life Sciences) new CANCELLED 09:30 - 16:45 Postdoc Centre@ Mill Lane, Eastwood Room

This course will take a complete look at the final year of your PhD. From the core elements of the thesis and viva and the often forgotten administrative tasks that must get done, on to looking at who you have become and what career path you may take.

Mon 23
Core Statistics (5 of 6) Finished 14:00 - 17:00 8 Mill Lane, Lecture Room 5

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R or Python confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.