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Graduate School of Life Sciences course timetable

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Thu 14 Sep 2017 – Wed 7 Feb

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September 2017

Mon 25
An Introduction to data analysis in R new (1 of 5) Finished 14:00 - 17:00 University Information Services, Titan Teaching Room 1, New Museums Site

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 explore more advanced data analysis techniques using the packages dplyr and ggplot. Finally we introduce the concept of reproducible research, and how this may be assisted using 'literate programming'—combining documentation with code.

After the course you should feel confident to start exploring your own dataset, using the materials and references provided.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Dr Michael Grayling, MRC Biostatistics Unit

Dr Simon Frost, Department of Veterinary Medicine

Dr Matt Castle, GSLS

Tue 26
An Introduction to data analysis in R new (2 of 5) Finished 09:30 - 12:30 University Information Services, Titan Teaching Room 1, New Museums Site

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 explore more advanced data analysis techniques using the packages dplyr and ggplot. Finally we introduce the concept of reproducible research, and how this may be assisted using 'literate programming'—combining documentation with code.

After the course you should feel confident to start exploring your own dataset, using the materials and references provided.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Dr Michael Grayling, MRC Biostatistics Unit

Dr Simon Frost, Department of Veterinary Medicine

Dr Matt Castle, GSLS

An Introduction to data analysis in R new (3 of 5) Finished 14:00 - 17:00 University Information Services, Titan Teaching Room 1, New Museums Site

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 explore more advanced data analysis techniques using the packages dplyr and ggplot. Finally we introduce the concept of reproducible research, and how this may be assisted using 'literate programming'—combining documentation with code.

After the course you should feel confident to start exploring your own dataset, using the materials and references provided.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Dr Michael Grayling, MRC Biostatistics Unit

Dr Simon Frost, Department of Veterinary Medicine

Dr Matt Castle, GSLS

Fri 29
Working Across Cultures new Finished 09:30 - 13:00 17 Mill Lane, Seminar Room B

Working Across Cultures: A Practical Introduction to Intercultural Communication

Have you considered how culture may be influencing your professional relationships and interactions? Do you know which factors to consider when dealing with other nationalities? Are you using to your advantage what you may have already noticed or experienced? The workplace today is truly international in composition and intercultural competency is an invaluable and positive addition to your skill set.

This half day session is an introduction to the field of intercultural communication with an emphasis on practical application and developing self-awareness. It is a fun, engaging, relevant topic that will enable you to work more confidently and effectively with any nationality. You will become familiar with key variations across national cultures, so that you can recognise if/when and why cross-cultural misunderstandings are occurring, as well how to be more flexible in your approach.

You can expect a mixture of trainer-led content, as well as interactive exercises in pairs and small groups

An Introduction to data analysis in R new (4 of 5) Finished 09:30 - 12:30 University Information Services, Titan Teaching Room 1, New Museums Site

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 explore more advanced data analysis techniques using the packages dplyr and ggplot. Finally we introduce the concept of reproducible research, and how this may be assisted using 'literate programming'—combining documentation with code.

After the course you should feel confident to start exploring your own dataset, using the materials and references provided.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Dr Michael Grayling, MRC Biostatistics Unit

Dr Simon Frost, Department of Veterinary Medicine

Dr Matt Castle, GSLS

An Introduction to data analysis in R new (5 of 5) Finished 14:00 - 17:00 University Information Services, Titan Teaching Room 1, New Museums Site

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 explore more advanced data analysis techniques using the packages dplyr and ggplot. Finally we introduce the concept of reproducible research, and how this may be assisted using 'literate programming'—combining documentation with code.

After the course you should feel confident to start exploring your own dataset, using the materials and references provided.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Dr Michael Grayling, MRC Biostatistics Unit

Dr Simon Frost, Department of Veterinary Medicine

Dr Matt Castle, GSLS

October 2017

Mon 2
Statistics for Biologists in R new (1 of 7) Finished 10:00 - 12:30 University Information Services, Titan Teaching Room 2, New Museums Site

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. Before moving on to explore classical statistical analysis techniques starting with simple hypothesis testing and building up to 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.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Jonathan Patrick, Department of Plant Sciences

Matt Castle, GSLS

Statistics for Biologists in R new (2 of 7) Finished 14:30 - 17:00 University Information Services, Titan Teaching Room 2, New Museums Site

