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Graduate School of Life Sciences course timetable

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Fri 23 Nov 2018 – Wed 5 Jun

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November 2018

Fri 23
Core Statistics (4 of 6) Finished 13:30 - 16:30 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 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 (5 of 6) Finished 13:30 - 16:30 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 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 (6 of 6) Finished 13:30 - 16:30 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 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
The Engaged Researcher: Introduction to Public Engagement new Finished 10:00 - 13:00 Computer Lab, SW01

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
The Engaged Researcher: Comedy in communicating your research new Finished 09:30 - 12:30 17 Mill Lane, Seminar Room E

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
The Engaged Researcher: Finding Your Research Story new Finished 09:30 - 12:30 Clinical School, Seminar Room 10

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
CSTP: Achieving Clarity in Academic Writing new Finished 13:30 - 17:30 Clinical School, William Harvey Lecture Theatre

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
The Engaged Researcher: Public Engagement Seminar new Finished 12:30 - 14:00 Clinical School, Seminar Room 3

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
How to write an academic paper and get it published Finished 09:30 - 16:30 Postdoc Centre @ Biomedical Campus, Newman Library

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
The Engaged Researcher: Gentle Introduction to Impact Evaluation new Finished 09:30 - 12:30 Postdoc Centre@ Mill Lane, Eastwood Room

This workshop introduces how to design an effective impact evaluation.

The Engaged Researcher: Questionnaire Design for Impact Evaluation new Finished 14:00 - 17:00 Postdoc Centre@ Mill Lane, Eastwood Room

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.

Tue 26
The Engaged Researcher: Research Video: Social Media new Finished 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) Finished 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) Finished 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) Finished 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) Finished 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

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 (5 of 6) Finished 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) Finished 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 Finished 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 Finished 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.

May 2019

Wed 22
The Engaged Researcher: Public Engagement with Dr. Giles Yeo new Finished 14:00 - 16:00 Postdoc Centre@ Mill Lane, Seminar Room

This 2 hour workshop will discuss "why does research matter and how do we share it?"

June 2019

Mon 3
Profile-Raising and Networking new Finished 10:00 - 16:00 Postdoc Centre@ Mill Lane, Seminar Room

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.

Wed 5
Core Statistics (1 of 6) Finished 10:00 - 13:00 Bioinformatics Training Room, Craik-Marshall Building, Downing Site

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 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 (2 of 6) Finished 14:00 - 17:00 Bioinformatics Training Room, Craik-Marshall Building, Downing Site

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 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.