Graduate School of Life Sciences course timetable
Thursday 31 October 2019
10:00 |
Core Statistics
Finished
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:
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. |
Friday 1 November 2019
14:00 |
How to Keep a Lab Notebook
Finished
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). |
Tuesday 19 November 2019
09:30 |
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. |
Friday 29 November 2019
10:00 |
Core Statistics
Finished
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:
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. |
14:00 |
Core Statistics
Finished
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:
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. |
Friday 6 December 2019
10:00 |
Core Statistics
Finished
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:
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. |
14:00 |
Core Statistics
Finished
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:
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. |
Thursday 12 December 2019
10:00 |
Core Statistics
Finished
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:
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. |
14:00 |
Core Statistics
Finished
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:
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. |
Monday 10 February 2020
10:00 |
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. |
Core Statistics
Finished
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:
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. |
|
14:00 |
Core Statistics
Finished
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:
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. |
Wednesday 12 February 2020
09:30 |
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. |
Monday 17 February 2020
10:00 |
Core Statistics
Finished
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:
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. |
13:30 |
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. |
14:00 |
Core Statistics
Finished
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:
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. |
Tuesday 18 February 2020
09:30 |
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. |
Thursday 20 February 2020
09:30 |
Critical Thinking and Bioethics
Finished
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. |
Friday 21 February 2020
10:00 |
Understanding Open Data
CANCELLED
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. |
Monday 24 February 2020
10:00 |
Core Statistics
Finished
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:
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. |
14:00 |
Core Statistics
Finished
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:
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. |
Thursday 27 February 2020
09:30 |
Critical Thinking and Bioethics
Finished
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. |
Monday 9 March 2020
10:00 |
Profile-Raising and Networking
Finished
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. |
14:00 |
Core Statistics
Finished
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:
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. |
Wednesday 11 March 2020
14:00 |
Core Statistics
Finished
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:
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. |