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Graduate School of Life Sciences course timetable

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Sat 17 Nov – Tue 11 Jun 2019

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November 2018

Mon 19
Core Statistics with R Intro (7 of 8) In progress 10:00 - 13:00 Clinical School, Addenbrooke’s E-learning Suite

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 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 (8 of 8) In progress 13:30 - 16:30 Clinical School, Addenbrooke’s E-learning Suite

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 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
The Engaged Researcher: Editing Your Research Video new [Places] 09:30 - 12:30 eLearning 3 - School of Clinical Medicine

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 (3 of 6) In progress 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 23
Core Statistics (4 of 6) In progress 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) In progress 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) In progress 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 [Places] 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 [Places] 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

Mon 28
CSTP: Achieving Clarity in Academic Writing new [Places] 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 [Places] 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 [Full] 09:30 - 16:30 Postdoc Centre, Newman Library @ Biomedical Campus

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.

March 2019

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

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to generalised linear model analysis. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.

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

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to generalised linear model analysis. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.

Mon 25
Core Statistics (3 of 6) [Full] 13:30 - 16:30 8 Mill Lane, Lecture Room 6

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to generalised linear model analysis. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.

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

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to generalised linear model analysis. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.

April 2019

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

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to generalised linear model analysis. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.

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

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to generalised linear model analysis. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.

Tue 9
How to write an academic paper and get it published [Places] 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.

June 2019

Tue 11
How to write an academic paper and get it published [Places] 09:30 - 16:30 The Postdoc Centre, Seminar Room @ Mill Lane

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.