Graduate School of Life Sciences course timetable
November 2018
Wed 7 |
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. |
Mon 12 |
Core Statistics with R Intro
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 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. |
MRL 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 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
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 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. |
|
MRL 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 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. |
|
Tue 13 |
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. |
Wed 14 |
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 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
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 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
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 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. |
MRL 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 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
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 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. |
|
MRL 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 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. |
|
Tue 20 |
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. It is strongly recommended that you also attend The Engaged Researcher: Shooting Your Research Video session. |
Wed 21 |
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 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
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 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
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 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
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 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. |
December 2018
Thu 6 |
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. |
Mon 10 |
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. |
January 2019
Tue 22 |
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. |
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 5 |
Come to this Public Engagement Seminar to hear about an inspirational patient and public involvement (PPI) project from one of your colleagues in the University, Dr Anna Spathis. The PPI project won a prize at the 2018 Vice-Chancellor's public engagement with research awards. This is also an opportunity to network with others interested in Public Engagement and to talk to a member of the Public Engagement Team. Why not bring your lunch with you? |
Tue 12 |
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 15 |
This workshop introduces how to design an effective impact evaluation. |
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. |