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Graduate School of Life Sciences course timetable

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September 2019

Tue 24
Managing Your Final Year and Preparing to Move On new (2 of 2) In progress 09:30 - 17:00 Postdoc Centre@ Mill Lane, Eastwood Room

Your final year is an exciting, yet unsettling time. You need to finish experiments, start to write your thesis and begin to think about the next chapter of your career. This two-day linked workshop is designed to help you make sense of the year ahead.

You will be given practical tips on planning your final year, as well as discuss the administration of your final year, writing your thesis and preparation for your viva. In addition, you will explore the career opportunities that are best suited to you, by thinking about your expertise, suitability and personal values. Finally, you will get the chance to review your C.V and experience the interview process.

October 2019

Mon 14
The Engaged Researcher: Introduction to Social Media Engagement new [Places] 10:00 - 13:00 Postdoc Centre@ Mill Lane, Eastwood Room

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.

Tue 15
Core Statistics (1 of 6) [Places] 10:00 - 13:00 8 Mill Lane, Lecture Room 10

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.

Thu 17
Core Statistics (2 of 6) [Places] 10:00 - 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 software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.

Tue 22
Core Statistics (3 of 6) [Places] 10:00 - 13:00 8 Mill Lane, Lecture Room 10

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.

Thu 24
Core Statistics (4 of 6) [Places] 10:00 - 13:00 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 software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.

Mon 28
The Engaged Researcher: Finding Your Research Story new [Places] 09:30 - 13:30 Postdoc Centre@ Mill Lane, Eastwood Room

We all love a good story- whether it’s the latest bestselling fiction book or a cheesy soap. And science is full of stories- stories of discovery, of persistence, of hope. Finding these stories can help take your public engagement to the next level, whatever medium you use to communicate.

Tue 29
Core Statistics (5 of 6) [Places] 10:00 - 13:00 8 Mill Lane, Lecture Room 10

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.

Thu 31
The Engaged Researcher: An introduction to planning and evaluating impactful public engagement new (1 of 2) [Places] 10:00 - 13:00 17 Mill Lane, Seminar Room G

This short course covers the what, why and how of public engagement and communication. The course is for research staff and PhD students who want to gain the skills and confidence required to plan and deliver an impactful public engagement project.

Core Statistics (6 of 6) [Places] 10:00 - 13:00 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 software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.

November 2019

Fri 1
How to Keep a Lab Notebook [Places] 14:00 - 16:00 Department of Genetics, Biffen Lecture, Downing Site

Your lab notebook is one of the most important and precious objects you, as a scientist, will ever have. This course will explore how keeping an exemplary laboratory notebook is crucial to good scientific practice in lab research. The course will consist of a short talk, a chance to assess some examples of good and bad practice, with plenty of time for questions and discussion. You might like to bring along your own lab notebook for feedback. (Please note that issues relating to protection of Intellectual Property Rights will not be covered in this course).

Wed 6
The Engaged Researcher: Telling Your Research Story new [Places] 09:30 - 13:00 Postdoc Centre@ Mill Lane, Seminar Room

Whether at a conference, a science festival or in the pub, all scientists need to be able to talk about their work in an engaging and understandable way. This practical, hands-on session will help scientists develop their communication skills, so they are confident talking to diverse audiences in a range of environments.

Fri 8
The Engaged Researcher: Media training new [Places] 10:00 - 13:00 Postdoc Centre@ Mill Lane, Eastwood Room

This course gives an introduction into how to engage with the public through media. It will cover the differing types of media, what makes research newsworthy, how to work with the communications office to gain media coverage, what to expect from an interview (print, pre-recorded, live) and how to communicate well in interviews. It will be delivered jointly with the University Communications team

Mon 18
The Engaged Researcher: Shooting your research video new [Places] 09:30 - 16:30 Postdoc Centre@ Mill Lane, Eastwood Room

Why is YouTube popular? Because people love watching videos. A video is a great way to spread the message of your research to different public audiences across the World! Attendees will be equipped with the skills needed to plan and shoot high quality footage for your very own research-video.

It is strongly recommended that you also attend The Engaged Researcher: Editing Your Research Video session.

Tue 19
How to write an academic paper and get it published [Full] 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.

Fri 22
The Engaged Researcher: Editing your Research Video new [Places] 09:30 - 13:00 Postdoc Centre@ Mill Lane, Eastwood Room

Shot your research video? Got lots of video clips, photographs & audio you want to bring together to make one research video to share with public audiences around the World? Attendees on this course will learn how to cut, add soundtracks and do audio-mixing to edit their very own research video.

Wed 27
The Engaged Researcher: Working with Museums new [Places] 10:00 - 13:00 Postdoc Centre@ Mill Lane, Seminar Room

This short course covers how you can get started to develop Public Engagement with Museums, how to go about it and what you can gain from it. This course will be delivered with staff of UCM.

Fri 29
The Engaged Researcher: An introduction to planning and evaluating impactful public engagement new (2 of 2) [Places] 10:00 - 13:00 Postdoc Centre @ Eddington, Multi-function Room

This short course covers the what, why and how of public engagement and communication. The course is for research staff and PhD students who want to gain the skills and confidence required to plan and deliver an impactful public engagement project.

Core Statistics (1 of 6) [Places] 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 software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.

Core Statistics (2 of 6) [Places] 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 software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.

December 2019

Tue 3
The Engaged Researcher: Evaluation of Public Engagement new [Places] 10:00 - 13:00 Postdoc Centre@ Mill Lane, Eastwood Room

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

Fri 6
Core Statistics (3 of 6) [Places] 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 software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.

Core Statistics (4 of 6) [Places] 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 software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.

Mon 9
The Engaged Researcher: Comedy in communicating your research new [Places] 14:00 - 17:00 Postdoc Centre@ Mill Lane, Eastwood Room

Ever wanted to bring comedy into your public engagement projects? This is for you, as trainer Steve Cross helps researchers to improve their communication skills, build confidence and find creative ways of communicating their research.

Thu 12
Core Statistics (5 of 6) [Places] 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 software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.

Core Statistics (6 of 6) [Places] 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 software environment. 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 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

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

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 and moreover know when, and when not, to apply these techniques.