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Graduate School of Life Sciences course timetable

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Fri 22 Feb – Thu 4 Jul

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February 2019

Tue 26
The Engaged Researcher: Research Video: Social Media new [Places] 09:30 - 16:30 OPdA at Biomedical Campus - Newman Library

Everyone is watching video on Social Media these days. So it is a great place to share your research. Learn about the best ways to create & upload video for, as well as go live on Facebook, Twitter & Instagram. You just need yourself, a smartphone and your enthusiasm!

March 2019

Mon 18
Core Statistics (1 of 6) [Full] 13:30 - 16:30 8 Mill Lane, Lecture Room 6

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 generalised linear model analysis. 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.

Wed 20
Core Statistics (2 of 6) [Full] 13:30 - 16:30 8 Mill Lane, Lecture Room 6

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 generalised linear model analysis. 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 25
Core Statistics (3 of 6) [Full] 13:30 - 16:30 8 Mill Lane, Lecture Room 6

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 generalised linear model analysis. 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.

Wed 27
Core Statistics (4 of 6) [Full] 13:30 - 16:30 8 Mill Lane, Lecture Room 6

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 generalised linear model analysis. 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.

April 2019

Mon 1
Core Statistics (5 of 6) [Full] 13:30 - 16:30 8 Mill Lane, Lecture Room 6

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 generalised linear model analysis. 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.

Wed 3
Core Statistics (6 of 6) [Full] 13:30 - 16:30 8 Mill Lane, Lecture Room 6

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 generalised linear model analysis. 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 9
How to write an academic paper and get it published [Full] 09:30 - 16:30 17 Mill Lane, Seminar Room B

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.

Tue 30
The Engaged Researcher: Telling Your Research Story new [Full] 09:30 - 12:30 8 Mill Lane, Lecture Room 5

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.

June 2019

Mon 3
Profile-Raising and Networking new [Full] 10:00 - 16:00 OPdA at Biomedical Campus - Newman Library

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.

Tue 11
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.

July 2019

Thu 4
The Engaged Researcher: Gentle Introduction to Impact Evaluation new [Places] 09:30 - 12:30 17 Mill Lane, Seminar Room B

This workshop introduces how to design an effective impact evaluation.

The Engaged Researcher: Questionnaire Design for Impact Evaluation new [Places] 14:00 - 17:00 17 Mill Lane, Seminar Room B

This workshop provides top tips and guidance on developing an impact evaluation survey that is robust. This will include helping participants identify and avoid common pitfalls in impact evaluation questionnaire design, as well as accounting for key issues such as representative sampling. Participants will also have the opportunity to develop their own survey questions with feedback and support during the workshop.