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. Before moving on to explore classical statistical analysis techniques starting with simple hypothesis testing and building up to 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.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Jonathan Patrick, Department of Plant Sciences

Matt Castle, GSLS

Tue 3
Statistics for Biologists in R new (3 of 7) Finished 14:30 - 17:30 University Information Services, Titan Teaching Room 2, New Museums Site

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. Before moving on to explore classical statistical analysis techniques starting with simple hypothesis testing and building up to 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.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Jonathan Patrick, Department of Plant Sciences

Matt Castle, GSLS

Thu 5
Statistics for Biologists in R new (4 of 7) Finished 09:30 - 12:30 University Information Services, Titan Teaching Room 2, New Museums Site

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. Before moving on to explore classical statistical analysis techniques starting with simple hypothesis testing and building up to 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.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Jonathan Patrick, Department of Plant Sciences

Matt Castle, GSLS

Fri 6
Statistics for Biologists in R new (5 of 7) Finished 09:30 - 12:30 University Information Services, Titan Teaching Room 2, New Museums Site

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. Before moving on to explore classical statistical analysis techniques starting with simple hypothesis testing and building up to 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.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Jonathan Patrick, Department of Plant Sciences

Matt Castle, GSLS

Statistics for Biologists in R new (6 of 7) Finished 14:00 - 17:00 University Information Services, Titan Teaching Room 2, New Museums Site

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. Before moving on to explore classical statistical analysis techniques starting with simple hypothesis testing and building up to 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.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Jonathan Patrick, Department of Plant Sciences

Matt Castle, GSLS

Mon 9
Statistics for Biologists in R new (7 of 7) Finished 09:30 - 12:30 University Information Services, Titan Teaching Room 2, New Museums Site

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. Before moving on to explore classical statistical analysis techniques starting with simple hypothesis testing and building up to 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.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Jonathan Patrick, Department of Plant Sciences

Matt Castle, GSLS

November 2017

Mon 6
How to Keep a Lab Notebook new Finished 14:00 - 16:00 Department of Genetics, Room G1

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).

Mon 27
How to write an academic paper and get it published Finished 09:30 - 16:30 PPD, Revans Room

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.

Cancellation and Non-attendance Policy Due to high demand we cannot accept cancellations for this course within 48 hours prior to the event. Any cancellations made after this time will be considered as a non-attendance. Participants who do not attend on the day will be subject to a £50 fee. By booking a place on the course you accept these terms.

December 2017

Thu 7
An Introduction to Scientific Writing (CSTP Lecture) Finished 14:00 - 16:30 New Museums Site, Babbage Lecture Theatre

This 2.5 hour lecture given by Dr Martin Welch covers the mechanisms of scientific writing; established formats for reports, best practice in writing styles and common mistakes people make.

This event is a compulsory CSTP component.

January 2018

Mon 15
Statistics for Biologists in R new (1 of 8) Finished 14:00 - 17:00 eLearning 1 - School of Clinical Medicine

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.

Wed 17
Statistics for Biologists in R new (2 of 8) Finished 14:00 - 17:00 eLearning 1 - School of Clinical Medicine

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.

Mon 22
Statistics for Biologists in R new (3 of 8) Finished 14:00 - 17:00 eLearning 1 - School of Clinical Medicine

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.

Wed 24
Statistics for Biologists in R new (4 of 8) Finished 14:00 - 17:00 eLearning 1 - School of Clinical Medicine

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.

Mon 29
An Introduction to Scientific Writing (CSTP Lecture) Finished 14:00 - 16:30 Cancer Research UK, Cambridge Research Institute

This 2.5 hour lecture given by Dr Martin Welch covers the mechanisms of scientific writing; established formats for reports, best practice in writing styles and common mistakes people make.

This event is a compulsory CSTP component.

Statistics for Biologists in R new (5 of 8) Finished 14:00 - 17:00 eLearning 1 - School of Clinical Medicine

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.

Wed 31
Statistics for Biologists in R new (6 of 8) Finished 14:00 - 17:00 eLearning 1 - School of Clinical Medicine

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.

February 2018

Mon 5
Statistics for Biologists in R new (7 of 8) Finished 14:00 - 17:00 eLearning 1 - School of Clinical Medicine

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.

Wed 7
Statistics for Biologists in R new (8 of 8) Finished 14:00 - 17:00 eLearning 3 - School of Clinical Medicine

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.