Graduate School of Life Sciences course timetable
October 2018
Mon 29 
Core Statistics with R Intro
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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 introduce the R language, and cover basic data manipulation and plotting. We then move on to 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. 
Core Statistics with R Intro
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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 introduce the R language, and cover basic data manipulation and plotting. We then move on to 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. 
November 2018
Mon 5 
Core Statistics with R Intro
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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 introduce the R language, and cover basic data manipulation and plotting. We then move on to 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. 
Core Statistics with R Intro
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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 introduce the R language, and cover basic data manipulation and plotting. We then move on to 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. 

How to Keep a Lab Notebook
[Places]
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). 

Tue 6 
The course takes an evidencebased 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 awardwinning editor and journalist with over 30 years' experience gained from working on national newspapers and for a range of specialist health and medical journals. 
Mon 12 
Core Statistics with R Intro
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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 introduce the R language, and cover basic data manipulation and plotting. We then move on to 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. 
Core Statistics with R Intro
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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 introduce the R language, and cover basic data manipulation and plotting. We then move on to 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 14 
Core Statistics
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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. 
Fri 16 
Core Statistics
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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 19 
Core Statistics with R Intro
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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 introduce the R language, and cover basic data manipulation and plotting. We then move on to 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. 
Core Statistics with R Intro
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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 introduce the R language, and cover basic data manipulation and plotting. We then move on to 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 21 
Core Statistics
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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. 
Fri 23 
Core Statistics
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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 28 
Core Statistics
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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. 
Fri 30 
Core Statistics
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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. 
January 2019
Mon 28 
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. 
February 2019
Tue 12 
The course takes an evidencebased 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 awardwinning editor and journalist with over 30 years' experience gained from working on national newspapers and for a range of specialist health and medical journals. 
March 2019
Mon 18 
Core Statistics
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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
[Places]
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 reallife issues in the biological sciences. There are three core goals for this course:
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 
The course takes an evidencebased 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 awardwinning editor and journalist with over 30 years' experience gained from working on national newspapers and for a range of specialist health and medical journals. 